Emerging Equity Market Volatility∗

Geert Bekaerta, Campbell R. Harveyb

aStanford University, Stanford, CA 94305, USA, National Bureau of EconomicResearch, Cambridge, MA 02138, USA

bDuke University, Durham, NC 27708, USA, National Bureau of Economic Research,Cambridge, MA 02138

Abstract

Understanding volatility in emerging capital markets is important for determining the costof capital and for evaluating direct investment and asset allocation decisions. We provide anapproach that allows the relative importance of world and local information to change throughtime in both the expected returns and conditional variance processes. Our time-series andcross-sectional models analyze the reasons that volatility is different across emerging markets,particularly with respect to the timing of capital market reforms. We find that capital marketliberalizations often increase the correlation between local market returns and the world marketbut do not drive up local market volatility.

Key words: Emerging markets, volatility, capital market reforms

JEL classification: G15, G11, G12

Correspondence to: Professor Campbell R. Harvey, The Fuqua School of Business, Duke Uni-versity, Box 90120, Durham, NC 27708-0120, USA, E-mail: charvey@mail.duke.edu.

∗We have benefited from the comments of Tim Bollerslev, Silverio Foresi, Rene Garcia, WillGoetzmann, Stephen Gray, Philippe Jorion, Jianping Mei, Steve Ross, and seminar partici-pants at New York University, Northwestern University, Yale University, Georgetown Univer-sity, Tilburg University, Southern Methodist University, Stanford University, the InternationalMonetary Fund, the CIRANO-C.D.R.E. Conference on Stochastic Volatility in Montreal, the1995 American Finance Association Meetings in Washington, and the 1995 European FinanceAssociation Meetings in Milan. We especially appreciate the detailed comments of the ref-eree, Richard Roll, which greatly improved the paper. Harvey’s research was supported bythe Batterymarch Fellowship. Bekaert acknowledges the financial support of an NSF grantand the Financial Research Initiative of the Graduate School of Business at Stanford. Wethank Arman Glodjo, Akhtar Siddique, Guojun Wu, and Nese Yildiz for research assistance.We especially appreciate the research assistance of Magnus Dahlquist and Angela Ng. E-mail:bekaert geert@gsb.stanford.edu and charvey@mail.duke.edu.

1. Introduction

It is now well known that emerging market equities have vastly different char-

acteristics than equities from developed capital markets. There are at least four

distinguishing features of emerging market returns: average returns are higher,

correlations with developed market returns are low, returns are more predictable

and volatility is higher. Our research focusses on this last feature.

We provide a detailed analysis of equity market volatility in emerging capi-

tal markets. We attempt to answer the question of why volatility is so different

across emerging equity markets. This is an important question. In segmented

capital markets, risk premiums may be directly related to the volatility of equity

returns in the particular market. Higher volatility implies higher capital costs.

Higher volatility may also increase the value of the “option to wait,” hence delay-

ing investments. Our research helps understand the forces that shape both the

time-series variation and cross-sectional dispersion of volatility in twenty emerging

equity markets.

We begin by analyzing the time-series of volatility. Given the evidence of

nonnormalities in emerging market returns presented in Harvey (1995a), it is

unlikely that the standard implementation of the Engle (1982) and Bollerslev

(1986) autoregressive conditional heteroskedasticity (ARCH) models is fruitful.

We address this problem by estimating models using the student t distribution

which allows for fatter tails in the return distributions. We also fit models using

the semi-parametric ARCH (SPARCH) model developed in Engle and Gonzalez-

Rivera (1991) and Gray (1994). The SPARCH model is explicitly designed to

account for leptokurtosis and skewness. This model is particularly appropriate

for emerging market returns which exhibit both types of nonnormalities.

We are careful to link previous research on the conditional means of the

emerging market returns to the conditional variance specifications. Since Har-

vey (1995a), Bekaert (1995) and Bekaert and Harvey (1995) show that emerging

market equity returns are predictable, our variance specifications allow for time-

1

varying conditional means.

Much of the focus of our paper is on the common world factors that drive the

time-series of volatility. This part of our research is related to the work of Schwert

(1989a,b) who attempts to explain the time-variation in U.S. market volatility

with macroeconomic and microstructural factors. In our analysis, we measure

the part of national volatility that is determined by world factors and the part

determined by local market effects. We also investigate whether this proportion

changes through time. We argue that the increasing influence of world factors

on volatility in some countries is consistent with increased market integration.

This analysis is related to the time-varying market integration parameter that is

proposed in Bekaert and Harvey (1995).

In addition to tracing the influence of world factors on market volatility, we

investigate whether capital market liberalization policies affect volatility. The

evidence in Kim and Singal (1994), based on average volatilities, suggests that

volatility may increase. Our methodology allows us to reexamine this issue in the

context of time-varying means and variances. As is clear from Bekaert, Garcia

and Harvey (1995a), insight on this issue is of great importance for policy makers

in developing markets whom may be weighing the costs and benefits of various

liberalization initiatives.

To control for other factors affecting volatility, we extend our analysis to the

cross-section of volatility. This allows us to explore the question: Why is volatility

different in different markets? We investigate whether the volatility is related to

the number of stocks in the national index, asset concentration and the cross-

sectional volatility of the individual stocks within each country’s index. We also

examine whether volatility is related to variables capturing financial and economic

integration such as market capitalization to GDP, exports plus imports to GDP

and country credit risk ratings. Finally, following Schwert (1989a,b), we examine

variables capturing macroeconomic volatility.

The paper is organized as follows. The second section presents the distri-

butional characteristics of the emerging market data. The results of estimating

time-series volatility models are presented in the third section. This section also

2

includes an analysis of asymmetric volatility effects. The fourth section decom-

poses volatility into the parts determined by world and local factors. In the fifth

section, we present an analysis of the cross-sectional patterns in volatility and

detail how capital market reforms affect volatility. Some concluding remarks are

offered in the final section.

2. Data and summary statistics

2.1 Sources and preliminary analysis

Data are available for 20 emerging markets from the International Finance Corpo-

ration (IFC) of the World Bank. Summary statistics are presented in Table 1 for

the period 1976.01 to 1992.12. U.S. dollar returns are displayed. The statistics in-

clude the average (annualized) arithmetic return, annualized standard deviation

and the first order autocorrelation. Each country’s return is based on a value-

weighted portfolio of securities that trade in that market. The number of stocks

included in the country indices ranges from 16 to 77. Stocks are selected for in-

clusion based on liquidity (how often they trade and the volume of trading) and

size (market value). The industrial composition of the index is also important.

That is, if there are two securities with approximately the same size and liquidity,

the security which makes the index better reflect the industrial composition of the

local market may be chosen. All of the indices reflect total returns: dividends and

capital gains. The IFC indices are detailed and compared to the better known

Morgan Stanley Capital International (MSCI) indices for developed markets in

Bekaert and Harvey (1995) and Harvey (1995a)

The mean U.S. dollar returns for the emerging markets range from 68% (Ar-

gentina) to -12% (Indonesia whose sample only begins in January 1990). This

sharply contrasts with the range of average returns in the developed markets. For

example, Bekaert (1995) and Harvey (1993) show that no developed country has

an average arithmetic return that exceeds 25% over the same time period. In

the IFC emerging sample, 8 countries (Argentina, Chile, Colombia, Philippines,

Portugal, Taiwan, Turkey and Venezuela) have returns that average above 25%.

3

As mentioned earlier, the emerging market returns are characterized by high

volatility. Volatility ranges from 18% (Jordan) to 104% (Argentina). In contrast,

the MSCI countries have a range of volatility between 15% and 33%. There are

12 emerging countries with volatility higher than 33% (Argentina, Brazil, Chile,

Greece, Mexico, Nigeria, Philippines, Portugal, Taiwan, Turkey, Venezuela, and

Zimbabwe). Three additional countries have volatility greater than 30% (Colom-

bia, Indonesia, and Korea). Both the range and the magnitude of the volatilities

is much greater than found in developed markets. Using the same sample, Harvey

(1993) finds that volatility in developed markets ranges from 15% (U.S.) to 33%

(Hong Kong) with an equally-weighted average volatility of 23%.

The autocorrelation measures the persistence (or predictability) of the mar-

ket returns based on past market returns. This persistence could be driven by

market imperfections, such as infrequent trading of the component securities, or

by some fundamental forces, such as predictable changes in sensitivities to world

risk factors. In the MSCI sample, there are only five countries with first-order au-

tocorrelation that exceeds 10%. In the emerging countries, there are 12 countries

with autocorrelations greater than 10%. Indeed, there are eight countries with

autocorrelations above 20% (Colombia, Indonesia, Mexico, Pakistan, Philippines,

Portugal, Turkey, and Venezuela). Although the sample is shorter for some of

these countries and the standard errors of the autocorrelations are higher, the ev-

idence suggests that the returns in many of the emerging markets are predictable

based on past information.

In focussing on these emerging equity markets, a natural concern arises re-

garding potential survivorship biases. Harvey (1995a) shows that the pre-1981

data is ‘backtracked’ by the IFC. That is, firms where chosen for inclusion in nine

countries in 1981, and the series were filled back to 1976. However, his analysis

shows little difference between the 1976-80 and the later data. More fundamen-

tally, one could argue that the sample countries are the countries that ‘emerged.’

That is, the countries that did not perform well economically over the sample, were

not likely to make it into the IFC emerging markets index. While this is a valid

argument, it is also true for any stock index (like the S&P 500). In contrast with

4

standard market indices, the sample of countries that we look at includes coun-

tries that have not done particularly well in economic performance (like Nigeria

and Zimbabwe).

2.2 Distributional characteristics

Evidence that many of the emerging market returns depart from normality is

also presented in table 1. If the data are normally distributed, then the coefficients

of skewness and excess kurtosis should be equal to zero. Richardson and Smith

(1993) and Harvey (1995a) test normality of equity returns based on Hansen’s

(1982) generalized method of moments (GMM). The following system of equations

is estimated for each asset i:

e1it =rit − µi

e2it =(rit − µi)2 −Vi

e3it =[(rit − µi)3]/V3/2i − sk

e4it =[(rit − µi)4]/V2i − 3 − xku

(1)

where µ is the mean, V is the variance, sk is the skewness, xku is the excess

kurtosis, et = {e1it e2it e3it e4it}′ represents the disturbances andE[et] = 0. There

are four orthogonality conditions and four parameters which implies the model is

exactly identified. The null hypothesis that the coefficients of skewness and excess

kurtosis are zero is tested with a Wald test.1

The null hypothesis of unconditional normality can be rejected at the 5% level

in 15 of the 20 emerging markets when measured in U.S. dollars. These results are

consistent with those reported in Harvey (1995a) and Claessens, Dasgupta and

Glen (1994). In contrast, Harvey (1995a) fails to reject unconditional normality

in the three largest developed markets, the U.S., Japan and the U.K.

1 Richardson and Smith (1993) present this general framework. However, ourweighting matrix is based on the spectral density at frequency zero with an optimalbandwidth which follows Andrews (1991). An alternative approach, presented inHarvey (1995a), is to set sk and xku equal to zero and estimate an overidentifiedsystem. This results in a χ2 test with two degrees of freedom.

5

3. Characterizing variance in emerging markets

3.1 Conditional variance estimation with nonnormal returns

We examine the following GARCH(1,1) model of volatility:

ri,t =X′i,t−1δi + εi,t

εi,t =σi,tzi,t

σ2i,t =wi + αiσ

2i,t−1 + βiε

2i,t−1

(2)

where ri,t represents the arithmetic return2 on the national equity index of country

i in U.S. dollars, Xi,t−1 represents a set of local information variables that affect

the conditional mean, σ2i,t is the conditional variance and zi,t are the standardized

residuals formed by dividing the residuals, εi,t by the volatility, σi,t.

This model of volatility is univariate in the sense that other countries’ volatil-

ities do not influence the predictions and there is no attempt to model the covari-

ance dynamics. We think of this model as the “fully segmented” model. Consistent

with this view, we allow local information variables, Xi,t−1, rather than global in-

formation variables, to affect the conditional mean returns. In particular, local

information variables include: a constant, the lagged equity return, the lagged

exchange rate change, the lagged dividend yield and lagged market capitalization

to GDP. In section four, we introduce a world factor model which allows for global

influences on both the mean and volatility.

There are three different distributional assumptions in the general model:

Model I : zi,t|It−1 ∼ N(0, 1)

Model II : zi,t|It−1 ∼ tν(0, 1)

Model III : zi,t|It−1 ∼{N(µi1, σi1), w.p. pi;N(µi2, σi2), w.p. (1 − pi).

(3)

where It−1 is the information set available to investors at t − 1. The first model

is the standard normal formulation. The second model introduces a t distribution2 In order to provide a direct link with the asset pricing models presented in

section 4, we use arithmetic rather than geometric returns. We have estimatedthe model for several countries using log returns yielding similar results to theones obtained using arithmetic returns.

6

with ν degrees of freedom. This is a one-parameter extension of model I. While

able to accommodate fat tails, the assumed distribution is symmetric in model II.

The third model is designed to capture both fat tails and skewness (which

quite a few of the emerging markets exhibit). Model III is a parsimonious ver-

sion of semi-parametric ARCH [see Engle and Gonzalez-Rivera (1991) and Gray

(1995a,b)]. Since in ARCH models, the conditional mean of the standardized

residuals is equal to zero and the conditional variance is equal to one, additional

constraints need to be imposed:

µ2 =−pµ1

1− p

σ2 =

√1 − pσ2

1 − (pµ1 + (1 − p)µ22)

1 − p

(4)

Hence, this model is a three parameter extension of the standard model.

We are also interested in asymmetric variance effects. Black (1976), Christie

(1982), Nelson (1991) and Glosten, Jagannathan and Runkle (1993) pursue the

intuition that variance is higher when market returns are negative. One expla-

nation is that the leverage of the firm increases with negative returns inducing a

higher volatility. It is most likely that these leverage effects will be found in firms

which already employ considerable debt financing. While we do not have data on

the debt-equity ratios of the individual firms in the emerging markets, many of

the countries themselves are highly levered. Hence, it seems important to allow

for the possibility of asymmetries in the variance function.

To accommodate asymmetries, we modify the third equation in (2) with:

σ2i,t = wi + αiσ

2i,t−1 + βiε

2i,t−1 + γiSi,tε

2i,t−1 (5)

where Si,t is an indicator variable:

Si,t ={

1, if εi,t−1 < 00, if εi,t−1 ≥ 0 .

This is the Glosten, Jagannathan and Runkle (1993) and Zakoian (1990) model.3

If negative returns increase volatility, γi > 0. This model allows us to test for

asymmetries in the volatility of emerging equity returns.3 Engle and Ng (1993) found this model to perform better than other asym-

metric models in Monte Carlo experiments.

7

3.2 Results

A detailed analysis of the estimation of the univariate, segmented models is

contained in Appendix A. We will briefly summarize these results. Table 2 sum-

marizes the final volatility model chosen for each country. The selection criteria

are based on the model diagnostics, parameter values and a visual examination of

the fitted variances.4 If a number of models are similar, the most general model

is chosen. In most cases, the choice of model was straight forward. However, with

five countries, Indonesia, Jordan, Nigeria, Taiwan and Turkey, the choices were

more difficult.

Our diagnostics are generalized method of moments based tests of the correct

specification of the conditional mean, conditional variance and first four uncondi-

tional moments of the standardized residuals (zi,t). Consequently, these tests are

quite demanding. Overall, the tests reveal evidence against the null hypothesis

for almost all countries. Clearly, the GARCH models have difficulty fitting the

highly volatile and nonnormal returns.

In general, the asymmetric GARCH provides an improvement in fit for most

of the countries in our sample. Surprisingly, negative return innovations appear

to decrease volatility in most countries, i.e. γi is estimated to be negative. This

is the opposite to what would be predicted based on the leverage hypothesis. A

potential explanation derives from survivorship bias.5 The markets in our sample,

with a few exceptions, are the ones that have emerged as successful stock markets.

For markets to survive, volatility ought to decrease (at least some of the times)

when markets are hit with negative shocks. This potentially explains the negative

relation between volatility and returns shocks.

Finally, our tests reveal that for some countries, the conditional mean spec-

ification may not be adequate. The tests reveal that there is significant serial

4 In this estimation, we constrained α + β ≤ 1, even when asymmetric effectswere allowed for. We reestimated all models for which this constraint was bindingrelaxing the constraint. Although α + β > 1 means covariance stationarity isviolated, the process may still be strictly stationary. Our cross-sectional analysisuses the estimates where the constraint is not imposed.

5 We thank Steve Ross for this suggested interpretation.

8

correlation in the standardized residuals in a number of countries.

While some estimations seem ill-behaved or are rejected, it is remarkable that

the different models yield quite similar volatility behavior over time. Although

we believe that the simple model in (2) captures an important part of the time-

variation in conditional variances, the specification tests indicate that we may be

missing some interesting dynamics. In particular, the assumption of having only

local variables and shocks affecting conditional means and variances may be too

restrictive.

4. World versus local factors in volatility

4.1 Model specification

In integrated world capital markets, shocks to the world market return affect

all countries who have nonzero covariances with the world market. Bekaert and

Harvey (1995) develop a model of the conditional mean return in emerging markets

which allows for time-varying influences of both local and world factors. We

apply the same type of intuition to our variance model. That is, as a market

becomes more integrated both the conditional mean and the variance should be

more influenced by world factors.

The following model allows for world and local influences in both the condi-

tional means and variances. First, consider a model that describes world market

returns and variances:

rw,t = δ′wXt−1 + εw,t

σ2w,t = cw + αwσ

2w,t−1 + βwε

2w,t−1,

(6)

where Xt−1 represents a set of world information variables which include: a con-

stant, the world market dividend yield in excess of the 30-day Eurodollar rate,

the default spread (Moody’s Baa minus Aaa bond yields), the change in the term

structure spread (U.S. 10-year bond yield minus 3-month U.S. bill), and the change

in the 30-day Eurodollar rate. These variables are designed to capture fluctuations

9

in expectations of the world business cycle. All of these information variables are

lagged.

Following Bekaert and Harvey (1995), we allow the mean return in country i

to be a function of both world and local information:

ri,t = θi,t−1κiδ′wXt−1 + (1 − θi,t−1)(δ′

iXi,t−1) + εi,t, (7)

where Xi,t−1 represents the local information variables: a constant, the equity

return, the exchange rate change, and the dividend yield, all of which are lagged.

