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INTERACTION BETWEEN STOCK

PRICES AND

EXCHANGE RATES

Author : Ankit Mital

Student ID: 0950302

Dissertation Supervisor : Alessandro Palandri

Date: August 2010

ABSTRACT

This paper examines the dynamics between the stock prices and

exchange rates for a set of four emerging economies and four advanced

economies using Cointegration & Granger Causality techniques. The

results show feedback relations for India, Brazil, South Korea & Japan.

On the other hand we find unidirectional relationship for the

Philippines, Australia & Canada with stock returns causing exchange

returns. While for the United Kingdom, we fail to detect any significant

relationship.

‘All the work contained within is my own unaided effort and

Conforms with the University’s guidelines on plagiarism.’

‘No substantial part(s) of the work submitted here has also been submitted by me in other

assessments for accredited courses of study, and I acknowledge that if this has been done an

appropriate reduction in the mark I might otherwise have received will be made.’

Word Count : 7252

To,

My Parents

Acknowledgements

I would like to thank my dissertation supervisor, Dr. Alessandro Palandri and our academic director, Dr. Richard Payne for their invaluable advice and guidance. I am grateful to my brother Abhinav Mital and friends Priyanka Khosla, Osman Ansari, Sibtain Masood, Kartik Pental and Hammad Pothiawala for their ready support and giving this paper a patient reading, pointing out its numerous errors.

CONTENTS

SECTION I............................................................................................................................................. 1

INTRODUCTION .............................................................................................................................. 1

SECTION II ........................................................................................................................................... 5

LITERATURE REVIEW ................................................................................................................... 5

SECTION III .......................................................................................................................................... 9

METHODOLOGY ............................................................................................................................. 9

SECTION IV ........................................................................................................................................ 12

DATA ................................................................................................................................................ 12

SECTION V ......................................................................................................................................... 14

EMPIRICAL PROCEDURE AND EVIDENCE ........................................................................... 14

STATIONARITY ......................................................................................................................... 14

COINTEGRATION ..................................................................................................................... 15

GRANGER CAUSALITY ........................................................................................................... 19

VECTOR ERROR CORRECTION MODEL ........................................................................... 29

RECESSIONARY IMPACT ....................................................................................................... 31

SECTION VI ........................................................................................................................................ 34

CONCLUSION ................................................................................................................................ 34

BIBLOGRAPHY ................................................................................................................................. 37

APPENDIX .......................................................................................................................................... 40

1

SECTION I

INTRODUCTION

The first signs of the financial crisis of 2007 came forth in the Equities market

in July 2007 (Mark Taylor & Michael Melvin, 2009). As the Foreign Exchange

market participants watched these developments anxiously, their worst fears

were met on August 16th

2007, when major unwinding of the carry trade

occurred. This was quite opposite to the sequence of events of the Asian

Financial Crisis of 1997 where it was the foreign exchange market that

triggered a tsunami in the equities market. It is believed that the sharp

depreciation of the Thai Baht triggered depreciation of other currencies in the

neighborhood, which eventually led to the collapse of the stock markets as

well. The different experience of the crises throws up a puzzle which needs to

be solved.

This study is undertaken to analyze the dynamics between stock prices and

exchange rates. This link can be exploited to predict the path of the exchange

rates. This will also assist in the management of corporation’s exposure to

foreign exchange rate risk of their earnings. More importantly for Investors,

since currency assets are an important part of investment funds, the

knowledge of the link between currency and other assets in the portfolio is

vital. The widely employed Mean-Variance approach to portfolio analysis

2

suggests that expected utility is maximized by the minimization of the variance

of the portfolio. For the calculation of this variance, the estimation of the

correlation between different assets of the portfolio is essential. Is the

magnitude of correlation different when stock prices are trigger variable or

when the exchange rates are the trigger variable? (Dimitrova, 2005). Also,

stock markets have been found to impact aggregate demand through wealth

and liquidity effects, influencing money demand and exchange rates (Gavin,

1989). Thus while formulating monetary policy or undertaking foreign

exchange intervention, its impact on the stock market needs to be taken into

consideration.