The influence of world and local information on the emerging market’s expected

return is allowed to change through time.6 The parameter, θi,t−1, represents the

importance of the world information variables. We restrict:

θi,t−1 =(λ′

iX∗i,t−1)

2

1 + (λ′iX

∗i,t−1)2

(8)

to fall in the range [0,1]. We consider a number of variables for X∗i,t−1 which

might proxy for the degree of integration. When capital markets open up to

foreign investment, the change of the marginal investor typically results in an

increase in market capitalization to GDP. Finally, we consider the size of the trade

sector (exports plus imports divided by GDP). International trade may enhance

the cross-country correlation of consumption and business-cycles which, in turn,

might lead to prices of risk and/or risk exposures moving together – even when

capital markets are segmented. Note that the quadratic relation in (8) implies that

the relation between X∗i,t−1 and θi,t−1 need not be monotonic over the sample.

This is useful when market capitalization increases because of local factors, for

example, the introduction of a private pension plan.7

We allow the shocks to the local returns to be driven by both world and local

shocks:

εi,t = ξiψi,t−1εw,t + ei,t (9)

6 The evidence in Garcia and Ghysels (1994) suggests that if expected returnsin emerging markets are conditioned exclusively on world information variables,there is evidence of structural instability in linear models.

7 We experimented with logistic functions but found estimation to be muchbetter behaved with quadratic functions.

10

where ξi is a scale parameter and ψi,t−1 represents the importance of the world

shock which is also restricted to fall in the [0,1] range.

ψi,t−1 =(ζ′

iX∗i,t−1)

2

1 + (ζ′iX

∗i,t−1)2

. (10)

As with θi,t−1, ψi,t−1 is time varying and a function of local information which

proxies for the degree of integration.

We impose additional restrictions:

(a) E[ei,tei,j |It−1] = 0 ∀i 6= j

(b) E[ei,tεw,t|It−1] = 0 ∀i

(c) E[e2i,t|It−1] = (σ`i,t)

2 = ci + αi(σ`i,t−1)

2 + βie2i,t−1.

(11)

This model implies:

E[ε2i,t|It−1] = σ2i,t = ξ2i ψ

2i,t−1σ

2w,t + (σ`

i,t)2 (12)

and

E[εi,tεw,t|It−1] = ξiψi,t−1σ2w,t = σiw,t. (13)

This model is related to, but different from, the factor ARCH models of

Engle, Ng and Rothchild (1990, 1992), King, Sentana and Wadhwani (1994) and

Diebold and Nerlove (1989). In these models, a world factor is allowed to influence

volatility at a constant rate. In the special case where θi,t−1 = ψi,t−1 = 1 for

all t, the variance model is similar to the Engle, Ng and Rothchild model. If

θt−1 = 1 and δwXt−1 is the world market premium, then the κi coefficient in the

conditional mean specification can be interpreted as the constant factor loading in

a world capital asset pricing model. These factor models also imply the restriction

κi = ξi. Below we will perform tests of κi = ξi and θi,t−1 = ψi,t−1 both jointly

and separately.

Unlike our previous models, this specification allows for both local and world

influences in the mean and the variance. Importantly, the influence, in both cases,

is allowed to change through time as a function of local variables which contain

11

information regarding the country’s degree of financial and economic integration

with world economic markets.

The covariance dynamics of our model have two important implications.

First, the covariance with the world market return is positively related to the

degree of market integration. Second, the covariance with the world return in-

creases in times of high world market volatility. As such, our results contribute

to the literature on international stock market linkages.8

The two stylized facts often noted in this literature are that the process of

globalization and deregulation has increased the correlations between stock mar-

kets over time and that the correlation between markets rises in periods when the

volatility of markets is large (for example, around the October 1987 crash). The

empirical evidence, particularly on the first fact, is mixed, however. For example,

although Longin and Solnik (1994) document an upward trend in international

correlations, King, Sentana and Wadhwani (1994) argue that the increase in cor-

relations may be transitory and related to the October 1987 crash.

The world market correlation in our model is given by:

ρit = ξiψi,t−1σw,t

σi,t. (14)

Hence, correlations increase when markets become more integrated or when world

market volatility is high relative to local volatility. The latter mechanism is the

only one present in the model of King, Sentana and Wadhwani (1994) to induce

higher correlations between markets. A trend in the correlations can only arise

when the factors in their model exhibit integrated GARCH behavior. Below we

will graph the conditional correlations implied by the model. We will also inves-

tigate their behavior post-crash and post-capital market liberalization relative to

the full sample.

Nevertheless, the assumptions in (11) are quite restrictive. Any misspecifi-

cation will also result in misspecification of the covariance term. For example,

8 See, King and Wadhwani (1990), Longin and Solnik (1994) and King, Sentanaand Wadhwani (1994). Erb, Harvey and Viskanta (1994) show that correlationsare higher in down markets and during recessions.

12

as long as ξi is positive, the covariance with the world return can never become

negative. Therefore, we estimate a more general covariance model:

σiw,t = ki + fiψi,t−1σ2w,t + gi(σ`

i,t)2 (15)

where fi 6= ξi. We refer to this model as the “extended model.” If our represen-

tation is true, we should find:ki = 0

fi = ξi

gi = 0.

(16)

These restrictions are testable.

One additional diagnostic of the model involves its implications for the cor-

relations between emerging markets. Since we have 20 different markets in our

sample, joint estimation for all countries is computationally infeasible. As a result,

we concentrate our attention on bivariate models. Our model implies:

E[ut|It−1] = 0

with ut = εi,tεj,t − ξiξjψi,t−1ψj,t−1σ2w,t

(17)

and note that the correlation coefficient is

ρij,t =ξiξjψi,t−1ψj,t−1σ

2w,t

σ2i,tσ

2j,t

. (18)

It is well known that cross correlations between emerging markets are small. Our

model may help us understand why the correlations are small. The correlations

between markets are directly linked to their degree of integration in world capital

markets.

Finally, we examine the proportion of local variance accounted for by world

factors. The following variance ratio is computed:

VRi,t =ξ2i ψ

2i,t−1σ

2w,t

σ2i,t

∈ [0, 1]. (19)

Using the definition in (13), we can equivalently write:

VRi,t =ξiψi,t−1σiw,t

σ2i,t

. (20)

13

The variance ratio can be decomposed into three pieces:

ξiψi,t−1︸ ︷︷ ︸Degreeof integration

σiw,t

σi,tσw,t︸ ︷︷ ︸Correlation

σw,t

σi,t︸︷︷︸Volatility ratio

.

V Ri,t gives an indication of the proportion of the conditional variance that cannot

be accounted for by the fully segmented model from section 3. We will also

examine the time-variation in V Ri,t post-crash and post-liberalization.

4.2 Estimation and specification tests

We estimate (6) using maximum likelihood assuming normal innovations.

These parameter estimates are then held fixed in the actual maximum likelihood

estimation of the bivariate model. We report White (1982) standard errors that

are robust to misspecification of the distribution of the error terms. However, we

do not correct for the sampling error of the world market model parameters in the

first-stage estimation. This approach yields consistent but not necessarily efficient

estimates.

Important assumptions underlying our bivariate country by country esti-

mation are: (1) The density of rw,t conditional on It−1 depends on θw =

[δ′w, cw, αw, βw]′ only not on any θi = [δ′

i, ci, αi, βi, κi, ζi,λ′i, ξ

′i]′ for all i and (2)

The density of ri,t conditional on It−1 and rw,t depends on [θ′w,θ

′i]′ and not on

any θj , j 6= i and the individual country specific shocks are independent across

emerging markets [assumption (b) in (11)].

4.3 Results

We structure our discussion of the results in three parts. First, we will ex-

amine the parameter estimates of the world factor model and the diagnostics.

Second, we will detail the implied level of integration, the time-varying correla-

tion with the world and the importance of world factors. Finally, we examine

some individual countries in greater detail.

14

4.3.1 Parameter estimates and diagnostics

Table 3 presents some parameter estimates and residual diagnostics for the

world factor model. In some countries, the estimation was ill-behaved. Global

optima may not have been found in Nigeria, Portugal, Taiwan, Turkey, Venezuela

and Zimbabwe. No estimation was attempted for Indonesia since there are too

few observations.

Consider the factor ARCH restrictions. These models imply that κi = ζi and

that the world factor impacts volatility at a constant rate. The first restriction,

κi = ξi, is rejected in 14 of 19 countries at the 10% level of significance. The

second restriction, ψit = θit, is rejected in 17 of 19 countries. A joint test provides

evidence against the factor ARCH restrictions in all 19 countries. The implication

is that world factors affect volatility in a different manner through time. This is

exactly what our model is designed to capture.

The results of estimating the extended covariance model are presented are also

presented in Table 3. The restriction fi = ξi is examined with a likelihood ratio

test. The restriction is not rejected in Argentina, Brazil, Chile, Malaysia, Mexico,

and Turkey at the 10% level of significance. In two countries, Philippines and

Portugal, the estimation of the extended model failed. When all of the covariance

restrictions are simultaneously tested (fi = ξi as well as gi = ki = 0) there is

much more evidence against our model’s specification. Of the 11 countries where

it was possible to estimate this version of the extended model, the restrictions

were rejected for 7 (6) countries at the 10% (5%) level of significance.

Finally, the residual diagnostics are presented in the final columns of Table

3. These diagnostics test for serial correlation in the standardized disturbances of

the model for the mean and variance. These tests reveal little evidence against the

specification. The means test does not reveal a single rejection for the 19 countries.

The test on the variance disturbances shows evidence against the specification in

only Portugal.

It is possible that these residual diagnostic tests lack power. Importantly,

these results must be taken together with the tests of the extended covariance

15

specification which show evidence against the specification in many of the coun-

tries. Of course, our model is highly parameterized relative to the available data.

It is perhaps not surprising that the specification is rejected in many countries.

Nevertheless, it may be possible to learn something from a model which is statis-

tically rejected.

4.3.2 Correlation, integration and the importance of world factors

Table 4 details the proportion of variance due to world factors, the average

levels of market integration and the average conditional correlations from the

world factor model.

The mean proportion of variance due to world factors is provided over the

entire sample and for two subperiods. The first subperiod is the post October

1987 (post crash) period. The second subperiod is calculated after significant lib-

eralizations. This subperiod is country specific. 17 of the 19 countries experienced

capital market liberalizations (exceptions were Nigeria and Zimbabwe). This al-

lows us to examine the hypothesis that the importance of world factors increases

after capital market liberalizations.

The results suggest that only a small amount of variance is being driven by

world factors. In 16 of the 19 countries, the average proportion of variance being

driven by world factors is less than 10%. The countries that are most affected by

world factors are Malaysia, Portugal, Philippines, Greece, Mexico and Thailand.

In the subperiod beginning in November 1987, the degree of importance of

world factors increases for 12 of 17 countries (Indonesia not estimated and Turkey’s

data begins after November 1987). This period often coincides with capital market

liberalizations. In the post-liberalization period, the importance of world factors

increases in 10 of 16 countries (no liberalizations in Nigeria and Zimbabwe). Some

increases are particularly dramatic. For example, the importance of world factors

triples in Thailand after their liberalization. In Greece, the influence of world

factors doubles after their capital market liberalization.

16

The changing importance of world factors is mirrored in the time-variation

in conditional correlations with the world market portfolio. In 12 of 17 countries,

correlations increase after capital market liberalizations. Again, there are a num-

ber of dramatic examples. In the full sample, the average conditional correlation

of Thailand and the world market is 15.5%. In the post-liberalization period the

correlation increases to 37.9%. Similarly, in the full sample, the correlation be-

tween the Mexican equity return and the world market return is 19.8%. In the

subsample since capital market liberalization, the correlation is 41.5%.

Table 4 also presents the average level of mean and variance integration. The

discussion on these parameters will be focussed on the three countries we examine

in detail. Note that the interpretation of these numbers as “degree of integration”

may be strong. They reflect the relative time-varying importance of global versus

local factors in the mean and variance.

4.3.3 Further analysis for specific countries

While space does not permit a detailed examination of every country, we

highlight in this section three important emerging equity markets: Korea, Mexico

and Thailand.

4.3.3.1 Korea

Korea is the second largest of the “emerging” equity markets with market

capitalization of $107.4 billion at the end of 1992. Liquidity is very good with

average daily trading volume of $480 million. Indeed, the capitalization of the

equity market represents almost 50% of GDP. While this information suggests a

well functioning integrated capital market, there have been a number of regulatory

hurdles to foreign participation in Korean equity markets. Given these restrictions,

one might assume that the Korean equity markets are not integrated with world

capital markets.

17

The foreign ownership rules were relaxed in January 1992 (just as our sample

ends). Even with these “relaxed” regulations, foreign ownership is limited to only

10% of the market capitalization in so-called “unlimited industries,” and 8% in

the “limited industries” which include utilities, defense, shipping, transportation,

finance and communications. The 10% limit was almost immediately raised to

25% for the companies that already had more than 10% foreign ownership. In

addition, no single investor can own more than 3% of any Korean firm. Foreign

investors must apply for an ID number from a Korean broker on the Korean Stock

Exchange and all shares must be held in the owner’s name. This liberalization

still presents significant barriers to entry.9

Nevertheless, there are alternative ways to access Korean capital markets.

At the end of 1992, there were 34 Korea funds trading either privately or listed

on the New York Stock Exchange, London Stock Exchange, Hong Kong Stock

Exchange. The ability to access Korean investments in this way could account

for the estimated integration parameters presented in Figure 1a. The estimates

support the idea that the Korean equity market is integrated into world capital

markets and are consistent with the analysis presented in Bekaert and Harvey

(1995).

Although the Korean market appears integrated, very little variation in the

volatility is accounted for by world factors. Figure 1b indicates that less than

10% of the variation is due to world factors in the base model and there has been

little change through time. The small role of world factors is mimicked in the

conditional correlations presented in Figure 1c. The influence of world factors is a

function of the correlation, the degree of integration and the volatility ratio. Given

that the first three elements are fairly constant through time, it is of little surprise

that the ratio of world volatility to local volatility is fairly constant through time.

This is evident in Figure 1d.

9 Bekaert (1995) provides a catalogue of barriers to entry. Also see Bae (1993).

18

4.3.3.2 Mexico

Mexico is one of the largest emerging markets with a capitalization of US$139

billion, high trading volume of US$171 million daily and high GDP per capita

of US$2500. Most probably consider Mexico a reasonably “integrated” capital

market. This opinion is perhaps influenced by its proximity to the U.S. or the

large number of ADRs (36 in June 1992) and closed end funds (6 funds with

capitalization of US$16 billion). However, this prior belief is influenced by the

recent capital flows to Mexico.

Figure 2a presents the time-series of the measures of integration for the mean,

θi,t−1, and for the variance, ψi,t−1. Both integration measures trend upwards until

1982 and then begin to decline. This coincides with the debt crisis which began

in 1982.

The implied degree of integration sharply increases after 1988. This is es-

pecially evident for the measure of integration associated with the conditional

variance.10 This roughly coincides with significant capital market liberalizations.

For example, after 1989, 100% foreign investment in most firms is possible. Key

sector firms are restricted to 49% foreign participation and the foreign investment

limit in the banking industry is 30%.

The effect of the capital market liberalization is particularly evident in the

world factor proportions and the conditional correlations presented in Figures 2b

and 2c. These measures sharply increase after 1988.

The influence of world factors is accounted for by the degree of integration,

the conditional correlation and the volatility ratio. Figure 2d suggests that the

volatility ratio does not contribute to the increased importance of world factors.

While variable through time, it exhibits no particular trend.

10 There are a number of similarities and differences between the fitted integra-tion series and the one presented in Bekaert and Harvey (1995). The series inFigure 2a and the one in Bekaert and Harvey (1995) both drop sharply in 1982.However, there is no clear pattern in the integration measure (which is restrictedto the mean) after 1982. In contrast, the measure based on the variance in Figure2a sharply increases after 1988.

19

4.3.3.3 Thailand

Figure 3a presents the time-series of market integration measures from the

standard model for Thailand. The measures of integration for the mean, θi,t−1,

and for the variance, ψi,t−1, are presented. The average levels of these measures

increase during 1987. This follows a number of liberalizations in the Thai market

which culminate in December 1988. In particular, Bailey and Jagatini (1994)

detail the opening of the Alien Board for extranational trading of Thai securities

at this time. The measure for the mean is far more variable than the measure

for the variance.11 The extended model results for Thailand are similar to the

constrained, standard model. While not shown, the time-series patterns in both

integration measures appear similar to the standard model.

The influence of world factors is detailed in Figure 3b. Before 1987, little to

none of the variance in the Thai returns can be accounted for by world factors.

This is also evident in the overall mean of 0.04 reported in Table 3. However,

there is a sharp increase after 1987. In recent years, world factors account for

closer to 20% of the variation in the local variance.

Figure 3c details the time-varying conditional correlations with the world

market return. There is a sharp increase in the conditional correlations in 1987.

However, this increase is not entirely due to the stock market crash in October.

The correlations begin to increase in January 1987. In addition, the increase in

conditional correlation in 1987 and 1988 does not appear to be transitory. The

correlations level off to about 40% in the last three years of the sample. This is

comparable to the average level of correlation that Harvey (1991) details for 17

developed market returns.

The influence of world factors can be decomposed into three pieces. We have

detailed the first two: the degree of integration and the conditional correlation

with the world. The final piece is the ratio of volatility: world to local. Figure

11 In Bekaert and Harvey (1995), a similar pattern in the integration parame-ter was detected based on the mean. Nevertheless, the evidence in Figure 3a ismuch more consistent with capital market liberalizations having some bite afterDecember 1988.

20

3d shows the time-series patterns in this ratio. Since 1984, the ratio has been

decreasing (Thai volatility higher relative to world volatility).

5. The cross-section of volatility in emerging markets

5.1 Explaining volatility across emerging markets

One important difference between developed and emerging capital markets, is the

dispersion of volatility across countries. Harvey (1993) shows that the range of

unconditional volatilities in developed markets is 18% (from high to low). In

emerging markets, the range is 86% (18% for Jordan and 104% for Argentina).

We explore four sources of volatility differences: asset concentration, stock market

development/economic integration, microstructure effects, and finally macroeco-

nomic influences and political risk.

The most obvious source is the degree of diversification and concentration

inherent in the IFC index for each country. Schwert (1989a), Harvey (1991) and

Roll (1992) explore whether the number of stocks included in the index influences

the cross-section of volatility. We construct a time-series of the number of stocks

included in each of the IFC country indices. Following previous research, we

use the natural logarithm of the number of stocks as a proxy for the degree of

diversification.