There is no theoretical consensus on the relationship between stock prices and

exchange rates. Basically there exist two hypotheses supporting the causal

relationship between the stock prices and exchange rates. The traditional

Goods Market Approach (or Flow Oriented Models) by Dornbush & Fisher

(1980) and Solnik (1987) suggests that exchange rates affect the value of a firm

via affecting the competitiveness of a firm as it affects the value of earnings

made in foreign currencies and the cost of funds borrowed from abroad. For

instance, a depreciation of the local currency makes the export goods more

attractive, leading to an increase in demand for the local countries’ produce in

the international market and hence the revenue. This leads to an appreciation

3

in the value and hence the stock price of the firm. On the other hand, an

appreciation in the local currency reduces the value of the local firm via

making its exports less competitive and thus causes a fall in its stock price.

However, the ultimate impact upon the stock market would depend upon the

importance of external trade in the economy and the degree of trade

imbalance. A depreciation of the currency, while making exports more

competitive, also makes imports more expensive, thereby it increases the cost

of production.

Alternatively, the Portfolio Balance Approach (or Stock Oriented Models) by

Frankel (1993), focuses on the role of capital account transactions. This

approach stresses the fact that the price of exchange rates is determined by

market mechanisms, supply and demand. According to this theory, a change in

stock prices leads to portfolio adjustments. A booming stock market attracts

Foreign Capital, leading to an increase in demand for the local currency,

leading to its appreciation. A bearish stock market on the other hand,

witnesses investors selling off their positions in the market to avoid further

losses. This would lead to an increase in the supply of the local currency while

increasing the demand for the foreign currency, leading to a depreciation of

the local currency. Moreover, movements in stock prices may influence

exchange rates and money demand because the investor’s wealth and liquidity

4

demand could depend on the performance of the stock market (Gopalan Kutty,

2010). Therefore according to this theory, a rise in the stock market leads to an

appreciation of the exchange rates, while a fall in the stock market leads to

depreciation of the currency.

As we can see, according to the two theories which offer completely opposite

views on both the direction and the lead lag relationships between the

variables, difference forces are at work in determining this relationship. The

traditional goods market approach focusing on the impact of exchange rates

on the competitiveness of exports, predicts that exchange rates lead the

relationship, with depreciation in the exchange rates leading to an

appreciation in the stock prices. On the other hand, the Portfolio balance

approach stressing on the impact of global equity flows on exchange rates

proposes that it’s the stock markets which lead the relationship, with an

appreciation in the stock prices leading to an appreciation in the exchange

rate. Ultimately, this relationship would depend upon the relative strengths of

the aforementioned forces. If the external trade of a country is able to exert a

higher influence on its economy than its capital flows, the Goods Market

approach will prevail. However, if the opposite is the case, the Portfolio

balance approach will prevail. There might even be a case of the two forces

5

cancelling each other out which would result in the absence of any clear

pattern emerging out of the relationship.

To examine this relationship, we employ cointegration tests (after establishing

non-stationarity) followed by Granger Causality tests (1969) or Vector Error

correction Models (VECM), depending upon their applicability to the data.

SECTION II

LITERATURE REVIEW

A number of studies have been undertaken over the decades to analyze the

relationship between stock prices and exchange rates. However, there is a

general lack of consensus on this relationship and the existing literature offers

conflicting evidence on the direction and even the existence of relationship.

One of the earliest notable studies to examine the relationship was undertaken

by Franck and Young (1972) who failed to establish any significant interaction

between the variables. Aggarwal (1981) found a positive correlation between

exchange rates of US dollar and changes in the indices of US stock prices.

Solnik (1987) tried to study the impact of several economic variables on stock

prices, including exchange rates for eight advanced economies. He however

failed to detect any significant impact of exchange rates over stock prices.

6

Ma & Kao (1990) found evidence of significant positive relationship between

the two and supported the traditional Goods Market approach for six major

advanced industrial economies.