The number of stocks in the index may not be that revealing of diversification

if there are few dominant stocks and many small stocks. Roll (1992) and Harvey

(1995c) examine asset concentration ratios:

CRi,t =

√√√√ Ni,t

Ni,t − 1

Ni,t∑

j=1

(wij,t −1Ni,t

)2 (21)

where Ni,t are the number of individual securities in the country i index in month

t and wij,t is the share of market capitalization represented by stock j at time t.

If one stock dominates the index, then CR gets close to one. If every stock has

equal market capitalization, then CR = 0. Using the EMDB’s individual stock

data, we create time-series of concentration ratios for each country.

21

A country index may have many stocks and a low concentration ratio but

may still not be diversified if all of the stocks are involved in a single industry.

Divecha, Drach and Stefan (1992) construct industry concentration ratios for a

single year using all available securities in 20 emerging markets. Harvey (1995c)

finds that these industry concentration data have only limited ability to explain

unconditional variance differences over the past five years. Given that a time-

series of industry classifications is not available, we are unable to examine the

effect of industrial concentration on the cross-section of volatility.

The second source is linked to both the development of stock market and

the degree of market integration. Unfortunately, exact measures of stock mar-

ket development and economic integration are difficult to specify. We consider

several proxies. First, we compute the cross-sectional volatility of the country’s

component stock returns at each point in time. As an economy becomes more

developed, it often becomes more diverse. If this diversity is reflected in the firms

whose equity is part of the IFC indices, then we would expect the cross-sectional

volatility of the country’s component stocks returns to increase. That is, as stocks

are less dependent on one sector, their covariances should decrease which should

increase the cross-sectional variance. At the level of the index, this effect should

decrease market volatility. Hence, our first measures of stock market development

are the cross-sectional standard deviation of each index’s component stock returns

and the cross-sectional mean absolute deviation. These are measured each month

relative to the average stock return in each country index. This interpretation of

the cross-sectional standard deviation will not necessarily hold in more developed

markets.

We also consider a number of additional proxies for stock market development

and economic integration. Bekaert and Harvey (1995) propose a model where

market integration is parameterized. They find that equity capitalization to GDP

is a useful instrument in characterizing the time-series of market integration. Stock

market capitalization to GDP is also often used as a stock market development

indicator [see Demirguc-Kunt and Levine (1993)]. We also track the size of the

trade sector by forming the ratio of exports plus imports to GDP.

22

The third source of volatility arises from market microstructure research. It is

well known that the heterogeneity of traders’ information sets as well as liquidity

affects the variance of returns. We proxy for these effects by examining the roles

of the number of stocks traded in any month and turnover ratios in explaining

the cross-section of volatility. In more developed markets, large changes in prices

across securities suggest that more private information is being revealed to the

market. In the model of Ross (1989), the volatility of prices is directly linked to

the rate of information flow in the market. Hence, increases in the cross-sectional

volatility could raise the variance of the distribution of future prices.

The fourth category focusses on macroeconomic volatility which Schwert

(1989a,b) shows is one of the underlying forces affecting stock market volatil-

ity. Unfortunately, the macroeconomic data are sparse or nonexistent in some of

the emerging markets. For instance, inflation variability is an obvious candidate

for an explanatory variable. However, the data are quite difficult to obtain and,

even if we used the published data, they are highly suspect in a number of coun-

tries. Since purchasing power parity is not rejected in high inflation countries [see

Liew (1995)], we use the variability of foreign exchange rate changes to proxy for

inflation variability.

Finally, political risk is also a likely candidate to influence the cross-section of

volatility. A time-series of political risk ratings is difficult to obtain.12 We chose to

focus on Institutional Investor’s Country Credit Ratings. These ratings are based

on a semi-annual survey of bankers. Institutional Investor has published this sur-

vey in its March and September issues every year since 1979. The survey represents

the responses of 75–100 bankers. Respondents rank each country on a scale of 0

to 100, with 100 representing the smallest risk of default. Institutional Investor

weights these responses by its perception of each bank’s level of global prominence

and credit analysis sophistication [see Erb, Harvey and Viskanta (1994, 1995)].

Credit ratings are not meant to solely represent a measure of political risk.

12 One of the best known rating services, Business International’s Country As-sessment Service, began ratings in 1971. It was sold in 1986 to The Economist.While the recent data from the Economist’s Intelligence Unit are available, thehistorical data has not been recompiled.

23

Many macroeconomic, as well as political factors, enter the bankers’ decisions on

the credit worthiness of a particular country. This variable captures both political

risk and macroeconomic stability. It is the only variable that we examine that

is ex ante (in the sense that participants are asked to assess the future credit

worthiness).

5.2 Methodology

The raw material for the cross-sectional analysis are the time-series estimates of

conditional volatility. We estimate a pooled time-series cross-sectional regression:

`n(σ2i ) = αi + β′Xi + ui i = 1, . . . ,N. (22)

There are N countries. σ2i is a Ti × 1 vector of preestimated conditional vari-

ances where Ti is the number of observations for country i, Xi is a matrix of L

explanatory variables for country i, αi are a set of intercept coefficients (one for

each country), and β is a L×1 coefficient vector. We use the conditional variance

estimates from the univariate, segmented volatility model. To check robustness,

we also report results using the world factor specification.

This model allows for fixed effects in the cross-section by not imposing that

the intercepts are identical across different countries. However, we also examine a

specification in which the intercepts are constrained to be constant across coun-

tries. This allows us to test how much of the variation in volatility is explained

by the specified variables. Our approach allows us to examine all observations for

all countries simultaneously.

Our initial estimation technique is ordinary least squares using the stan-

dard White (1980) correction for conditional heteroskedasticity. A standard La-

grange multiplier test reveals substantial evidence against homoskedasticity across

countries.13 Hence, we also present a generalized least squares estimation which

13 We adjusted the standard test discussed in Greene (1993) for the unequalnumber of observations present in our analysis.

24

allows for heteroskedasticity across countries (“group-wise heteroskedasticity”).

Finally, we present estimates which correct for both group-wise heteroskedasticity

and serial correlation. The serial correlation correction, which is detailed in Greene

(1993), is specific to each country and is based on the Prais-Winsten method. This

correction is particularly important given the extreme serial correlation in some

of the countries’ fitted volatility estimates.

Our formulation forces the coefficients to be identical through time. One way

to avoid this assumption is to estimate the model month by month. However,

between 1976 and 1981 we only have nine countries in the cross-section. As a

result, we do not have the degrees of freedom to feasibly estimate at each month.

5.3 Results

5.3.1 Summary analysis

The fitted volatility series cover (at most) 1977:01 to 1992:12. There are a total of

2627 fitted variances. However, the country credit ratings only begin in 1979:03.

As a result, for nine countries 32 observations are lost, reducing the total number

of observations to 2339.

Some summary statistics on the variables used in the cross-sectional regres-

sions are included in Table 5. The average values of the cross-sectional standard

deviation, number of firms in each index, asset concentration factor, country credit

rating, trade to GDP and market capitalization to GDP are presented in this ta-

ble. Correlation of the average volatilities and these variables are presented in

Table 6.

While the correlations are only based on 20 observations, there is much to be

learned. There appears to be a positive relation between the cross-sectional stan-

dard deviation and the volatility. The largest cross-sectional standard deviation

is found in Argentina (which also has the largest volatility).

There appears to be a weak negative relation between country credit ratings

and volatility. Lower quality ratings are generally associated with higher volatility.

25

However, one observation, Taiwan, is very influential. Taiwan has the highest

credit rating and also the fourth highest volatility.

The correlation matrix suggests that there is a weak positive relation between

the average number of companies in the index and volatility. This implies that a

larger number of companies in the index increases volatility. However, the relation

is not significant.

The analysis of concentration factors is difficult to interpret. The correlation

analysis reveals a negative relation between concentration and volatility. However,

the analysis is highly influenced by Jordan, which has the highest concentration

factor and the lowest volatility. Without Jordan, there appears to be a positive

relation (more big firms implies higher volatility).

There is a distinct negative relation between exports plus imports divided by

GDP and volatility. This suggests that the more open the economy is to trading,

the lower the volatility. The correlation between these variables is -38%.

There is also a negative relation between market capitalization to GDP and

volatility. This relation is weakened by two influential observations: Malaysia and

Taiwan. Both of these countries have very high capitalizations to national output

ratios and high volatility.

5.3.2 Time-series cross-sectional analysis

The time-series cross-sectional regression results are presented in Table 7. Panel

A considers the estimation with the standard White (1980) correction for het-

eroskedasticity. The results which correct for group-wise heteroskedasticity are

presented in Panel B and the estimation which corrects for both group-wise het-

eroskedasticity and serial correlation is in Panel C. In the base case with no

country-specific intercepts, 34% of the cross-section of volatility is explained with

the eight variables. Separate regressions are run with the cross-sectional standard

deviation of the individual index stocks and the cross-sectional mean absolute

deviation because these measures are 99% correlated. When the country specific

26

intercepts are included, the explanatory power of the regressions increases to 60%.

The way the cross-sectional standard deviation enters the regression depends

on the level of market development. As such, we allow this variable to en-

ter the regression as an interaction variable associated with the deviation from

the cross-sectional mean market capitalization to GDP ratio. If MCit/GDPt <

(MCt/GDPt) which is, for example, always true for Zimbabwe, Brazil, Pakistan,

then an increased cross-sectional standard deviation negatively affects the market

volatility. If MCit/GDPt > (MCt/GDPt), then the derivative of volatility with

respect to the cross-sectional standard deviation is positive which is what is pre-

dicted by the information flow model of Ross (1989). The results provide some

support for this specification. Both the cross-sectional standard deviation and

the interaction term enter the regression with coefficients more than 1.6 standard

errors from zero in Panels A and B. The coefficients are positive for the regression

with standard deviations in panel C but not significantly different from zero.

The number of companies in the index has a negative coefficient that is about

two standard errors from zero in the base estimation. This implies that a larger

number of companies decreases volatility. This is exactly what one would expect.

However, in Panels B and C, this variable fails to play a significant role.

The evidence on the concentration factor is somewhat puzzling. This factor

is weakly negatively related to volatility. That is, a country with a few highly

concentrated firms is likely to have lower volatility. This variable is highly signif-

icant in all the regressions with fixed effects in panels A and B. In panel C, the

coefficient is less than two standard errors from zero in the fixed effects regression.

We investigated the influence of Jordan by adding a slope dummy variable for this

country. There was still a negative effect. The results suggest that markets with

a few very large stocks have lower volatility.

The relation between turnover and volatility is difficult to interpret. There

are two countries, Taiwan and Korea, with turnover ratios an order of magnitude

greater than the other countries. Using 18 countries, there is weak negative re-

lation between turnover and volatility when measured by averages. However, in

the time-series cross-sectional regression, turnover is positively related to volatil-

27

ity. Since the turnover data begin in 1986, a separate regression is estimated with

turnover included and the coefficients are reported in the far right column of panel

A of Table 7.

In many of the regressions, the country credit rating enters with a negative

coefficient. This implies that a lower credit rating is associated with higher volatil-

ity. This variable is often significant in panels A and B and less significant in panel

C.

There is also a very significant negative relation between the size of the trade

sector and volatility. In the regression without country specific dummy variables,

the trade sector is often more than 5 standard errors from zero irrespective of

the standard error correction. A more open economy is associated with lower

volatility.

The market capitalization to GDP ratio enters the regression with a positive

sign in panels A and B. This is unexpected given the correlation analysis suggested

a negative relation (larger equity market implies lower volatility). However, this

variable is highly correlated with the trade variable (70%). When the regression

is run without the trade variable, the market capitalization ratio enters with a

less significant coefficient. In addition, in the estimation which corrects for serial

correlation and heteroskedasticity, this variable enters with a positive coefficient

which is more than two standard errors from zero.

Finally, the volatility of foreign exchange rate changes plays a very important

role in explaining equity return volatility. In the regression without fixed effects

the coefficient on this variable is 15 standard errors from zero. When country

dummy variables are allowed, the coefficient is six standard errors from zero. The

significance of this variable may be not that surprising given that we are measuring

equity returns in U.S. dollars although it may also capture inflation variability.

In addition, an extensive sensitivity analysis is conducted. In the univariate

models where the αi + βi constraint was binding, we reestimated removing the

constraint. When these fitted values were used in the time-series cross-sectional

analysis, there was little difference in the results.

In another exercise, we used the fitted variances from the world factor estima-

28

tion (except for Indonesia) in the time-series cross-sectional analysis. The results

are broadly consistent with those reported in Table 7. In panel D, we report two

of the estimations using the world factor variances. This estimation corrects for

the group-wise heteroskedasticity and serial correlation. Although most results

are robust, there are a few differences. The cross-sectional standard deviation

variable now enters with a positive sign and the interaction term with MCGDP

also obtains a positive and much larger coefficient. Although the coefficients are

not statistically significant, the effects are as expected: for countries with large

market capitalizations, increases in the cross-sectional standard deviation reduce

volatility.

Overall, the sensitivity analysis and the results in Table 7 suggest that the

prespecified variables capture an important part of both the cross-section and

time-series of volatility.

5.4 Capital market liberalization and volatility

Figure 4 informally characterizes the effect of capital market reforms on vari-

ance. The average conditional variance two years after the reform is depicted on

the y-axis and the average conditional variance two years before the reform is pre-

sented on the x-axis. On average, if there is no effect on volatility the variances

should fall around the 45o line. If variance decreases, then many of the points

should fall below this line.

The major liberalization dates are from Bekaert (1995) and are contained in

Table 8. The evidence in Figure 4 suggests that volatility decreases after liberal-

izations. Of the 17 countries, where there was a liberalization within our sample,

only 4 show evidence of increased volatility (Pakistan, Colombia, Venezuela and

Turkey). In most countries, volatility decreases. Particularly dramatic decreases

are found for Taiwan, Mexico, Portugal, Argentina and Brazil. The evidence for

Brazil is consistent with the empirical findings in Bekaert, Garcia and Harvey

(1995b).

29

A weakness of this analysis is that other events could occur that decrease

or increase volatility that have little to do with capital market liberalizations.

Therefore, we introduce liberalization dummy variables into our cross-sectional

analysis and test whether, after controlling for these factors, these interventions

significantly decrease volatility.

The results are contained in panels A through D of Table 7. We introduce

four dummy variables to break each of the 17 countries’ volatility into four pieces:

early (more than 30 months before liberalization), pre (30 to six months prior to

liberalization), during (six months prior to three months after liberalization) and

post (four months after liberalization to end of sample.) The logic here is that

when liberalizations are pre-announced or anticipated by market participants,

volatility may change some time before the liberalization date.

Consider Panel A of Table 7. The pre, during and post indicators enter

with coefficients that are significantly different from zero. In both the regressions

that use standard deviations and mean absolute deviations, the coefficient on the

post-liberalization indicator is lower than the coefficient on the pre-liberalization

indicator. For example, in the regression that uses mean absolute deviation, the

post-liberalization coefficient is 0.279 and the pre-liberalization coefficient is 0.345.

We also conducted a heteroskedasticity consistent Wald test of the the hypothesis

that the coefficient on the pre and post-liberalization dummy variables were the

same. The null hypothesis is not rejected with a p-value of 0.229.

The evidence of a volatility change after liberalization in much sharper in the

estimations which use generalized least squares. For example in the regression

which corrects for group-wise heteroskedasticity and uses standard deviation, the

coefficient on the pre-liberalization indicator is 0.130 and the coefficient on the

post-liberalization in -0.037. The heteroskedasticity consistent Wald test provides

a rejection of the null hypothesis at the 0.001 level of significance. Similarly, in

the regressions which correct for both group-wise heteroskedasticity and serial

correlation, the Wald test provides evidence against the null hypothesis at the

0.007 level of significance.

To check robustness, panel D reports the results using the conditional vari-

30

ances of the world factor model with the correction for group-wise heteroskedas-

ticity and serial correlation. In both specifications, the post liberalization volatil-

ity is slightly but not statistically significantly lower than the pre liberalization

volatility.

5.5 Economic significance of the results

In the preceding discussion, we have analyzed the statistical significance of

various influences on volatility in emerging markets. In this section, we explore

which factors are economically significant. To do so, we conduct the following

experiment. Consider a relatively closed country with a not very well developed

stock market. Such a country is likely to be characterized by high stock market

volatility, a low cross-sectional standard deviation, a high concentration ratio, and

low market capitalization to GDP. There may be political risk reflected in a low

credit rating and unstable macroeconomic policies translating into high foreign

exchange volatility. To make this more concrete, we interpret high (low) as the

75% (25%) quartile in the cross-sectional distribution of the relevant variables

using all the observations for all the countries over the full sample.

Panel A of Table 9 reports the relevant data characteristics for this hypo-

thetical country (the 25th percentile). Suppose this country opens up its capital

markets and becomes a “median” country, in terms of concentration ratios, open-

ness, political risk and the other factors. We use the cross sectional regressions

to predict what would happen to its (annualized) equity volatility. This exercise

also allows us to assess the impact of the different estimation techniques on the

model predictions.

Panel B of Table 9 details the partial effects of each of the variables. The cross-

sectional standard deviation or mean absolute deviation, the concentration ratios

and the market capitalization to GDP have relatively minor and/or inconsistent

effects. The number of companies in each index has a negative contribution (more

firms less volatility) in the OLS model and the world factor model. The credit

31

rating variable has a negative contribution (higher rating lower volatility) in the

world factor model. The trade sector to GDP, foreign exchange volatility and

liberalization indicators have consistent important contributions. When the effects

are cumulated, the implication for volatility is remarkably consistent across the

estimation methods. With the univariate variance estimation, the cumulative

effect on volatility ranges from -6.0% to -7.2%. Using the world factor variances,

the cumulative effect on volatility ranges from -6.0% to -6.6%. In other words,

the move from the 25% percentile to the median for the hypothetical emerging

market, reduces volatility by at least 6.0%.

6. Conclusions

Our research has a number of goals. First, we are interested in characterizing

volatility in emerging markets. Volatility is an important input for asset allocation

decisions. In segmented capital markets, country volatility is a critical input in

the cost of capital calculation. However, volatility is difficult to model in these

markets. We present a number of volatility models, allow for very general error

distributions, and provide a battery of diagnostic tests.

Second, we are interested in the forces that affect the time-series of volatility

in these emerging equity markets. In fully integrated markets, we believe that

volatility is strongly influenced by world factors. In segmented capital markets,

volatility is more likely influenced by local factors. Our decomposition of the

sources of variation in volatility helps shed light on the degree of integration of

each market with world capital markets and on how the degree of integration

varies over time.

Third, we explore the forces that determine why volatility is different in the

various emerging markets. We construct a number of variables: number of firms

in the country index, asset concentration factors, country credit ratings, cross-

sectional standard deviation of the individual stock returns within the indexes,

size of the trade sectors to GDP, and market capitalization to GDP ratios. Among

other interesting findings, we show that more open economies (in terms of world

32

trade) have significantly lower volatilities. We also find that political risk as

proxied by credit quality explains a large amount of the cross-sectional variation

in volatility.

Finally, we study the effect of capital market liberalizations on volatility.

Given our time-series models, we investigate whether volatility decreases after

liberalizations. Our evidence suggests that volatility either remains the same or

decreases in 13 of 17 countries. There is a sharp drop in volatility in five countries

in our sample. Even after controlling for all the potential influences on the time-

series and cross-section of volatility, we find that capital market liberalizations

significantly decrease volatility in emerging markets.

33

References

Andrews, Donald W. K., 1991, Heteroskedasticity and autocorrelation consistent covariancematrix estimation, Econometrica 59, 817–858.

Bae, Kee-Hong, 1993, Time-variation in the price of risk and the international capital marketstructure, Unpublished dissertation, The Ohio State University.

Bailey, Warren and Julapa Jagtiani, 1994, Foreign ownership restrictions and premiums forinternational investment: Some evidence from the Thai capital market, Journal of Fi-nancial Economics 36, 57–88.

Bekaert, Geert, 1995, Market integration and investment barriers in emerging equity markets,World Bank Economic Review 9, 75–107.

Bekaert, Geert, Marcio Garcia and Campbell R. Harvey, 1995a, The contribution of speculatorsto effective financial markets, Catalyst Monograph Series, Catalyst Institute, Chicago,IL.

Bekaert, Geert, Marcio Garcia and Campbell R. Harvey, 1995b, The role of capital markets ineconomic growth, Catalyst Monograph Series, Catalyst Institute, Chicago, IL.

Bekaert, Geert and Campbell R. Harvey, 1995, Time-varying world market integration, Journalof Finance 50, 403–444.

Bekaert, Geert and Robert Hodrick, 1992, Characterizing predictable components in excessreturns on equity and foreign exchange markets, Journal of Finance 47, 467–509.

Black, F., 1976, Studies of stock market volatility changes, Proceedings of the 1976 Meetings ofthe American Statistical Association, Business and Economics Statistics Section, 177–81.

Bollerslev, Tim, 1986, Generalized autoregressive conditional heteroskedasticity, Journal ofEconometrics 72, 307–327.

Bollerslev, Tim, and Jeffrey M. Wooldridge, 1992, Quasi-maximum likelihood estimation andinference in dynamic models with time-varying covariances, Econometric Reviews 11,143–172.

Bonser-Neal, Catherine, Gregory Brauer, Robert Neal and Simon Wheatley, 1990, Interna-tional investment restrictions and closed-end country fund prices, Journal of Finance45, 523–548.

Campbell, John Y. and Yasushi Hamao, 1992, Predictable stock returns in the United Statesand Japan: A study of long-term capital market integration, Journal of Finance 47,43–70.

Chen, Nai-fu, Richard Roll and Stephen A. Ross, 1986, Economic forces and the stock market,Journal of Business 59, 383–03.

Christie, Andrew A., 1982, The stochastic behavior of common stock variances: Value, leverageand interest rate effects, Journal of Financial Economics 10, 407–432.

Claessens, Stijn, Susmita Dasgupta and Jack Glen, 1995, Return behavior in emerging markets,World Bank Economic Review, 131-151.

Cumby, Robert E. and Anya Khanthavit, 1992, A Markov switching model of market integra-tion, Unpublished working paper, New York University.

Diebold, F. and M. Nerlove, 1989, The dynamics of exchange rate volatility: A multivariate

34

latent factor ARCH model, Journal of Applied Econometrics 4, 1–21.

DeSantis, Giorgio and Selahattin Imrohoroglu, Stock returns and volatility in emerging finan-cial markets, Unpublished working paper, University of Southern California, Los Angeles,CA.

Engle, Robert F., 1982, Autoregressive conditional heteroskedasticity with estimates of U.K.inflation, Econometrica 50, 987-1008.

Engle, Robert F. and G. Gonzalez-Rivera, 1991, Semiparametric ARCH models, Journal ofBusiness and Economic Statistics 9, 345–359.

Engle, Robert F., David M. Lilien and Russell P. Robbins, 1987, Estimating time varying riskpremia in the term structure: The ARCH-M model, Econometrica 55, 391–407.

Engle, Robert F. and Victor M. Ng, 1993, Measuring and testing the impact of news onvolatility, Journal of Finance 48, 1749–1778.

Engle, Robert F., Victor M. Ng and Michael Rothchild, 1990, Asset Pricing with factor-ARCHstructure: Empirical estimations for Treasury bills, Journal of Econometrics 45, 213–237.

Engle, Robert F., Victor M. Ng and Michael Rothchild, 1992, A multi-dynamic factor modelfor stock returns, Journal of Econometrics 52, 245–266.

Erb, Claude, Campbell R. Harvey and Tadas Viskanta, 1994, Forecasting international equitycorrelations, Financial Analysts Journal November-December, 32–45.

Erb, Claude, Campbell R. Harvey and Tadas Viskanta, 1995, Country risk and global equityselection, Journal of Portfolio Management, 21, Winter, 74–83.

Ferson, Wayne E. and Campbell R. Harvey, 1993, The risk and predictability of internationalequity returns, Review of Financial Studies 6, 527–566.

Garcia, Rene and Eric Ghysels, 1994, Structural change and asset pricing in emerging markets,Unpublished working paper, Universite de Montreal.

Gibbons, M. R. and W. E. Ferson, 1985, Tests of asset pricing models with changing ex-pectations and an unobservable market portfolio, Journal of Financial Economics 14,217-236.

Glosten, Lawrence R., Ravi Jagannathan, and David E. Runkle, 1993, On the relation betweenthe expected value and the volatility of the nominal excess return on stocks, Journal ofFinance 48, 1779–1802.

Gray, Stephen F., 1993, Capturing the nonlinearities in short-term interest rates: A new classof regime-switching models, Unpublished working paper, Stanford University.

Gray, Stephen F., 1994, Semi-parametric ARCH: Evidence from the foreign currency market,Unpublished working paper, Stanford University.

Hansen, Lars P., 1982, Large sample properties of generalized method of moments estimators,Econometrica 50, 1029–1054.

Hansen, Lars P. and Robert J. Hodrick, 1983, Risk averse speculation in forward foreignexchange markets: An econometric analysis of linear models, in Jacob A. Frenkel, ed.,:Exchange rates and international macroeconomics (University of Chicago Press, Chicago,IL).

Harvey, Campbell R., 1991, The world price of covariance risk, Journal of Finance 46, 111–157.

Harvey, Campbell R., 1993, Portfolio enhancement using emerging markets and conditioninginformation, in Stijn Claessens and Shan Gooptu, Eds., Portfolio investment in develop-

35

ing countries (Washington: The World Bank Discussion Series, 1993, pp. 110–144).

Harvey, Campbell R., 1995a, Predictable risk and returns in emerging markets, Review ofFinancial Studies 8, 773–816.

Harvey, Campbell R., 1995b, The risk exposure of emerging markets, World Bank EconomicReview, 9, 19–50.

Harvey, Campbell R., 1995c, The cross-section of volatility and autocorrelation in emergingequity markets, Finanzmarkt und Portfolio Management 9, 12–34.

Heston, Steven, and K. Geert Rouwenhorst, 1994, Does industrial structure explain the benefitsof international diversification, Journal of Financial Economics 36, 3–27.

International Finance Corporation, 1993, IFC index methodology, Washington: World Bank.

Kim, E. Han and Vijay Singal, 1994, Opening up of stock markets: Lessons from emergingeconomies, Unpublished working paper, Virginia Tech.

King, Mervyn, and Sushil Wadhwani, 1990, Transmission of volatility between stock markets,Review of Financial Studies 3, 5–33.

King, Mervyn, Enrique Sentana and Sushil Wadhwani, 1994, Volatility and the links betweennational stock markets, Econometrica 62, 901–934.

Liew, John M., 1995, Stock returns, inflation and the volatility of growth in the money sup-ply: Evidence from emerging markets, working paper, Graduate School of Business,University of Chicago.

Longin, F. and B. Solnik, 1994, Is the correlation in international equity returns constant:1960–1990?, Financial Analysts Journal.

Nelson, D. B., 1991, Conditional heteroskedasticity in asset returns: A new approach, Econo-metrica 59, 347–370.

Richardson, Matthew and Tom Smith, 1993, A test for multivariate normality in stock returns,Journal of Business 66, 295–321.

Roll, Richard, 1992, Industrial structure and the comparative behavior of international stockmarket indexes, Journal of Finance 47, 3–42.

Ross, Stephen A., 1989, Information and volatility: The no-arbitrage martingale approach totiming and resolution irrelevancy, Journal of Finance 44, 1–17.

Schwert, G. William, 1989a, Why does stock market volatility change over time? Journal ofFinance 44, 1115–1154.

Schwert, G. William, 1989b, Business cycles, financial crises and stock volatility, Carnegie-Rochester Conference Series on Public Policy 31, 83–126.

White, Halbert, 1982, Maximum likelihood estimation of misspecified models, Econometrica50, 1–26.

World Bank, 1993, Emerging stock markets factbook, International Finance Corporation,Washington, D.C.

Zakoian, J. M., 1990, Threshold heteroskedastic models, D. P. INSEE.

36

Appendix: Estimation and testing of univariate, segmented models

A.1 Specification tests

We present the standard battery of specification tests. However, we are most

interested in distinguishing the model’s overall performance. To this end, we

propose the following test which is inspired by the presentation in Nelson (1991).

Consider the standardized residuals, zt = εt/σt. Under the null hypothesis that

the model is correctly specified:

(a) E[zt] = 0

(b) E[z2t − 1] = 0

(c) E[ztzt−j ] = 0 j = 1, . . . , k

(d) E[z3t − sk] = 0

(e) E[z4t − ku] = 0

(f) E[(z2t − 1)(z2

t−j − 1)] = 0 j = 1, . . . , k

(A1)

where sk represents the skewness parameter and ku is the kurtosis. The correct

specification of the conditional mean is implicit in (A1c). The conditional variance

is in (A1f). In (A1a,b,d,e), the unconditional moments of z are compared to the

ones predicted by the model.

In the standard setup, model I, sk = 0 and ku = 3. For model II, the skewness

is also equal to zero, sk = 0, but the kurtosis is ku = 3(ν − 2)/(ν − 4). For the

SPARCH model, the skewness is:

sk = p(µ31 + 3σ2

1µ1) + (1 − p)(µ32 + 3σ2

2 µ2),

and the kurtosis:

ku = p(6µ21σ

21 + 3σ4

1 + µ41) + (1 − p)(6µ2

2σ22 + 3σ4

2 + µ42).

Notice that the SPARCH model collapses to model I (ku = 3, sk = 0), when

p = 1, µ1 = 0 and σ1 = 1.

37

Much like our normality tests, it is straightforward to use generalized method

of moments to conduct specification tests.14 The conditional mean specification is

tested by setting k = 6 and obtaining a χ2 statistic from (A1c). A similar test is

conducted on the conditional variance in (A1f). The distributional assumptions of

the model are tested by examining (A1a,b,d,e). This results in a χ2 statistic with

four degrees of freedom. It is also possible to jointly test all of the restrictions.

With k = 6, there are 16 degrees of freedom in the test statistic.

A.2 Baseline estimation

Table A1 presents the parameter estimates for the three models: normal, t-

distribution and SPARCH and Table A2 presents detailed diagnostics of the three

specifications. Given the results in Table 1, which shows very high unconditional

variance and sharp departures from normality, it is no surprise that these are

extremely difficult variance processes to fit.

In Table A4, we conduct diagnostics on both model residuals and scaled resid-

uals. In the first panel, we analyze the residuals. The Cumby-Huizinga (1992)

`-statistic is a test for serial correlation that is robust to heteroskedasticity and

corrects for the fact that the residuals are estimated. However, the test is only

strictly valid when the model is estimated using ordinary least squares or in-

strumental variables techniques. The Q2-test is the standard Box-Pierce test for

heteroskedasticity and tests for persistence in the squared residuals. Finally, we

report skewness and kurtosis of the residuals. In the second panel, we scale the

residuals by dividing them by the conditional standard deviation. The specifica-

tion tests detailed in section 3.2 are detailed in these panels.

Consider the analysis of the three models’ residuals reported in panel A of

Table A3. First, the residuals of all three models for most of the countries are

autocorrelated. The Cumby-Huizinga test shows significant serial correlation in

14 However, in contrast to the normality tests, the specification tests will bebased on moments from generated time series.

38

the residuals for 13 of 20 countries when the normal model is estimated; 10 of

19 for the SPARCH model (estimation failed to converge for Chile); and 14 of 20

countries for the model based on the t-distribution. This serial correlation occurs

despite the fact that the lagged returns appear in the conditional mean specifica-

tion. Indeed, the lagged return enters the specification more than one standard

error from zero in 13 of the 20 countries for the normal model (parameter δ2).

The residual serial correlation suggests that the specification of the conditional

mean could be incorrect.

The analysis of squared residuals suggests that there is significant het-

eroskedasticity in the data. This test, however, only measures the multiple correla-

tion of past squared residuals. Significant autoregressive conditional heteroskedas-

ticity is found in about half of the countries examined. There is little variation

across the different models.

Finally, the distributional characteristics of the residuals are examined. With

only a few exceptions, the distributional features of the residuals are similar across

the different models. The analysis indicates that the residuals depart from nor-

mality. For example, after estimating the model based on the normal distribution,

significant skewness is detected in 8 of 20 countries and kurtosis departs from the

value implied by a normal distribution in 9 countries. The significant kurtosis

in the residuals suggests that a model based on the t-distribution may be more

appropriate. The residual skewness motivates going to the SPARCH model which

allows for fairly general skewness and kurtosis.

Now consider the parameter estimates and the diagnostics based on stan-

dardized residuals. The first model presented is based on the normal distribution.

Given the evidence on the distribution of the returns and residuals, this model is

the most likely to be rejected. The diagnostic results show that 7 of the 20 coun-

tries show significant covariance of current and past standardized residuals (mean

test) and 9 countries have predictable squared standardized residuals (variance

test). When we test the null hypothesis that residuals have a zero mean, unit

standardized variance, zero skewness and kurtosis equal to three, the null is re-

jected at the 5% level for 14 of the 20 countries. When all of the standardized

39

residual tests are combined, the joint test reveals a rejection of the specification

for all 20 countries.

As mentioned above, the model based on the t-distribution attempts to ac-

commodate some of the kurtosis found in the residuals. One extra parameter, the

degrees of freedom in the student t-distribution, ν, is estimated. For the fourth

moment to be well defined, this parameter must be greater than four. However,

the estimation presented in Table A1 reveals that in six countries (Argentina,

Colombia, Greece, Nigeria, Pakistan, and Turkey), the degrees of freedom param-

eter is less than four.

The analysis of the standardized residuals in the second panel of Table A2

shows that the null hypothesis of no predictability of the standardized residuals is

rejected in 4 of the 14 countries where the model presented a degrees of freedom

parameter greater than four. The squared standardized residuals are predictable

in 7 of 14 countries. The moment test presents rejections for 10 of 14 countries.

The joint test suggests that the restrictions implied by the null hypothesis (stan-

dardized residuals follow a t distribution) are rejected for all countries.

The SPARCH model which allows for general nonnormalities fares slightly

better than the two fully parametric models. Underlying the SPARCH is a mix-

ture of normal distributions. As a result, three additional parameters are esti-

mated: the mean and variance of the second normal distribution and the mixing

parameter. In some of the countries (Colombia, Malaysia, Mexico, and Pakistan),

the mixing parameter is greater than 0.96. The model did not converge for Chile

and Portugal. In addition, Nigeria’s results are suspicious.

The null hypothesis of no predictability of the standardized residuals is re-

jected in only 6 of the 20 countries compared to 7 of 20 for the normal model.

The squared standardized residuals are predictable in 12 of 20 countries (9 of 20

for the normal model). The test that the standardized residuals’ moments match

the theoretical moments implied by mixture of distributions is rejected in 14 of

20 countries (compared to 14 of 20 for the normal model and 10 of 14 for the t-

distribution). Similar to the other models, the joint test reveals evidence against

the null hypothesis for almost all the countries’ specifications.

40

Figure A1 presents the fitted variances for the three models for each of the

countries. The thin solid line represents the GARCH model with the normal

distribution. The thin dotted line is the model with the t-distribution The thick

dotted line represents the conditional variances from the SPARCH model. The

fitted values show where each of the models fail. The normal model produces

unreasonably smooth fitted values in the Philippines and Turkey. The model based

on the t-distribution produces too smooth variances for Chile and the Philippines.

The SPARCH model fails for Chile, Indonesia and Nigeria.

Overall, the GARCH models have difficulty in fitting the highly volatile and

non-normal returns in the emerging equity markets. Even when non-normalities

are explicitly allowed for in the estimation (t-distribution and SPARCH), the

diagnostics reveal that the specifications are almost always rejected. We now

explore two different routes: building a multivariate framework to allow for cross-

country effects and incorporating asymmetries into the variance process. First,

we will explore the asymmetric GARCH specification.

A.3 Asymmetric GARCH estimation

Results for the asymmetric GARCH models are presented in Table A3. The

parameter γi allows for asymmetry. The test for asymmetry is whether this pa-

rameter is two standard errors from zero. In general, the sign and magnitude

of this parameter is not that sensitive to the distribution used in the GARCH

estimation. As such, we will concentrate our discussion on the normal model or

SPARCH model.

In 11 of 19 countries (no model converged for Turkey), the coefficient is neg-

ative indicating that volatility is lower in negative returns market. In 9 of these

11 countries, the parameter is significantly less than zero. In contrast, only 4

countries (Korea, Nigeria, Philippines, and Zimbabwe) show significantly positive

coefficients.

In general, the asymmetric GARCH provides an improvement in fit for most

41

of the countries in our sample. The fitted variances are presented in Figure A2.

However, it is surprising that in most of the countries, negative return innovations

appear to decrease variance. This is the opposite to what would be predicted based

on a leverage hypothesis.