Using a Multi County approach, Smith (1992) employing linear regression

techniques examined the relationship in two separate papers, once for US,

Japan and Germany and once for the UK. In all cases, he found that equity

values had a significant effect on the value of exchange rates.

When the above mentioned studies were conducted, techniques of

Cointegration to study relationship between variables had not been developed

adequately and thus were not employed in the analysis. Also, they worked

with either monthly or quarterly data.

Oskooee & Sohrabian (1992) employed cointegration and Granger Causality to

study the relationship between S&P 500 and US Dollar. While failing to

discover any significant long run relationship between the two, they

discovered bidirectional causality in the short run.

Ajayi & Mougoue (1996), using cointegration techniques were able to establish

a significant relationship short run and long run relationship for eight advanced

economies. However, the variables interacted differently over the periods,

7

displaying a negative relationship in the short run, while a positive one in the

long run.

Using similar techniques Abdalla & Murinde (1997) investigated the causal

linkages between leading prices of foreign exchange market and stock markets

for India, Korea, Pakistan and the Philippines. They found unidirectional

causality from exchange rates to stock prices for all the countries except for

the Philippines.

Ajayi, Friedman & Mehdian (1998) studied seven advanced market economies

and eight emerging market economies to explore the relationship between

exchange rates and stock returns. They found Granger Causality between the

stock and currency markets in all the advanced economies, but found no

consistent causal relationship in emerging markets.

Granger, Huang and Yang (2000) tried to determine the appropriate Granger

Causality relation between stock prices and exchange for nine East Asian

economies, with a special focus on the relationship during the Asian Financial

crisis. They were able to establish a significant relationship for all cases, except

two. However, the direction and lead lag relationships were found to be

different for different countries.

8

Nieh & Lee (2001), using cointegration tests did not find any long run

relationship between the stock prices and exchange rates for the G-7

countries. However, they did find bidirectional relationship between the two

variables for the sample countries.

Employing similar methodology, several studies have been undertaken since

for individual countries, like Mansor h. Ibrahim (2000) for Malaysia, Ying Wu

(2000) for Singapore, Nath & Samanta (2003) for India, Aydemir & Demirhan

(2009) for Turkey and Kutty (2010) for Mexico. Mansor, while failing to

establish any long term relationship in the bivariate model, found a long term

relationship when model was extended to include Broad Money and reserves.

He also found a significant short run impact of the macroeconomic variables on

the stock prices. Wu, although establishing a significant long term relationship,

discovered different directions in the relationship for different periods. Nath

and Samanta did not find any strong causal influence from stock market return

to foreign exchange market return. Aydemir & Demirhan found bidirectional

causality between exchange rate and stock market indices. Kutty, although did

not discover any long term relationship for the two markets, found a causal

relationship, with stock prices leading exchange rates.

9

Uddin & Rahman (2009) undertook a similar study for India, Bangladesh and

Pakistan. But in all the three cases, they found neither a long term, nor a short

term relationship between stock prices and exchange rates.

In general, there is very weak evidence for any long term relationship between

stock prices and exchange rates. However, most of the studies did find

evidence for short run relationship between the two, though there is a lack of

consistency in the direction of causality between the two.

Significant work has been done on the theory and application of Granger

Causality tests by Granger (1969), Gupta (1987), MacDonald & Kearney (1987),

Shoesmith (1992) and Reimers & Helmut (1992), which shall be useful in the

application of appropriate tests.