42

COLGI9POU - R926 COU.GI9!OU - coutsou - v4Oqi 5

CO CO CO Q CO Q Q CO CO Q CO 0)I- - I- CO CO CO CO CO CO CO CO COCO 0)0 — CA C.) 4 1%) (0 COO) - CA

AOl PO - B926 AOl 9ftO - 4OqG J AOl - oqe 5

0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) CO 0) 0)V V V CO CO CO CO CO CO CO CO CO CO 0) 0) 0)V CO 0) 0 - CA C.) 4 IU CO I- (0 CO 0 - CA0

08

lucxi (wegu) lL1G&9!OU (A9U9UCG)

0) 0) CO 0) 0) 0) 0) CO 0) 0) 0) 0) CO 0) 0) 0)V V V CO CO CO CO CO CO CO CO CO (0 0) 0) 0)V CO CO 0 — CA .) 4. lU CO V CO CO 0 -. (A

B926 oqeIU6äLS!OU GS2flLG2

-I

MoLlq bLO - B° oqq bLO - 'I MoLl bLO - oqe 5

I- CO CO 0 - CA C.) 4. (0 (0 V CO 0) 0 CA

JJJL6G oqeeE9COL bLObOLfl0U

KOLG9E!dI1LG J

0

05

04

oe

08

05

0'8

jpLee joqeeC0LLGIS!OU M!fIJ OLj

0'R -

JJJLG6 oqeieMOL\OC9 ASLISUC6

05

0-p

0

0•5

04

.) :1J .,NS.t''• 4. •01

09

cOu.eiou - 9928 C0LLI9I!OU - oqe COLLOI9flCU - oqe

a, a, o a, a, a a a, a, 0) 0) 0) 0) 0)'- I- - a, a, a, a, a, a, a, a, a, a, 0)a, 0) 0 — (A 4. a, CO i.- 0) 0) 0 —

-02

0

0208

AOl bSPO - 9929 AOl - oqe j Aol l9i!O - oqei

IIJLGG joqeMOL ESCfOL bLObOLflOU

Ll'JGX!COE!ärlLe 5

a, a) a, a) a, 0) a, 0) a, Q) Q) a, Q) Q) a, a,I- I- I- a, a, a, a, a, a, a, a, a, a, 0) a, 0)I- a, a, 0 a. CA C) 4. a, CO I- a, 0) 0 — CA0

05

oe

JJJLGG AJOqG2 IIJL6G lJoqe2COLLGI9!OU M!fIJ MOL MOLIqrocsi ASLISUCG

LIUOaL9 (wesu) lu6&.sp.ou(A9u.9uce)

05

A.. A 4.. A.. S A.. A A A.. A — A — A A.. — a. — 4 4.. 4.. 4.. A.. A.. A A.. 4.. A.. S — a. a.0) a, 0) a, a, a, a, a, a, a, a, a, a, a, a a, a, a, a, a, a, a, a, a, a, a, a, a, a, a,i- i- i— a, a, a, a, a, a, a, a, a, a, a, a, a, I- I- a, a, a, a, a, a, a, a, a, a, a, a, a,I- a, Q 0 A.. (A C.) 4 (C) CO I- 0) a, 0 a. CA I.. a, a, 0 a. CA C.) 4 (U (0 0) a, 0 - CA0

[M0LIq bLO - 9928 MOLI bLO- I4OqGl J M° bLO- oq6

oe

0

05

04

0•8

oe

09

B926 ijoqeIUGdL9!OU 692flL62

B92

eCou

eIaf

lou.

cqe(

JCO

a6te

nouv

J

CO

LLG

Spo

u

M!P

MO

L

lUG

äLS

fIOU

JGS

2flL

e2

,,

k- - —

MO

Llq\

ocs

A9L

I9U

cG

- 92o

MO

LI

bLO

-

joqe

JMO

LJq

bLo-

yoqe

j

Q) Q 0)

0)

0)

0)

0)

0)

0)

0)

0)

0)

0)

L- 0)

0) 0)0

— Cl

C.)

i) CO

CO

- —

—

MO

LIq

E9C

OL

bLO

bOL!

OU

JJJs

isuq

Efl

L6

3

-o.Q

0

05

JjIL

GG

frsj

oqej

2

r -.1 (w

Gsu

)

LUG

th$I

Ou

(L!8

uco)

0)

0)

0)

0) 0)

0)

0) CO

CO

CO

CO

CO

CO

CO

CO

CO

0 —

Cl Ct)

CO

I-

CO

0) 0

o.s

09

B92

6

'goq

e

pionäp DGCGWPGL .i eas

AOI9!I!fl BGOL6 r!pe1I!f!ou

0

oo.1

005003o.o'f

002ooe

008008

0•.1

o02 ooe oo.s 008 008 0.1

J EWGLä!Uä 9LKGl2

CSb!f9I LAJSLK6I r!peLsI!sf!oua suq AoIs{!I!1IE!anL6 4

0 00.1 003 003 0Q't

AOI!I!4A QqIGL

4,-

62 O JG Ufl }3ThOpGBI2 }1 flJG CO6CiGI1j2 6LO 12 L6bo46qO 26MIJG22 &uq 6XC622 JU1140212 L6 1OJUI IA G2IUJ6q 9joU ii;p pG W6IJ svq Ag1XucG jps b-/fn6 LOW JqC6UJGL 1883 2IJqLq GLLOL2 u bz.6ucpGa uq b-AYln€2 JJJ pLcJc62 JjJ6 CO6JCIGU2VII WOIUpJA L6flLU2 tLOW I LU1IOUl E!UIICG C0Lb0L9-4!oU uq ccn;q iu qoj J6 2swbjG €uq u D-

(ooe) (1'38o) [o132]iLX1ppM6 .1Q01 3408 0138 os 4002(oeas) (1002) [oooslAGU6fl61 8201 3318 410 0391 0101 3082 131'fO

(0321) (ot3) [<oooilJ/7LGA 810I 3114 13.14 0333 F0'12 0858 '13010(0202) (1325) [ooo3]

5298 011'1 -ooaes 339.1 11340(0.393) (0928) [03441

9201 3403 2595 001'1 0344 0028 3130(02.12) (3339) [oosolhOiT91 8905 3421 1885 0591 1218 2018 1121(o'so) (oo9) [o0301

ptpbbwG2 8201 4231 3188 W332 0411 5133 1011(0933) (is) [ooo31

820! 5123 5308 0320 3083 8292 11120(roes) (323.1) Ioooel,4I6II 220T 310 3931 0082 1111 11530 10320(0444) (1200) [00111

AGXICO .1901 3038 4442 0348 -0839 3984 9302(o218) (14.2) [03121

9201 1394 5951 0025 -0e30 5149(0-301) (1590) [<oooi]KOL6 1901 3152 3559 -0001 0881 1091 3.108

(osis) (0101) [oosolOLU 1001 10.12 1124 0000 0425 0819.1 .1848

(oses) (044.1) Io.uoluqouG2i 8001 -1333 3310 0392 0130 -03180 02350(0388) (0981) [ooo1.1901 5030 3.119 0018 0993 5343 1823(0442) (5114) [<oooilQL66C6 .1901 143. 3913 0133 1833 .1331 1148(0332) (3038) [<oooi]C0I0U1P! 8201 4394 3188 0188 4020 3943(osle) (5.131) [oo.1o1

Cl'!!6 101 3991 3049 0190 0831 3159 2333(osis) (0421) [ooos)

BL!J 1901 3310 9000 0038 0210 1018 1318(ottl) (3288) [<oool]V617!11 1901 918.1 10310 0024 1891 .1332

tCflLO2I2

Cofl1LX 391f UJ6U aq qGA )1 2JC6MU622 6XC622

yuunpq C0GWc!611 O

D!2LPJmou1 CpL9'CcGLJ2iC O GWGLJ11 Gd1111A m9LJCG LGu1LU2

LPL6 I

112 qoJJr2 jpG wbj 6uq2 ' iaasVI! LJJOUçJJJA L6fTLIJ2 tLOUI 10 LiJJ0U! E!U11C6 C0Lb0L!011 iiq CJcn96q Ju

Twp9pM6 2bVIcCII A62

A6U6t161 MO

8hVI{CH 4O

pIjuq 2bVB'CHJ,91M1J OLUJ A62

1,4jgT L40LWI

bPrI!bb1062 2bVI{CH2bVB'CH

W6LW OLUJJ MO

6X!CO 4OtWJ A62

OLG 2BV!cCH

'JoLqu A62

OLU1J A62

I0q! 2BVffCHUL66C6 2bVICH A62

COIOWPI 2bVCH A62

CPJ6 W0LWI A62* B!J 2bVB'CH A62

yLG1JU1 2bVICH

C000f LA woqG! V2AWm6LJC

LIUCG

WGLIC L2bOIJ2G2 O U69'IAG 91J bOii6 LG4TLIJ OCJ29'1J qqipouj GLW in JJ6 A9'L IJCG 6dfl9'OU JqCJJ 9'JJOE2 tOL w-AOJA2 9' W!X1TLG O UOLIJJ9' q2p11OU2 JjJ6 9'2?UJWGLC uJoqGJj4G L62xqn9'j: llOflB9' l qLIpIOtJ "q 2IVJSCH LP6 bVI{CH "-Aoj9'J? o. JJJLGG [GLGIJ qr2J4psTou9f 9'211mbl!OU2 9'L6 OU

a9'IJq9'LqJGq LG2iq1T9'J otmq p? qiqiJ LGIqr19'2 c p? ipf}JG couqou9'J UJG9'IJ JJ6 couqion A9I9'IJCG 9nq 916 pG

L6bL626u2 9' O JOC9' !UL0Lm9'40U A9LJ9'pJ6 4JJ9' 9'UGCMGLG L LGbL62GIJ42 cJJG L6J1LU oU u9'4iou91 6drn uqGx O COHU-

=s + + ET=Qsc

L:' =x 2 +

1116 (ojjomiiJ IJJoqGJ i

flIJ!A9'L!9'6 26JJJGUl6 A9'Li9'IJC6 moqGJ 6I6C4OU

19'PIG

(oe) (raa) [o83) [<0001] [<o'ooi] [0002] [0532]L4fGLçq 032 040 0002 313U 3342k 9033 804 241

(sso) (o2) [oeo3] loon) loon) [oiio) [0180) [o!!9] [oaee]JAGXLCO J93 1258 O5O 11320 13013 1811 O'33 331 T'40

(oe) (osel) [0542] [<ooo] koooi] [oe1 - [0134) [028110334 1143 1320 iW23 J853 0313 1001

(orse) (ois) [0011] [<0001) [<oool) [oooj) Iois] (o4e8]JOLG9 0829 038 e428 >1000 >1000 8834 -

803 e43(0308) (ou2) [ooeo] kooorJ l<ooor] [oor] [oJo31 [0188]

oLqu -0309 0318 3303 10210 15102 2108 .003 820 2014.

JuqouGi(49a) (0118) [o38] [oN8] [<ooo) [<oooi) [<oooi) [0118) [0208)4131 o314 Goal 3022 40844 33310 1012 49!

(o104) (0342) [0020) [<ooo) [<0001] [ooo8) [oor) [oeo4]0201 1O!3 3048 >1000 >J000 0084 1T891C 424

(Jo42) (o.ie) [ooae] [<oooi] [czoooJ) [<oooi) [<oooi) [oa3] lois]C0I0mP 3380 0230 3.13.1 34!88 38482 322'L1 40380 313 000

(on.i) (oioe) boos] kaooi) [<oooi) [o13) [oae] [oor) [03101GP!1G -0204 00!0 0338 >1000 >1000 0132 3802 1238 13

(roae) (o33) [ooo) [oosa] [0034] [o.iie] [ooeo) [oaa] [o2e)BL!1 0181 0328 0148 0244 10403 0081 8048 181 4i2(0024) (0140) [oooe) [<oooi] [<oooi] [0223) [00141 [oss] bios]0388 W000 >1000 >1000 0321 13241 120 053

COUIJf LA

=mi

=q

==

== V =

nc G22

= UIG9IJ AgL1gIJC€

}f6iql1J qix1oeice

12 2CJG btrm66L uq L6bL626u2 1mboLncG o lP6 oLJq 2}JOCJ Mplcp i o L62LJC6q o 9Jj !u JJ6 IoT] LITG

= + (IT)

12 IIUCIOU O JOCifI 1IJtOLW!OD uq 12 L62L1C6q O p6 [oT] WIJG M6i1O JJ6 2JJOCJ2 O JJ6 IOCS1 L6411LU2 O p6 4A6U p? O}J oLjq uq jocj 2JJoc2:R6L6 X' LGbLG2GIJç2 IJJ6 JOCJ !II(OLmOU AqpJ62: cOuu J6 J6q 6dff LG1TLU ij.r Jf6 6XCJWIJ6 L916 C}WU6 uq IP6 jrq q!!q"q ?6Jq

= '2'X1 + (T — —')(x—1) + (a)

fl2 P!II) 'iq JGq CPflJ6 IIJ lJJ6 3O-q EflLOOJJ L96' 1116 lJ16 W6YIJ L6I1LU I1 COffIJL? 2 piucioxi o po MOLJ 9uq joc JuoLmiou:£96 lP6 J6q q6[9nJ 2bL6q (ooq?a B Irnun v pouq ?6Jq2) p€ jq CJJD6 ifi JJG 6LW 2LnC;1uG 2bL6q (ri J-?GL pouq 16jq miua2 :-uJOUl1J

L6bL626U2 26 O wOLJq !IJtOLUJ9f IOU ALWJ62 liplCp IUCJflqG: couu JU6 ioijq mLJ6 qIAiqGuq ?4Gjq !' 6XC622 O JJ6 O-qAEfIL00JJUL

= Cm + + \mçfl'—lJ= x—' +

E2!m!OIJ COU2!22 O MO 2l6b2 E!L2 lJJ6 OLJq AUL1UC6 q?JJUh1C2 916 u6u p?:

OLJ t9'COL moq6j: b9191JJ6GL2 uq qiJo2çic2

19P16

(b9unuinoD) t 9IdST

Djionib !ub!9R uiiv ns9m

3 = 0 = =

i 2J £3 =

=

a189i bIsW 3 =

£3 i inuo3 io.a

[e1.o] [e.o] - TM.0 [100.0>]

0001< [100.0>]

0001< [100.0>]

OQO.M [100.0] (T8.0)

eoa.o (I.0) aail&

18.C [en.0] [.o] - 0001<

[100.0>] 0001<

[100.0>] 0001<

[100.0>] 0001<

() 0001<

0 niqqi1id9

e.ci [ac0.0]

ao.i [8aa.o] - '- 0001<

[100.0>] 0001<

[100.0>] 0001<

[100.0>] (0e1.o) oooi<

() !utoq

l0.1 [va.o]

81. [v14.o] - [100.0>]

0001< [100.0>]

0001< [100.0>] 8Q.11

[100.0>] 10 (8a.o) (Ida)

nswjT

8T.d [Tor.oJ

C.V [tr.0] Q'T [100.0]

e8.1 [oc0.o]

C2.! [100.0>]

ei.a [c0l.0]

118.d [r0.o] oea.o

(eIC.o) 8t bnIisdT

81.01 [aol.o]

0 [ooe.o]

v8c.a1 [doo.o]

aazi [a.oJ 0001< [100.0>]

0001< [100.0>]

0001< [100.0>]

aea.i- (1C0.o)

1.8- (H0.0)

'IiuT o.a [E.o] [awo]

leV.8 [vao.o] [oeo.oJ

0001< [100.0>]

0001< [100.0>]

0001< [100.0>]

8.eec 0 t08.E (8C0.0)

£1us9n9V

8e. [018.0]

C8.8 [e1.o]

i.ec [100.0>]

881.od [100.0>]

0001< [100.0>]

0001< [100.0>]

81.d [100.0>]

a.o (8a1.0)

8.8- (81.o)

9wdsdmiS

.eei -x9dm9DsQ iii ab09 91qms if T .exItob 2.U ni b9i.fu1s3 bns noiiioqioD nni'I IEnoistrisnI moil afnui9l 'Ixftnom HA .l9bom 9di O 'fl 9i6m11a9 O £isb w91 ooi bsd ,iinobnI9

!snfl di Jjjfj nsm Iima9D9n ion 29ob aidT u nixJi snobi asw noiismiia di bn noiiSmii9 tsniiio 9th ni nibnid asw I = \ + ) ink iino ,di ,,hinuo 9di 1o .1 < R + o bth9islsfb e1

.100000.0 nsrli 191!sm2 eimurniiqo" rf I I ainibi di iud ivno 'fIu1 03 bsIisl 9lub9Dolq noiimiia9 9dT .stuiiqo 1ol 309I9lIjb 'nm 91911 T .ldo1 bnuol &vd ion ym noiisrniia b9vc19d-11i

.b9ffz9j 9c1 bluoD 9n19vno) oW

4..

L4o COIIA€LI1CG conjq pc LcpqCrJLJ) -p9'q uviou ur Uo J1AG onuq opj JGLG wx wv? qijr.cir 1ocg1 obiwgJjJG G2pIIWoxJ bLocGqm.G wqq 1TJJ cou,Ic pni pG 2Lqcuçt gyc ob;nunwLc 2wwci. pI7 000000r

!r LP qo€2 IJO IJGC€2WLIJ IflGVIJ 1JJW rw bvLwcçcLa + \ > r9JOL JJ2G COff LTG2 JJG COIJL9W +\ = J M2 puqu LU CJLG OULYWJ G2RIJWç1OIJ wuq fIG G1TOUM LGqOIJG LGJ9X1U

prqouGLi JJq OO GM q O GTWG 9J) O fIGuroqcpVII liIOIJfYJ) LGrLLU2 LLOIJJ pJUiOUJ EJUUCG irq ii "n qou L1sc wbJG G11q2 1" DGCGWPGL J3

0039 003. - 0e52 o1e2 o'13e -

0012 005J 0032 o'aa2 0000 ooea O0 0122.LI1L1) 0O2 - 0030 083 oaa - -o.IJe

oo oia ojie 08a 0122 034croe 0o2 0033 o8i 0.O o12e 01'U 0I3

OLS1J 0200 0313 0000 032 o4e3

MnJ1bbTu oreo ooi o000 oooo 020't oooo oii oe boo oi.o or oa 02110003 0002 - oeoi 0133 0033 0031 -

yJGXICO ooei 0J3 ojga oe32 032 ora o•a0a2 03Q 032. 0'981 012 08 0428 012

j(OLG 003 cYO4O 0032 088& F000 O1B 0301 O1OLq1I oog ooe3 ooei oaa oeoe oree 03'H

Iqou'005a ooia o0i O33. 0B08 0122 0130 oioaooe o•JJ'1 0113 F000 0233 o318 oe001 0050 0032 o3eo 010J oisi o•fl.s

CPUS 0001 0001 0001 oaea 1000 0031 oo23BL91I 000w 0004 0004 oae 08a1 oos 0OeJ o0e2

0005 0003 0003 082 0a4 0•04 0020 0021J/lfl 2mbJG },OU-CLLUJJ bo-flpGLWI

qITS O MOLJ COL2fGJJ bLoboL1o1J Ot MTLI9IICG

IffCW1I AWLWUCG

LIOII LOW

JJTfl 2WUJbJG 0-cLJi b0-1TP5LJ!ATfJ MOLJ

CJ!° k"

= (5)

JJJG bLoboLfiou Oj ILJ9i1CG ccorni€q OL p) MOLJ ICOL2 2

Q. '*" ()

jç 9j20 jJJ2 Ill [oT1 LflJG JJJG MOLJ IJJLJGç COLLG1JOiJ ill OJJL woqj !2 iAGU[oar] —' L6bLG2uf2 iuJboLucG 0 MOLJq IU(OLWf IOU III 4}G COfIUçL?2 C0IJq!çIOIJJ ALI9IJCG GdfrfJou

I LGbLG2GUç2 jmboI.ucG o ojq JUo1WçJoJJ !U cIJG conul?2 couqicrouj mG911 Gd1TJOIJ i; I.1I2 J

COLLGJT0I. I GL9çiou iiq fIG imboL;9ucG o oqq icoe

FPJ6 f

y WOu}JA LGf1LU2 LOUJ lu wiou IuuC CoLboLwou uq cjcn€q qo;9I2 jp nub16 6uq2 ! D6C6WP6L 1803

3f1WWL?k epapca OL CLO22-e6Cl1OUJ IJJ?12

JPJ6 2

IJJM6A6U606I

LnL16?