SECTION III

METHODOLOGY

Our objective is to establish whether stock prices causally affect exchange

rates or the other way round. We use Cointegration, Vector Error Correction

Models (VECM) and Granger Causality to test the relationship between the

Stock Prices and Exchange Rates in a bivariate model. First we check the data

for stationarity using Augment Dickey Fuller (ADF) and Phillips-Perron (PP)

Tests. Optimal lags shall be calculated using Schwarz Information Criterion

10

(SIC). If the series are found to be stationary, we will straight away apply the

Granger Causality test. But existing financial research indicates that the spot

prices for the two variables are unlikely to be displaying stationarity and points

towards the presence of unit root. Thus if ADF and PP test find the data to be

non-stationary, we will proceed to testing for cointegration after determining

the number of roots. Determining cointegrating relationship is important to

establish the existence of long run equilibrium relationship. Even though

majority of the existing literature points against the existence of any long term

relationship, some researchers, namely Ajayi & Mougoue (1996) and Mansor

(2000) did find some evidence in favour of a long term relationship. To test for

cointegration we shall employ the Johansen approach. If the series display

cointegration, then an error correction term is required and hence we will

apply VECM to find the causality between the two variables as it can capture

both long term and short term relationships. To this end, we shall estimate the

following equations.

∆�� = �� + Σ �� Δ �� + Σ �� Δ �� + �� + ��

Δ�� = �� + Σ �� Δ �� + Σ �� Δ�� + �� + ��

Where �� and �� are error correction terms, which capture previous

periods disequilibrium between stock prices and exchange rates. The

11

terms, �� and �� are stationary random processes included to capture the

information not contained in the lagged terms.

If however, there is no evidence of cointegration, which past works indicate

has been the case for majority of the countries, we shall apply Granger

Causality test to establish the causal relationship. But, before applying the

Granger Causality test, we need to difference the series since they would be

displaying non-stationarity and differencing would induce stationarity into the

data. Failure to do this would lead to spurious regression. Thus we shall

estimate the following equations.

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ��

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ��

The optimal lag length shall be selected by choosing the lag that yields the

lowest SIC.

Then, we perform robustness checks by including more variables in the

specification and work in a multivariate model to see whether they lead to any

significant changes in the results previously obtained. The variables which we

will include to test the robustness are: the US stock market and rate

differential with the US overnight call rate. These variables have been chosen

upon their ability to impact both the stock markets and the exchange rates.

12

SECTION IV

DATA

To undertake our study from the start of the 1999 financial year to the end of

the 2009 financial year, that is from 31st

March 1999 to 31st

March 2010. Our

sample consists of a total of eight countries, four emerging economies and four

advanced economies. The four emerging economies being studied have been

chosen for them having a floating exchange rate and their importance in the

global economy. This obviously leaves out China since the value of its currency

Renminbi is unofficially pegged to the USD. We undertake our analysis for

India, Brazil, the Philippines and South Korea. The advanced economies under

consideration are also the major currency economies, namely, Australia,

Canada, Japan and the United Kingdom. The advanced economy, conspicuous

by its absence is the US. It was dropped since we are using its currency as the

reference currency, i.e. the currency in terms of which all others are being

expressed and also that its stock market and its overnight call rate is being

used as an exogenous variable when we check for the robustness of our

model. Also, none of the advanced economies from the Euro zone have been

included for the obvious reason of them using a shared common currency. Our

analysis starts from 1999, since this was the year when Brazil deregulated its

currency, Brazilian Real. Also the global economy had finally recovered from

the Asian Financial Crisis by this time.

13

We required daily spot rates for exchange rates, stock prices and overnight call

rates for the period under consideration for the relevant countries. The

exchange rates being considered are Rupee (India), the Real (Brazil), Won

(South Korea), Peso (Philippines), AUD (Australia), CAD (Canada), Yen (Japan)

and GBP (United Kingdom). All the currencies are expressed in USD. The stock

markets being studied are BSE 100 (India), Bovespa (Brazil), KOSPI 200 (South

Korea), PSEi (Philippines), S&P/ASX 200 (Australia), S&P/TSX Composite Index

(Canada), NIKKEI 225 (Japan) and FTSE 100 (United Kingdom). It is important to

mention here that the pound is the only currency which instead of being listed

as pounds per dollar, is listed as dollars per pound. Thus to maintain

consistency with other currencies under study, we take inverse of the listed

price to get pounds per dollar. Lastly we use the S&P 500 Composite and the

US Call Money middle rate as exogenous variable to check the robustness of

our model. All the data was readily available and was obtained from

Datastream.

A small note on the interpretation of exchange rate is worth mentioning. An

appreciation of the currency implies a reduction in the exchange rate and

depreciation in the currency implies an increase in the exchange rate.

14

SECTION V

EMPIRICAL PROCEDURE AND EVIDENCE

STATIONARITY

We start our analysis by checking for stationarity and determining the order of

integration. To this end, we employ Augmented Dickey-Fuller (ADF) test and

Phillips-Perron (PP) test. The optimal lag length is determined using Schwarz

Information Criterion (SIC).

The results obtained from stationarity tests are provided in table 1. The results

indicate the presence of non stationarity and the integration of the order of

one (i.e. I(1)) in all cases for both stock prices and exchange rates. The t-

statistics provided in the table indicates towards the failure to reject the null

hypothesis of the presence of unit for both stock prices and exchange rates.

Then we take the first difference of both the variables and check for

stationarity. In this case, we do reject the null hypothesis of the presence of

unit root. This indicates the presence of integration of the order of one.

With the unit root property established, we shall proceed towards testing for

cointegration.

15

Table 1, Unit Root Test Results (1999-2010)

INDIA BRAZIL SOUTH KOREA PHILIPPINES

Variable ADF PP ADF PP ADF PP ADF PP

S -0.38112 -0.33865 -0.0504 0.162903 -1.00659 -0.95915 -0.77231 -0.58529

X -1.81855 -1.89331 -1.48704 -1.39886 -1.6169 -1.8962 -1.92259 -1.91353

∆S -49.3132 -49.2562 -55.7237 -56.0428 -53.1706 -53.2067 -48.1056 -48.0749

∆X -53.8788 -53.9251 -48.8105 -48.6496 -13.9939 -41.8738 -52.6631 -52.6828

AUSTRALIA CANADA JAPAN UNITED KINGDOM

Variable ADF PP ADF PP ADF PP ADF PP

S -1.29333 -1.20457 -1.53271 -1.45107 -1.60513 -1.53505 -1.80706 -1.95537

X -0.87974 -0.79764 -0.69913 -0.72097 -1.59204 -1.47647 -1.47091 -1.40981

∆S -55.9915 -56.2166 -55.0831 -55.214 -54.6449 -54.7419 -35.516 -56.875

∆X -55.1429 -55.2112 -53.566 -53.5724 -56.1819 -56.2353 -50.1101 -50.0086 The critical values are :

1% level = -3.4324, 5% level = -2.8623, 10% level = -2.5672 ∆ = 1

st difference, S = Stock Prices, X = Exchange Rates

COINTEGRATION

Cointegration analysis is used to test whether there exists a long term

relationship between the two variables which contain a unit root. For this, we

apply Johansen approach. The reason for using Johansen approach is that it is

able to accommodate more than two variables, unlike Engle-Granger Two step

method. Though right now we are testing for cointegration between only two

variables, later on, to test the robustness of our results we shall work with a

multivariate model, which would necessitate the use of Johansen approach.

The Johansen approach employs two methods to determine the existence of

cointegrating relationships via the rank of the matrix, namely trace statistic

and maximum eigenvalue statistic. We tested the two series for cointegration

under this approach using different lag lengths, from a lag of one day to five

16

days, i.e., one trading week. The results for the tests based on trace statistic

and maximum eigenvalue statistic are provided in table 2 and table 3

respectively. The results indicate the absence of any cointegrating relationship

between the stock prices and exchange rates for all economies under

consideration, save for the Philippines. In other words, the tests indicate that

there is no long-run equilibrium relationship between stock prices and

exchange rates for all economies except for the Philippines. This is consistent

with existing literature, with majority of previous research providing evidence

against the presence of any long term equilibrium relationship. In the case of

the Philippines, we detect the presence of one cointegrating relationship.

Consequently, we need to include an error correction term while testing for

Granger Causality only in the case of the Philippines and run a Vector Error

Correction Model (VECM). For rest of the cases, where the variables are non-

cointegrated we shall use standard Granger Causality Tests.