1I'!1"qL9!M9U

1or-u1IbPlPbbiDG2

4J6L1jGX!CO

OL6oqu

Iuqou62rJUq!9

CL66C6

oompwcp!16

VL6U!U9

ias81

eo

183

84

U9'1

8494

94185

31

103

ias84

185

ias

134

94

3e

13Q

84

'U91

84

84

148

81leoI 234

'34ieo94

leo

ieo

011014011O•O!

010011013000ooeOle0100•08

00!0•3!0030•08

015o.J5018023

0100110• J I

0•11

0020080090100•0.1

00101100!00201200000!008008o 1i038

100138lea14123Q55 •3

33-I210180SF'23!10!12304833812230!52!31333e

03805303803301803303301!01!0-Jo030018020018030042010033OS!034

3313e 141024 1

00233!38030313•3

231oie34.420348!2332! I

3!0318

0200210420280830!102!034020038I 380001130220J202302302101!012

002008o'oe0000480_iT

0• J J

001003002oes0130300080•03

o-oe

002031003003

00040_Olo

0018001303330_Oil0031000800010024001100!00_Ols

082000!000100002000!00320031

005000810019001400 150021003300100102002800 15

001500130002001000330015003]0008

00148

JIGjO9J

JU LGL6 011

OP I1q

GM9TOU

2Uq9Lq P20IITIG

G9U CL022-26COUJ

LI1J2

L4IIWpGL oj,9COL

0UCGU;L9TiOU

L1UCL6qJl

C0flU.10Db

J,Lq6\

0Db

vi.Jc

JJILUOA6L AOJ9!Jr6XC9UG

EOLGIU

91dT

91dnsv I.GffOj 39-oiD 9ILt lo xhJm floijs!9TIOD

X'1 niuT qiM mI+xa $100 zrnoD oO UAM veb .biE IoV

11.0 10.0 1.O- 8C.0- O.0- .0- 10.0 81.0 I.0 00.1 '.ti1i&IoV

Q0.0 01.0 0.0- eo.o- 10.0 0.0- o.o e.o 00.1 noiithveb bebn ao.o ei.o 0.0- 1.0- 0.0- 0.0- 10.0 00.1 noiiiveb euIoad nE9M

10.0- .0 0t.0 0.0 ft.0 8.0- 00.1 eeinsqmoz lo iedniull 11.0- 11.0- sa.0 1C.0 0.0- 00.1 1oiD1 flOiiIifl9flOD 1'.0- i.0 e.o t.0 00.1 nit&i iibei 'iiniioD VC.0- l'0.0 .0 00.1 UO\eioqmI+ehoqx .0- p0.0 00.1 9QD\noii1iiqD 19*LSM

p0.0- 00.1 ivoniuT 00.1 'ii1i.sIov enrbxe nieioI

ci veb .bi .QOI ledmeDeG ni ebne stqrase edT .ei1lob EU ni b9M1uD1 oa noisioqioD eDncni'I Isnoi3nietnI moiI eniuiei 'h{1nom hA boa 1ubivibni eihi ho nogjiveb eiuloede nem elf i ej UAM ,xsbni einuo ibse ni arnuiei boie Iubivibni eu I ho noiisiveb bibnsic edi

mI-f-xa nisi iibeiz '1Jnuo3 ei beiD 1oiD fbi iineDno ieaas exhi ai znioD xbni O1I 9fLt ni eeinsqmoD lo iedmuu edi ci oD* anIuiisi ieAmm 'd bebivib nibii lo uthv) ievoniui ci niuT ao d bebivib noiissi13iqsz iehcm ci qm ,qcio 'd bebivib choqmi+ciioqxe i

.C9BILBIb eiei siiibxe aie 991111 euoiveiq I1J lo iiIiis1ov erhi ci X1 bn (fioiiiI6iqD

(6L Jip6Li9Tiou/OJr!pcA I0MGL

6L J!p6L1i9iOU/OJCiiA jOM6L

V'WD

q qGA

[oso2]108

(osso]142

oqGJ [b-ijn€]3

U (os.ii)080!

(351e).1048

(oo2e)-018! (osii)-O4'tQ

(00014)ooo

(ooot)-0032 (os.i)OQ04

(0203)ao

(0082)0084

(oo83)0342

(0110)0338

(0008)05.18 03208

(0083)0213

(isse)2Q21

(0o2e)-0311

(0515)-0484

(00014)00099

(0083)-0022

(0308)0.18!

(o200)8114

(0082)0109

(0084)0348 (oiio)0333

(0008)03.13 03449

EIL6C O Jip6LJi9riOUe

6GCl2 tq GA q qsl 4VD VD *C C11c CCff EX+P cb EXO. J!P6LJ I!PGL9I PPLJ JrpGL9J 'EXq B60L6- ELG- DflL!U b°2

A

A

A

A

U

U

(002!)0NQ

(0088)0333

(J03e)18Q0

(J1.1e)3050

(0183)0454

(o.in)0431

(0144)0384

(03.13)0Q35

(02.10)508!

(1231)4845

(1859)418Q

(soei)3382

(oo2o)-0035(0038)-0012(0020)-0038(0020)-00e0(0048)-0158(0048)-0132

(osso)-1448(osie)-1214(ossa)-1223(ossa)-128Q(osoa)-0382(osio)-020!

(00018)-00043

(woo!!)-00021(00018)-00023(ooo18)-00025(00013)00083

(00013)00084

(0503)-04Q3

(ossi)-0200(0351)-0238(0004)-0840(0083)-00Q8

(o18o)-0058

(0182)0130

(0183)0348

(0333)0080

(osoi)104Q -

(02.10)3252

(0203)331.1

(0248)33e1

(0248)31Q9

(0488)8081

(0484)81Q2

02882

0QOOQ

0Q00!

02003

03414

0332!

(0.131)130!

(0.113)0808

(0.133)0095

(o.143)1'3!4

(o.1ea)51Q3

(0814)3Q30

V flU!AU;G G2iWiOU o A9LI9UCG woq62—pIG uqiq 6LLOL

6U6C2 qc q qGA !VD VD CCff EX+Pll cb I

j1x6q

EXbJU!U 4G CLO22-26crOU O A9ITUC6

19P16

4- 4,

U

(woei)U 0341

(0112) (1802) (0043) (0188) (oooi) (00!!) (0180) (0414) (ooe4) (oo!o) (0083) (oo!e)o8e2 !3!4 0032 -0432 00044 -O4!3 -OQ38 822 -0144 0130 -0098 -003!

(1083) (oo') (osoo) (oooii) (00!!) (ojes) (0411) (ooe2) (Do!!) (0084) (oo!e)2884 0033 -O4i2 00042 -0210 -0480 8!0J -0130 0133 -0043 -0040

E6C O J!p6Ljt9l!OIJ2

66C q6A ;q qGA !'IVD V!VD ccLc ycsb LX0 JIGL !!PGL9I 1!P6L91 I!P1EPc1 BGL0LG- bLG-

A

A

A

A

U

U

(0040)00824

(0028)038!

(o!2i)1181

(0888)3308

(oai)03e4(ooee)0103

(oo!e)0321

(oiro)0!e3

(s3o)3120(1028)-0338(1238)3888

(1e24)22e0

(oo4o)0138

(0031)0033

(0041)0133

(0040)0118

(003!)0048

(003!)0033

(o18!)-1380(0102)-1q25(0503)-1483(o3o3)-14!Q(0184)-0'39e(0182)0438

(oooI4)-00002(00014)-00012(00014)-00008(00014)-0008

(oooio)0002!

(o'ooio)00038

(o133)-0313(o14e)-0124(o14e)-0183(oo!o)-0481(00!!)-023

(0-138)-028!

(0148)-02e1(o13e)-04!Q(o12e)-0388(0143)-038!

(oi)3822(oa!3)

3293(0380)

3'!88(o3!3)seio

(o411)8283

(040!)8e43

B flUISLIG !'''°" OIALUC€ 1UOq62—L0flb-MI pGr6LO2JCGq&2CtA

2q VD NVD C0C CCIf EX+Im cb EXO.

ix6q

1?PI' ! (couiun6q)

91GI l!P6r!c1oUA0191!I1 10M€L

jGL JipGLJIOU/OCijIfA JOMGL

VFID

q q€

[<oooi]11!!

[<oooiij34Q

yioq6j [b-Ijn6

915GLA0J!J f0MGL

j5GL Jip6LIi1iOUA°L1'1! fOMGL

VIVD

2fq q

[ooo]:34

[00051asa

qoqj [b-/n6)j3

(oosi) (o45e) (0028) (ossa) (ooora) (oisi)-

(oil?) (o2es) (03e4) (ose?) (osee) (ose)-0002 -0OQS 0034 -0041 00013 -0800 0181 2O.A 0381 0308 0231 O34i

(ooI4) (0520) (oo2a) (osse) (ooora) (0132) (0109) (oess) (os.i) (os) (os) (0380)U 0003 0034 0004 00i 00010 -0e22 013 F35 0343 022! 0300

EUGC °L i!P6I.9I!1!0U2

GG2 ;q 6A q qGA VVD VWD cclc F2+I" ycb EX'. J!P6L91 I!PGL9J !!PGL9I PP6L91

1X6 B6OL6- bLG- DnL!U

A

A

U

Ii

(oars)0003

(0014)0001

(0332)00!

(0540)0011

(oo34)-0008(ooso)-0003(0033)-0010

(oosi)-0000

(0303)0024

(o381)03e3

(03!!)ooes

(0388)-010!

(0021)0023

(ooii)-003!(0023)0011

(0023)0010

(002!)-0002(002!)-0009

(osia)-0120(010?)-0008(osie)-0081(0318)-0O!0(0333)-0OIQ

(osss)00!4

(ooosj)-ooooe(00018)-0000!(oooso)-00004(ooos)-00008(00010)00012

(oooia)00013

(our)0340(orao)03!0

(0184)0328

(olse)-0922(0131)-0!0

(oiii)0310

(olo!)01Q3

(0102)0123

(oroa)0°1!Q

(0103)oieo

(o23e)in(0483)0883

(o2se)1138

(0282)1383

(o2!3)4er8

(oe33)3882

C flU!A9LJG G2ç!UJf IOU O AL1UC6 woqGJ2—3GLIJ COLLGJ9IOU Uq LOflb-M1G p66Lo2Jc6q2çiciA

6a6C12 fq 'JGA q qGA 11VD C°" (}ç E+I" I

X6

L9PI6 ! (cou;rnn6q)

(buninoD) V eIdT

-91o9H b9xi Isl9diI 1819d11 I19di1 Lsl9di! XI qM rnl+xa $IDD znioD oO *QAM QAM tv9b .b2 v9b .b32 abs1I

13i3iibs1ao19i9ñ saiw-quoi bn noi!no !sii :floi6miie9 9Da6IISV 1o3D1 blioW CI

1O 1.O £81.0 CI0 1.0 TV.0- 1100.0- 0.0- £I0.0- 8E.0 £10.0 a (o1.o) (ec.0) (8C.0) (VE$.O) (e.0) (8!.0) (101.0) (8100.0) (10) (810.o) (re.0) (alO.0)

18C.0 211.0 EC1.0 Q14.0 1I.t 1.0 I a.O- 2100.0- V0.0- VC0.0- V1C.0 £00.0 a (O2.O) (1o2.0) (E0$.0) (202.0) (8.0) (021.0) j(ve0.0) (8100.0) (1V1.0) (T0.o) (8M.o) (CC0.0)

5 [9uL6V-q] I9bOM

ôI.1 [V22.o]

I1 [VV$.0]

.vb .bi2

UAM

l3woI iiIiiIoV noiiiLsidiI 19i16

l3woJ vIiIiiEIoV nozii1siediI 19i18

-iol iq9)1iai insisBib £ el 91Sdi iefLt 2flEMU eJD119 bxiI .2001 i9dm5D9U fit bfi9 9Iqm.Ge iIT .eiIIob .2.U ni b9IuDIs bus noi$sJoqloD eDflSfiI'I InoiMuiinI moil afliIJi9l 'IdiaomIIA moil noiSsiveb siLt d bsiIqiIum noitstvb bisbnie IsnoLbs-aso1D siLt si .vsb .bf2 xsbni eiauo zbss iii SnIU$9i boe Isubivibni ed3 lb noiistvob bsbnsie sd3 ci veb .b38 .i3nuo d3s9

d bsilqislum iioLtaivcb siuloeds usem Jmnoibsa-uoiD sd3 ci QAM swiuisi boia Lsubivibni cdi lb noiisiv,b e3ulo.ds usem ciii ci ClAM noiiacila$iqs icihem ,ei,vs Lcnoibee-eaoi cdi uiisi ithei 'fliffuo, ci bczD iobcl fbi liac3ao) icaas Siii Si no) xsbui DIl ciii iii ecinsqmo3 lb idmuii ciii ci oDi ,noiiscitciiqs3 iehm civs Iaaoibec—aeo1 ciii inoil noiisiv,b cdi

i1iisIov ciii ci bXI bits (noiisciIsiiqs iciLisni d bebivib nibs-it lb culsv) i,voniui ci triuT IUD d b9bivib noiisciIsiiqs ickism ci qsm quo d b9bivib eiioqmi+ahoqxe ci mI+x .eastb 9J51 9fis1bX9 Sisc'f di euoiveIq 9i1 i lb

.mobccil lb 99i9b no asd DiiSiisie 5 cdT .aoiisxilsi9dil-ieoq bus aorisiIs19duI-9Iq iol aioisDibni ,ili no b9iDubnoD ci in9isflib ci iiIiis1ov icrlicdw lb iai bleW 9dT

I.

lL0I11 BGJ6I4 (1a82) ________________________A

Ln4C6A 1888

Jj3iwUq DGC6WP6L 1088

i''' iaorbOLfl1 DGCGWPGL

bP!ITbb!U62 I4O/GWp6L 1001

bJC!2U EP'' 1001

L40U6

GXICO L4O/6mPGL 1880

D6CWP6L

uatA raaloLqgu DGC6WPGL

IqUG aGbrsuJpGL iooo

psq lITUG TOOS

0L66C6 DGCGWP6

C00U3P! 1881

CPIIG VbL!J 1080

BL11 y\AVIUIJ D6C6111p6L

COnULA D6 DG2CL!bOU

Cb!J ILJG HPGLJ!!OU

LPJ6

e 9IdT

I9bom nthw noi -aoi ñi o auois3i1qmi DifflOflO3

'oX'i qM mI+xa 1YD noO o QAM v9b .bJ iiIis1oV

noiiudiiieib 1nOib9a-aeo1D .A

C8040.0

8120.0

1800.0

Q8eto.o

C80E.O

8I.0

1.

1.t4

ICOE.0 1

£

00.0

81E10.0

01T0.0

2880.0

IQ11.0 9[on]

80.0 D[g]

t1

nib9M

ieio.o- 800.0 £181.0 o.ai ôQ'VO.O- 8 nr1

mu2 lsl9diJ qsM mI+x 2DD DnoD oD* UAM .veb .b I9boM

ioiil knoib9a-aeoi3 lo iqrni Dimono3 .8 - rae.- cIo.- 8- @8C.8-

TC.'V-

V8.ô-

0.1- tt.i- e0.E- V0t.C-

T8S- 080.- 180.1- 10.1-

£8.C- t.E- 101t- £8E.C-

iea.i- E8Q.1-

1.1- ICQ.1-

Tôt.O

8.0- 8V.0- £01.0

11.0 80.0 001.0

C.C- ii- 88.1- Q1".1-

C1- ee.- t8.$-

CQÔ.

TV.1 1'E.1 0C.0 8ec.0 cc.o- TÔC.0-

ô8T.0

'V.0 ITT.0

0ô0 80.O- 'vao.o

co.o 21.O

018.1-

81'V.l-

ei.o

C0.0 eo.o a0.0- CE.0-

0E.0

14E.0

00.o-

100.0-

'Vll.O

81.0

100.0

100.O

9iif1W-JO 9JiI{WJO

i9H-1D i9H-JD

D\i9H-EJ3 O\i9H-JD

EJO\i9H-1ob1 bhoW 2JD\i9H-IoizI bhoW

.mdiriso1 thiuiVI .am19 9D19 bei1EunnA ei I9bom noth91 i L&noibea-eeoi luo mon iihis1ov b9i1unns no )99 Isiisq 9ilT

—x—=

:9jIuDL6D 9W 'iiIiiIov b9i3ft erti no 1oa91e1 9113 Lii enath lo 3D9TI9 edi eiuem oT .iaeieini lo 1o1e1 edi i L 91911W

x ,x x oxOOI xT'

oh beisIuzkD bas nohiioqioD enni' 1noiitn,inI moñ enxuiei Idinom no beasd ai noi3inhie edT .in9k3fflco noiacisi cdi ai 91911W

jo iisq moiiod cdi ni beineaciq elebom noji6j1619d1I cdi no cis ainsbfflco3 flOiE919$I .QeI icdmeeU iii ebne elqnise edT .aiellob ..U ciu!oad nssrn 9113 j UAM xcbnl avi3nuoz ibec ni aniuicl ,boia Leubivibni ciii lo noi3iveb bibnsie cdi 21 veb .bi .' 9ldT jo Cl—A e1sn1

21 beiD iob noi31incDno ie2aE ciii ai noD xebni YII cdi iii a9ia6qmo jo iedmun ciii 21 oD eniuici boia Isubivibni erli o noiisivb to iiIi3slov cdi 2I oXI 1QD d bebivib noi3si1siiqsz ic*xsm ai qsm ,dIQD d bebivib aiioqmi+eiioqxc 21 snl+xa niisi jibe-ID 'IinuoD

noiisiLs19di1-i2oq bits nozisxIsi9di1-exq ciii neewied cDrtclcThb cdi Einceelqei LeicdiJ bns 29BILSIb cii ensbxe 'a1a9 cciii auoivclq cdi .1dsjisv ioisibni

IAJJGLE 1 ia f6 JIJjOLIJflfiO1J 2G IfAipJpJ6 0 1UA62fOL2 9 —J M6 L6b014 LOPII2I 29iJq9iq 6LL0L 2 2fl62Gq p? MP!G (ia)

I (t3o.3) ib (j —TATO(1GT TIT 1111

JiJ) &b ty0q6j II: jy'1 "(o 1)j(JO6J I: i'' 'qacLpnçiouJ g2nwboI1e smq:

W6flJ i JJG couq!4ou9-J iLfIJC6 niq ' 9L6 2çuq9iqJ6q I.621qn9ja ouuGq p?k qzAiqiu L62qn9J2 pA JJG AOJ!J!A O JJJLGG 1J6LGUfL L6bL6(I1f JG L(4IILU OU 4}JG IJ9JoUJ Gd1Ji4A piqx 01 conuA x' LGbLG2Gup 1 01 J0C9'j iHt0LIJJ0u A911pJG }1 J1GC f JIG coIJqTfioIJcJ

=+ + '—'=Q

LS. =x:'—'s +JjJG 0JJoIiiu woqGj 6UJfGq:

bL'1116f6L G2!111;6 (OL 1PL QVI{CH IJI06J2

bbGIrqix JtJ6 vJ

(o'os3) (ooai) (0003!) (oe)-0'02'IJ 03382 00044(00302) (o'i Jio) (0001!) (o ia.e)-ooei o'siea o'ooe2 -O'O2!(o'os.i!) (03138) (00053) (o482)-00201 0382! 00040 -o8e33

II!C0J011JP!

1!coIoIJJplg

co1owpJ111

CP116

IICP!16

CiJ1IG

HIBLg'I

IIBL9I

flL9iIHI

V.6IU!0H

vJ-6I3"u&

p(o'os3e)-0 '0330

(00312)-0'0I8!(o'os!)002!!

(ooe!e)00204

(00044)002!!

(oos2)00520

(oo3!2)-0 0 10!

(00340)00222

(oio.i)01220

(03331)0' 1808

(osue2 -00.120

(ooso2)0'OO.II

(oosia)000!!

(00033)-11320(08o31)

(o.1e)-12084(o'gs)-O•0!Q1

(o'o'teo)-01088(o140e)-00200

(01002)01843

(oo.i4)01818

(00.123)00883(oot3)0'iaOg

(oo3)0088!(o'oeo3)-00025(0000!)00332

(00932)0103!

(00008)00013

(0001.1)0'0031

(00023)00033

(oooos)00000

(00004)0'OOO!

(o0003)0'0004

(ooooa)00002

(oooos)0•0004

(oosoo)00483

(ooa42)00204

(ooisa)00308

(ooos..)0•0004

(ooose)0'0005

(ooos8)-0'005'4

(ooo42)-0•0018

(o'oois)-0001!(o'oois)00042

(00024)00032

(oooao)-000.19

(o!!3o)04.124

(o'seee)04222

(1081!)03550

(00334)0•8880

(ouo3)08343

(o'oooo)08841

(00381)08!33(oo3)089.1e

(oiees)05113

(o1a88)0210!

(o3413)03.130

(oso.u)03038

(03202)01844

(01103)-05.1.13

(o 1200)-03331

(o 78o!)-03158(01428)-0114!(osseo)-012.10

(o3e58)00804

(00331)08010

(00400)02405

(00423)08883

(01883)03802

(o'seee)0244!

(03838)03383

(ooiei)0•0004

(01282)00109

(o'oooo)01128

(ooaoi)0130!

(003.19)01154

(ooeoi)01018

01.150

03.124

(oo241)-01331

(00281)-03588

(oo48o)-01.134

(00883)0.1588

(00250)00320

(00.182)02828

(04001)50338

(2'se!4)81188

(38083)

(o833!)34131

COflU 2' 2 2

bbGuqix .LPI6 VI (coucuitiiq)

4

111

VIGX!CO

IIltIGX!C0

IA!GX!CO

IH

II1rAei,

III

11j<OL6

}(OL6111

3o1q9u

Hlox.q9u

oqu'H

IIiUqOU62

iIJqou62ig

I''

IIjuqi

I U

HCLG6CG

CLGGC6

(ooso)003!2

(oo033)03443

(00012)-00038

(o-!81e)-02431

(oIsol)-0030!

(o0002)00032

(00000)00.132

(00511)-0 0001

(009.18)031.15

(00040)aDo!!

(oai.io)-00541

(oiioi)o133I

(00012).00031

(01034)02040

(00384)00319

(0-1082)03443

(00020)-00038

(03021)-0210!

(ojeai)-00303

(ooon)00010

(0110!)00023

(000!!)-01130

(ooooe)00402

(o-ooie)00018

(00000)03203

(o000.1)-00319

(oooii)324'3000

(ooooo)01022

(00432)-00582

(01140)-00420

(00102)00313

(01321)0810!

(005.13)-00285

(o000.1)00038

(ooooo)00000

(00881)00124

(0321!)00133

(003.10)00308

(00023)1120!

(00338)-00020

(0001!)00038

(ooooo)00000

(0-0341)00330

(00.103)-00330

(00023)00018

(080.18)33300

(orois)-00243

(ooooi)0000!

(ooei.t)08313

(00308)00333

(ooee3)-00088

(0002!)-0'0013

(14023)3-oo2e

(01088)-01083

(o000l)00002

(00218)08133

(00422)o-oieo

(00805)-00034

(o-oo.s)00031

(o-&sse)p8099

(01425)-00383

(00004)O000!

(ooexo)02193

(0-0123)00010

(01133)-00410

(00018)-00015

(0-1088)-010.18

(o-oeo8)00508

(oo003)00003

(oo83o)08118

(o-oiee)-00101

(03202)-00888

(oooia)-00013

(13203)-00338

(ooeai)W01!1

(0-0002)011018

(00804)00034

(00142)-0004.1

(01033)-00334

(o-ooio)-00010

(0-3200)-011132

(0-0285)011232

(oooos)00003

(00.180)010!!

(oooia)00118

(00144)02394

(oooea)01030

(o0088)53120

(oaala)-93020

(00041)00032

(osaoe)0138!

(ooe9o)03858

(018.10)02103

(00308)00423

(3-8808)138!g

(voooo)-3!084

(00018)00031

(ooooo)00000

(00080)03838

(018!e)02103

(00308)00423

(38808)10910

(10880)-31084

(00018)00031

(ooooo)00000

(00333)00100

(0-08.13)-011334

(oooe.1)ooose

(o100.1)-03308

(03032)-01.188

(oooos)00003

(00283)09194

(0034.1)-00333(00304)-00318(00182)00148

(001.18)00040

(00304)00011

(0090.1)-01410(00808)-01503(ooeo.1)01040

(0-0008)00951

(0-1092)00003

(o0008)00090

(000.11)00080

(0001.1)-00003(00010)-00000(oooss)-00022

(oieia)-03423(01.134)-03021(033.10)01.10!

(030.18)03383

(0-1021)01881

(olel!)01118

(02118)01113

(0-1003)-01202(0-1.100)-01318(o-soei)-0132!

(00008)0001!

(ooooa)011053

(00010)0000!

(o-oooa)00008

(oooii)00021

(0-3421)0513!

(oJ2ee)03113

(01333)093.18

(0-1108)01804

(ooooo)011000

(o-oooo) (ooooi)10000 00050

(ooooo) (ooooo)1•0000 00000

(01482) (oisee)00332 -03908

(00894) (0132.1)04813 -03300

(0-0103) (01383)0344! 03439

(0010.1) (00100)082.10 -015!!

(0303.1) (00100)01400 -0130!

(ooooo)03512

(01531)05385

(ooiie)05311

(ooooo)05842

(a isie)03.111

(0.1110)00081

(00039)01132

(0-0222)00083

(00.14!)01042

(00831)0001!

(oseie)04184

(00833)0108!

(0-0838)004!3

(0-109!)0.1210

(o4e8!)01210

(0-0330)00802

(0-3830)02189

(0- 1801)04331

(00832)00813

(a i.12)o-iee

(cr0000)I 0000

(oooas)08132

(ooooo)00013

(00023)0218!

(00828)01131

(00120)003.12

(00238)08011

(0132.1)02283

(35.138)22810

(02200)

(s 1313)2'0031

(5o28 I)401±3

30103

(10840)48335

(00.138)38333

C0flIJfLA 91 93 93 91 92

bb6iJqx 1l)IG vi (conrltJnGq)

IIIL" I"

IIJ}1LfCGA

JJI4WA

111

lpgr'9uqII

Lpg!P'Jq

jpjuqIII

,LS!MffU

11L9JU

JiMgIjUI

boLt1IU

b0Lfi9I

bOLCfl1In

bPIIIbb woe

bP!1!bb!u62

bP!I!bbIUG2

111bic'IIb1U

UI

W

j4IGLiV

(00033)00213

(0 Jv11)-01182(001.13)-00012(0032.1)00383

(00382)o'osia

(00340)0033!

00323(00492)00328

(00228)00204

p(o'o143)00912

(00313)00912

(00244)01032

(00414)001!!

(00232)0'0213

(00333)00.159

(00323)00.102

(00542)00202

(o'oiis)-00005(o'o'uo)-0014.1

(o'osei)00380

(00009)-00842

(01304)-00800(01332)-00330

(01221)01242

(o'oaeo)03028

(00821)0188!

000122

(o'122o)01043

(01201)01103

(oo44.1)03223

(00489)05223

(0-0844)03311

(00810)osses

(010.15)0321!

(ooooa)00830

(01002)00121

(o'iaai)01233

(00828)03083(03228)034.18

(03204)00220

(o'oooi)00031

(00154)00122

(00082)00138

(00030)-0'OOOO

(00034)-0'0018(ooosi)-00034

000038

(00128)-00012(00332)-00100

(00033)-00002(oooo)-00002(o-ooii)-00024(00103)00044

(00088)0 '0020

(ooosa)-00022(00039)-00023(00035)-00034(0001.1)00011

(00033)0003.1

(00011)0'0013

(ooooo)1'0300

(04343)12385

(01858)1.1111

(a's 110)-01112

(05288)

(05198)-01238

0F1800

(1-2942)01243

(5800!)13333

(01182)-02380(05522)-02380(03312)-03210(05.184)-0 '5292

(11308)-0'OSOT

(01233)01135

(0 '2 154)03324

(a '32 18)-00258(00881)-O3aee(03282)

(03139)-0 '0422

(0-0088)-01882(00308)03132 -

(03013)-03310(01221)-01133(0128.1)-00320(a' 1080)-00200

0-0042!(00228)-00210(00844)-00822

(0' 1410)

(o'ssoa)-012.12(o'seso)-02304(os 380)-02233(05892)-04882(01830)-03312(alas!)-04823(03333)-02884(01.102)01021

(i'iooe)00438

(02.113)-01203

(ooaoo)00001

(00023),O'0040(o'oooo)00000

(00013)0'0019

(o'oooa)000 10

(0000!)00012

000012

(00019)0001!

(0004.1)00039

(00008)0'0031

(0-0001)00031

(00032)000.11

(o'oooo)00000

(o'oooo)00000

(o'oooo)00001

(o'oooi)0'0003

(ooooi)00003

(o'ooss)00023

(0002!)00028

(0-0009)00013

(0' 00 12 )09823

(o'oaoi)0154!

(ooooo)08824

(05322)03813

(0' 1830)02882

(01338)0412!

()00388

(o'1388)0248!

(a' 1238)02214

(o'oooo)00000

(o'oooo)0'0003

(ososi)01308

(0-0021)08824

(00040)08828

(o'oooo)0182!

(009!!)01382

(o'oooo)02542

(ooooo)0'OOOO

(03283)02442

(ooee!)

(0-3310) (0-0113)01944 -09020

(0-3112) (01545)0'3232 -03331

0 008588 -05218

(003.18) (o'oeoo)08313 -03123

(0000!) (o'ooie)08!!! -01018

(0-0302) (00284)09314 -o'oe2e

(00012)00831

(a's 134)osae

(o'oooo)00000

(a' 1293)03184

(a' ileo)03148

(01433)03383

00'3!13

(o'iaai)02213

(02213)0383!

(o'oooo)10000

(o'oooo)08889

(o'iaso)04024

(o'oooo)00000

(o'oooo)00000

(o'oooo)0'3143

(o'ssii)03408

(o'oooo)0'3!2e

(o'oooo)1'OOOO

(05983)03223

(o'oeeI)0.1214

(a'oosa)00012

(o'aoee)02213

001333

(00.185)02922

(00141)01443

(002.13)04323

(08128)38814

(18828)20283

(3838!)238480

(0' 1080)5182300

(30330)43023

(12108)32103

(0- 1342)

CORUfLA 91 93 93 91 92 t1

D!q uo COJJA€LG

}6iu c9jcnJ9fioU [9fl6q 3fH9L 6LL0I2 uo L6bo146q

3

iii!WP9PMG

iiwpvpM6

tSJUJPPM6

'U/UGflG

ii/GUGnctI

ip4IGn6j

(oo35)00080

(00323)00048

(oosoe)0004

Oct-0092i(oos)-0022(oosea)-00224

(o0813)0J00

(ooi.3)01.38(ooi)01824

003811(ooiso)03840

(00288)03008

(00054)00019

(00051)00018

(o'oose)00035

00O42(ooii)00405(oous)00402

(03903)-03824(o5e44)-034(05241)-03484

000290(oiaos)0S3..O

(oisoo)03313

(o1o38)-01303(00833)-01184(ooia)-01341

0-01023(oso.&8)01880

(01898)o182

(oooos)00003

(oooos)00003

(o000s)00003

0000.t3

(00014)00022

(00012)00024

(00414)09831(oose)08883

(oo52e)00103

000000(00000)00000

(ooooo)00000

(oo448)O0!43

(00302)00.14

(oosi3)00.i9

C)0.82

(ooooo)10000

(00132)00888

(03028)0808Q

C)03303

(o1o2e)-00338

0-0L13

(o1e80)08181

000013

(41280)W3080

(4a232o)18830

COfluf LA L 3 3 1 2 i c: -x i l

vbbnqix jypj yj (col,puhIGq)

AJO6J i ! lP6 cuqcq UOLUflf uJoqGJ 12 2i1W6q' AO6J JJ ! f JiG 26WI-bL9'UJ6LiC VIICH

DW1JO2C2 lOt JJ6 AL!UCG uoqje

ybbGnqix jpJG y5

!U1ppMG

A6IJG11GI9

J}14CGA

LOL1I

bP!I!bb1U62

J.4I6L1g

V16)CICO

1,g1gA2Ig

KOL6

oiquuqouG2i9

jJJqI9

C

BL9II

VL6UIiU

boss]14830

10181189.!. 1

[01.111228Q

1<oooo]

1<ooool1423eo

[0

1oo8111 •05!

[<0000]318a40

1<ooool4.1.1490

[<00001343e8

beau3881

1083113°831

1o•issliooeo

[<0•oo1134431

[<oooo]1022400

kzooool134.120

[<0000]208380

1<oooo]31111

(0014]1000.1

[<0000]201200

[ooeaj

[osao]81 10

1<oooo]312.12

[<0000]343300koooo(358520[o iouW33Ô

105231.1804

koooo]34!330(<oooojm eiokoooo]3!33![oo2II15220

[oooi]

[0009]1.1230

[<oooo]34418

koooo)102 100

[<o'oooj180300

[<ooooJ12.1830

[<0•000130813

ko'oooj24443[0333]8332

[oo2a113' 120

[0.123]3311

(<ooooj23331

[<oooo]13.1000

[0823)3043

[oieil8338[0.101]33.15

[<ooool100380

koooo]0.1231

koooo]343.12

[0080]11'3!8

[oo.14J11213[00.10]ii'eie

[<oooo]40080

ko'ooo)1148500

kooool80308

koooo]11.1080

[<ooooj34.183

[o'.1a.1l3001

10392].1110

[osi!].1881

10208123.13

to Joe]1040.1

[0004118330

[oo54]14244

[0830]390't

[<oooo]Saab[0884]1023

[<ooool22'eOT

[0.10.1)3.353

[oooil31010[oosi]11803[039.1].1311

[<oooo]2333!boos]30848

[<0000]20.108

[0110]3304

[0580].1328

10043]13893

(0333108.13

[033418000

[03321e820Iooaol..

[oooi118'412

10034111238

[0820]5008

[<oooo]58'203

[088311008

koooo]4!881[0.155]3004

[00.111II •00.1

[0023]132O3

[032.1].13.1!

(<0000]48402[oooel12'303

kooool00303[0.18813080

10 33218044Io'sleI.1883

[0305].130.1

104301202.1

[013218.1.15

to Joe]104.14

10002]184.18

[0054]14240

1085515881

[<0000]5.1030

[o'0811I'll 1

[<o'ooo]20'811

10882]0029

[<0'OOOj54.104

(0031]13'8!!10.100)3'332

[<oooo]12'380

[oooel1.1832

koooo)20023

C

[0382].1111

[0318].1031

(o'381)0142

(03.13)0000

(0300)0.184

(0044)012!

(030.1)-00.10

(02.1.1)V.188

(oiie)0204

(048.1)333!

(1002)-1013(0483)-0.118

(0214)-0844(03.1.1)0023(o'sii)0'433

(0212)-0323(0331)0805

(oiea)1803

(0030)088!

(0020)0883

(0333)0351

(0031)5•538

(o3a1)01e3

(03.13)0011

(0331)0838

(004.1)-0404(0333)-0300(02.1.1)1.188

(o'144)0281

(o2e3)5110

(1 ioo)

(o'233)-0800(020.1)-1048(0311)1048

(o'338)0401

(0310)

(o330)0812

(0403)1.182

(0233)1141

(oe23)0884

(0334)0380

(0231)1828

(0383)0122

(0234)0028

(0249)110!

(0033)-038.1

(03.14)01.12

(02.1.1)1.188

(0303)0080

(0011)1'951

(i'oao)-142!(0494)

(0121)-0014(03.1.1)1033

(0338)0184

(0343)-0248(0343)08.13

(oboe)1.182

(0291)1030

C

(o'332)0333

(o'235)304!

(Vol.1)1034

(00.10)13.18

(090!)023!

(5•134)430!

(0203)0030

(3052)0020

(08.15)1935

8423(3,44)8'240

(1023)3'218

(reso)5'220

(iioi)1932(ob13)1'020

(o'200)

(1108)3001

(3138).1011

(1204)3388

(3824)4818

(o'.114)1'430

(381.1)10'18!

(108.1)1.138

(00.13)1382

(u 034)0942

(3142)4'3't(o's)1038(3032)e821

(1018)31.13

(3120)8083

(3234)10'333

(1140)4323

(reao)3919

(1331)3'101

(0428)108!

(0200)-0248(1303)3•389

(3084).1180

(1.184)3431

(30.13)4920

(0.133)1400

(504!)9430

(108.1)1.133

(0244)1118

(1581)1108

(303.1)3111

(0200)0220

(5032)0821

(0008)1883

(3'32o)8331

(3'129)O'030

(1028)3233

(1228)5.393

(iiio)3'038(o'43o)

1 '031

(023.1)-0040

(1183)5880

(3082).1184

(1083)3132

C

(0082)1400

(3'!8o)8'088

V 16f °U IG.iqn I

COUULA 1 11 111 1 11 111 I II III 1 11 III(e) 2J(6MU622 fTI4Oi2

aGug, COLLGI9I !°'Cfl1UP-llfl!111g

dwLGq L6Jqrr9jaBOX-bT6LC6

D!q IJo COUAGL6

2bVJSCIt G JUJfIO&J 1G 1JJIX!U bLUw6L 1) =

111 -q!'P!" woqG pG GLGG2 o LGGOW b&LwG;6L i < ____________ __________ __________ __________ ____________

[0010] 100131 Iwois] los.1i] [oe) 1o.ise] [0323) [03331 Ioswal [oooil [<0000] [oooi]JwppM6 10.100 100.18 10448 2101 5330 3022 0004 08.19 .1305 38CO.1 4413! 41331

[oeo1 [oiii) [080.11 [0891) 102491 l<oooo) 10103) tooa) [oiii1 [0020] [0002] [oooil39.18 3.148 5210 1183 30014 53012 1024! 10.110 340! 32013 3F2I1 4J•340

10200] [O.'t2] [<ooool 1oooi] [05131 103311 [<o000) [<ooooj4800 3211 31011 18200 8383 8302 2304!

lo'sasl 1oaz4) [030!) 101031 [<ooool [osool 100431 [oegi) [02.14] [0031] 1<ooooJ [0181].1231 0.12! 140 .1.140 100402 2023 'U 320 3044 4.1eo 38023 302380 30034

[08111 Loie1 [one] [<oooo] 108301 [01801 [oi) (<oooo]L!M9U 5892 33I 4200 143040 3.183 3'824 T200'U 384000

[o.isal loiso] 10138) [0304) [03021 [03041 [0801) Eo8e11 (o8e1) [oeol] [0004] [oeoi]3013 3013 3013 4333 431! 4352 3203 3203 3204 T330 13832

[0.1821 10430] [0039] [<wooo] (0302) (08221 [00381 [<0000)bP!I!bb!U 310! 0050 3232 U 040443 0242 s'e12 38408 U 138!J.1

(05411 [0530] koooo) [ooJ.1] 102381 (o3801 [<o000] kooool.1802 811! 23380 U 130!! 203! .1302 88048 3seP84

[020.1) (009.1] [0021) (oooil [o04e) [02001 [003.1] [<ooooJ4iGLW 2381 3832 8434 U 18910 4334 2303 3.1392 U 83028

[0.1.14) [0800] (03031 [<oooo] 10.1001 [0.1131 103i2] [<oooo]?4GX!CO 35.13 3342 4844 38.1.12 3834 3.111 W8.1a 93000

[o3o3) [os.12] [ooos) koooo] [o8e31 [oaeol [0003] [<oooo].1184 !235 10!80 3.1800 3220 1489 p 228!0 23403

[08891 [oa98) [0001) [<ooool [<o'ooo] Iossa] (0802) [0.1.10) [oasol [ooos] [<oooo] 1003010403 0041 0831 33383 188043 203! 3034 3332 1089 3!032 331420 38'331

[oo.14J (0139) [oo8a) [oo211 [<oooo] [<00001 [oi] [0389] [034!] koooo] (<oooo) [<oooololqvu 11233 8.10! T08.IP 8580 403280 30834 .10.11 0318 0.130 43!00 281103 2204!

[osoil [osoi] [<ooool koooo) kooool [0032] [oooi] [00011 [02011 koooo) [<ooool [<00001juqou& 9244 8242 30880 303098 503.1.13 0001 33033 33033 4802 3.13308 3!3'104 29454

[004.1] [o2301 [0284] [0089] [<ooool [<oooo] [0312] [030.11 [03801 [0004] koooo] [<oooo]431! 2032 4001 9110 1200.15 213383 8331 !e33 .130! 34!T0 3.11380 113000

[0013] (0210] (<0000) [0381) [oseoj [oJea] koooo] [oiaolCLGGc6 103.10 U 2sea 51'!O! U 2003 .1.113 8090 82!51 30.110

[0.140) [08231 [<oooo] [oiisl [oeosj [0.10.11 [<zoooo] tool?]Co1owP 3230 3043 31388 .140! 4224 U 3.1.18 115434

101151 [ooao] 1008.11 [0933) [03811 [0.10.1] [0813] (0404] 104041 1o3181 [or421 [o313)CP'I 10302 10001 10'!38 1230 4183 1858 38.11 2043 01!4 18100 31800 191o3

108.11) [0020] [08.13] (03481 100331 100031 [oisi) bios) [0085] (000!] Iooojl [<0000]1 310 1 203 1 300 2408 1134! 10393 10000 0308 11310 33080 38035 40Q80(o2e!) (0.1281 koooo] 10058) [0591) (0151] (<oooo) [033!]LGIJ1U 4932 3300 !!23! 10880 .142! 10080 111342 1Ô040

conhJcL? I II III

f3()LAGU f62C

I II III

(i)LAI0WGIJI fG2

I II 111

5(Y)ML!&1JC6 f6

1 II HI

y+4)1UILJ

fl 1G4f OU cuqLqJ6q L62JqnUj I\

V I)bGIJ(11X .L'PI6 VS (cons IIJflG(4)

(oiei)oeae(0' 1e1)

020Q'(o'oooe)

0.000w

00034(o'0003)

0.000e(o0002)

0.o00(00002)

0.000e

(00002)0'00T

(00002)00012

(0' U 32)-03413

0•0000

(oo23)08033

(ooo)o.aeo(oo2io)0T

(ooooo)0'OOOO

(05018)

0 '0000

(ooa)0'040(o'o2o3)

00288(00382)

0'0348

(ooo42)0'1332

(o'2e)02834

0'8e83(oJoo3)

05082(oJ!o4)

03315(oJo4)

03123(o'o3.)-o'aaa

(o3oeQ)-0'23.2

(o0003)00003

(o'oose)0'0002

(00013)00030

(00008)00013

(o0008)0.00Jg

(ooori)0'0030

(o0004)00013

(o'ooo)D•0014

(00004)00015

(o'ooo)00014

(o0008)0.001

H

1gA2z 4"III

yOL6H

1<0L69

OLG111

U

')oLqU

Ifl1Ip1qou6T9

luqouG2'gIII

I'

uqiIII

IICLGGCG

CIII

C010WP!II

oouJpiC0J0WP1 -

HICP!1

II

CP!IG

IIIBL9TJ

II

IIIVLGUrIIJ

IIcyL6IJJU

VLGUHJ

(o i'ne)0'145

(o'o4)(ooooo)

00000(o5838)

o'e342(o589e)

034Q3(03838)

03418(oriir)

081e8

(ooi)08131(o13)

02422

(o'oooo)0'OOOO

(o'oooo)00000

(ooooi)10000

(o'saa)0'3001

(o's)0Q238(o'328)

02348(oiso)

O'1e38

(01038)o'soea

(o' 1228)0'J,83

(o'I3't)04358

(oreei)04242

(o'3s'4)

(oaooa)

(04218)-0'428

(0' 18 13)-0'33.2

(or832)-0'3888(o'sii)-04448

(ooos2)-03023

(o12a)

(oisai.)-03430

(0.J41)-0•e033

(o'I3)

(oooor)00001

(o'ooio)0'0004

(o'oooe)00002

(o0002)0'0002

(ooe4)o.aie4

(00224)O8Q.2

(o'o458)08232

(ooQo2)Q•8QQ1

(ooo')0'033Q

(o'ooa)0'0800

(00202)0'0410

(oo8o)ooesi

(oos2o)-0'0349

(03518)O'1388

(o'ies)0'384

(05408)01801

(0' 1138)00232

(oo133)0•043.

(oseoe)0'4838

(oio)01203

(o4e8e)03048

(oe2)08083

(T'e4o2)-0.30.U

(o212)-0'8348

V2?IUUJ6L1C 6GC42 ! couqou9J AtWUCG2

bbGuqnc LPI6 V

CorTucQ t: c$ J:

ybbGuqnc jypj6 y (couiunGq)

(00003)00002

(00003)0•0004

(ooooi)00002

(ooosa)O00.8

(oooii)oooia

(00003)00010

(oooo)0•0011

(o'0030)000Q3

(oooss)0.0oJ

(oooi)00031

1JO p6 JOpj E9i16q o cOU!6L6 cLLoL uoc qroc3 obiwg n2bGcfGq

in (00013) (ooooo) (oo)rwp9pM6 0I 00000 0T21II (ool2e) (ooooo) (00252)

00000 013L21 (ooosa) (ooooo) (00332)

iWppM6 o'aro 00000 oioa1IIc

A6U6Sfl61

IIAGU6flGI

(ooooo) (ooooo) (o1333)6U6nG 00000 10000 -ioooa

IllLnL!G

IIJ)1LJ6

JJILJC

iii (03233) (oaTh) (oaoo)JJ13q 010.3 02033 -02113

H (ooo) (oooa) (ooa)jpijuq o2a1 oaaoi (oieo) (o3oa4) (oso3e)

LpJq 01Q22 020Q0 -0433ein (ooa) (oooa) (os.)03513 0'LUii (o1234) (o1234) (oeooe)

04ã0 02210 0.003.i (ooooo) (ooooo) (01131)

O2h4 01223 -o32e1HI

b01311II

b0Lfli (ooooo) (ooooo) (osos)

b0Lfl91 00003 -oseoin (ooooo) (ooooo) (oi152)

bPflbb!UG 00000 00000 01a28(ooooo) (o'oooo) (00201)

BP!1bb1U62 Y0000 00000i (ooooo) (ooooo) (oesu)

pT1bbw62 00000 00000 oaoin (ooooo) (ooooo) (ooas)

083ô0 0J10 -03914II

(00451) (ooooo) (Joaha)6LT 03008 W0000 saeo

H (osi) (rasaio) (18oa2o)MI6LI 03328 03331 231211

i (00282) (oe.i) (1a320)MI6L 0.12a0 o511 310

iii (ooooo) (ooooo) (ooel2)6XCO 0342 o3e22 0144

ii (o1o2) (on8o) (os1r)JAXICO o1a82 03242 003O

i (ooisi) (ooisJ) (ooea2)GXJCO 0e392 032e1 -wieeo

(oooo8)00031

(oooso)0•00,12

(oooi)0.00

(oooI2)000Q8

(ocoor)00003

(00004)00013

(01323)0033e

(oooo)00010

(00002)00033

(oooie)00023

(00004)00018

PCci)

CCC')0C00

•Ibi1.

91 H {L 80 01 3

Jof9ç![Tf? Lf1j9L woqGJ2J1th1LG T CP'L ETI1LG iq coJowp

CqC')

C

P

C

C/)

/oJYfrJrçA L6J1J1I. LUOqGJ1!IIL, Y VLIJtUJ9

bbcuqix E!L6 jJ

j. ) 91 83 80 02

4-

— — — LU!x6q

— IJOLW9I

AOJ9fJJJfA LGflJL LIJOGJ2

'°JflJ!I2 LG1JJ9L uJoqG111LG TV H'''1

A 9 L

'3

P . 13 81 93 82 913 81 830C

ci)

ci)

Aojcipf?I InJgL UJGj2EIJLG J uqoIJ62I9

AoJIfrJffX LI1JL LJJOGJ2I1L

AOJ9fiflf?1 LGTI1L IJJOGJ2LI11LG JJJ 'oLqu

bbGuqIx ElL6 %j (courunGq)

— — —

P i 3 91 3 9 88 01 020DQ

QQ

0 91 93 92 9. 80 01 03

/OI91JI!1A LG11JJL UJOG}2j HqicJ

AG9L

C .) i I U U... l) I

AOfiJi1A L11J9L woqjzE!nL6 JJ( XfCO

91.

AoJQf!J!i7 L6FJjL UJOGJ2

1G9L

L!1IL( I! I<0L9

P0C,)

P0

CCCO

bb6uqIx !nL6 jj (couurI6q)

C)C)Co

PC')

LJnLG jJ ]4JGL!

P £ 8 i B3 Q 9 I 300

P0(/3

AOI9IIIJIA L6flJYL WOCJGJ /oI91!1UA LGJ1JUJ. JJJOCJGJJ,Ji1L 11 y{9J/9

/OJfJ1f? LG11J9L IJJOJGJ2'' T b°-f"'1

bbGuqix E!nLG vi (courun6q)

X6gL

.\a @1 @3 @2 9 @8 81 8300P0Pcj0P000

-

[_—LU&XGI

uoLwg

a 1 3 @2 8 @8 ai 83a

0

0CO — — —

•I• I P.11!

:

w!x6q. c

— UOLUJ9It

ALia 91 - 3 92 Q 80 ai

00

AoJfrJf? L6I119L woqGLir]LG jb jiu

AOJ9fIA, LGflJ9L JJJOGj2I!1JLG jw

/0!fEJ!f LJIUL WOGj2Ju LIJIJJbbJLJ

- - - w!xGq

- — uoLwgJ

,oI9cr1!f? LGflJ9L WOG2FfG J2 AUGJ1cI9

bbGuqIx EunL6 jj (courun6q)

AGIL

) UI U U UD 01 i P.4

Ab9L

PCCo

C

PCO

0C

cCc,1C

3 91 9300

L G

93 31 33C0

PC4

PCCD

C4C0Co

P

LI1JL woqJf IIJJppWiG

AGL8 91 93 82 8 90 01 03

1———-

UOLWj

/OjfJ[!f?i LGf1J11. woqGJ2EMflLG JL JflLf(c,?

AoJfJJJç?' LGflJ9L woqjzE!nLG jd- jjjjjujq

PCO

aac/s

XGL

AOjJii1!f?\ Y\IJI1J{Lrc [JJoqGJ21!11 LG U V1IJ jiJJtI

bbGuqIx &!flL6 V5

a 81a4

AGJL

83 12 8 80 01 03 J0 91 83 82 8 80 01 03a0

c'sa

(/3

PQ-4.

P0CO

, ij

. '9IJJwGL1C uJoqGJ2EIJ1LG Sc• CP'J

81 3 92 8

Qa

iojçijiç? g2XWWGfLTC uJoqGJ11LG q c°J"-P

ci-4.

8 91 93 82 @ 9 1 03

A()J ![IfA 9I\IJJUItILiC L1JO(1GJSP LP-'!t

(b9unuJno) A

91U

FI

xibn9qqA 0 b a) 0 0 0

6ibfTI

IS ii;i'1

19bom

iiJrnrnyn ,\iiIiiF

ilOV

c,)

D31)

.S iui1

1born iiJsrnrn.

,JiIiJstov

P

0

I6mlorI

—

b9xinl —

—

—

ce8veace'ae 16ev

nfibIoL rIS

iuiT

2Isborn oiiirnm

a ,\jiIii6Iov

0

0 b P 0 a) P

0 P 0 cJ) P

0

0 0 C

I69

isriobn! .S

91uiI Isbocri

iii9rnm'26

,\ii!iJ8Iov

P 0

C@

t@

@

8 T

B

8 C

8 18

@V

0 0 —

0

TV

C

@

1@

@8

1GSV

V8

8 C

e

C

C

18 1

PCCCD

PC-4CD

PCCI

PCCI

PD-4.

CCcc,

P

CQ

bbGuqax EnLG (courun6q)

AG9L

0 91 83 2 8 01 B30

CI

9J 83 92 Q 98 31 3

CD

-40C'-,

1o1fTJif? g2?uJuJ6fLrc L1JOGJ2EJI1LG SV• GX!CO

—4

iojçi1ic? 92AwW6IL!c uJoqGJSI 4JGLI

P0C

P 91 92 2 Q 98 01 3C

AogfiJiA yzAiiJ11J€Lic woq1,!1''•' 5!

AOJ'J1 !IE1' 7LU1JJG1L!G WG[21!1111.6 S•i VYIA2!9

XGLAGL

AojgfijJf?I 92?LLJUJ6fLIC woqejELnLG 50- b0Lffl1

AoJfiJrfX' g?iuJWGfLiC LIJOqG2E!flLG 5b ju&u

?6JL0 81 93 82 9 88 81 83

CCC

PC

PC-4.

PCC,)

AoJ9f[!fA J2AWWGfLrG LJJOGJ2LJI1L 5W b1!21YU

bb6uqux iflL6 (courlun6q)

C. d 8! 83 82 8i 93 81 33dCPC

J3 8! 93 82 8 83 81 83

I I I• II IIIIIIIII -

— — —. msx6q Iijoiuigl

II

P

p 8 91 93 82 @i 88 81 33CC

PC

PCCO

F- —.— JJOLW9I

PC

AOJfJJ1fA 9XWW6fLIG woq6fL!" SIJ bPiI!bb!U2

80 51 03 j500CC

00(0

0(0

CCl

CCcj(4

AOjfJj!{? 92AWWGfLIC WOqGj2J!J16 d ,1,I'! 1 ciuq

bb6siqtx if1L6 5 (cou1unGq)

.4

J 3D0

0

C(0

(1

A6L3 2 9 0 51 a3

IoJfrif?i 2AwwGfLrc woqGJ2E11LG S2 /1GUGsnGJ

AoJf!Jf1 g2XwwGçLIc uioqIJI1LG f jjjpgpj