17

Table 2, Johansen Cointegration Test Results (Trace statistic) (1999-2010)

INDIA

null

hypothesis

alternate

hypothesis L=1 L=2 L=3 L=4

critical

value

r=0 r>= 0 7.85861 7.5913 7.60785 7.60171 20.2618

r>1 r>=2 1.044673 1.09098 2.07668 2.36960 9.16454

Trace test indicates no cointegration at the 0.05 level

BRAZIL

null

hypothesis

alternate


critical

value

r=0 r>= 0 9.629284 10.0241 10.6006 10.4491 20.2618

r>1 r>=2 3.515322 3.76453 4.47103 4.45527 9.16454


SOUTH

KOREA

null

hypothesis

alternate


critical

value

r=0 r>= 0 4.440368 4.14035 4.07438 4.17672 20.2618

r>1 r>=2 1.790409 1.51235 1.27187 1.25769 9.16454


PHIL.

null

hypothesis

alternate


critical

value

r=0 r>= 0 20.96146 21.8834 22.7168 23.3746 20.2618

r>1 r>=2 4.218562 4.39655 4.62164 4.51412 9.16454

Trace test indicates 1 cointegrating equation(s) at the 0.05 level

AUSTRALIA

null

hypothesis

alternate


critical

value

r=0 r>= 0 5.18152 4.87290 4.78424 4.62984 20.2618

r>1 r>=2 1.38833 1.31234 1.30890 1.32808 9.16454


CANADA

null

hypothesis

alternate


critical

value

r=0 r>= 0 8.618836 8.07410 7.92762 7.74851 20.2618

r>1 r>=2 1.967126 1.92769 1.84467 1.84338 9.16454


JAPAN

null

hypothesis

alternate


critical

value

r=0 r>= 0 5.307801 5.21551 5.23006 5.2284 20.2618

r>1 r>=2 2.196572 2.08611 2.07255 2.07352 9.16454


UNITED

KINGDOM

null

hypothesis

alternate


critical

value

r=0 r>= 0 7.070676 6.77888 6.11631 6.43755 20.2618

r>1 r>=2 2.318772 2.26452 2.03519 1.99598 9.16454


18

Table 3, Johansen Cointegration Test Results (Maximum eigenvalue statistic) (1999-2010)

INDIA

null

hypothesis

alternate


critical

value

r=0 r>= 0 6.81394 6.50031 5.53117 5.23210 15.8921

r>1 r>=2 1.04467 1.09098 2.07668 2.36960 9.16454

Max-eigenvalue test indicates no cointegration at the 0.05 level

BRAZIL

null

hypothesis

alternate


critical

value

r=0 r>= 0 6.11396 6.25958 6.12959 5.99387 15.8921

r>1 r>=2 3.51532 3.76453 4.47103 4.45527 9.16454


SOUTH

KOREA

null

hypothesis

alternate


critical

value

r=0 r>= 0 2.64995 2.628 2.80251 2.91903 15.8921

r>1 r>=2 1.79040 1.51235 1.27187 1.25769 9.16454


PHIL.

null

hypothesis

alternate


critical

value

r=0 r>= 0 17.4868 16.7429 18.0951 18.8605 15.8921

r>1 r>=2 4.39655 4.21856 4.62164 4.51412 9.16454

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level

AUSTRALIA

null

hypothesis

alternate


critical

value

r=0 r>= 0 3.79319 3.56055 3.47533 3.30176 15.8921

r>1 r>=2 1.38833 1.31234 1.30890 1.32808 9.16454


CANADA

null

hypothesis

alternate


critical

value

r=0 r>= 0 6.65171 6.14640 6.08295 5.90513 15.8921

r>1 r>=2 1.96712 1.92769 1.84467 1.84338 9.16454


JAPAN

null

hypothesis

alternate


critical

value

r=0 r>= 0 3.11122 3.12940 3.15750 3.15488 15.8921

r>1 r>=2 2.19657 2.08611 2.07255 2.07352 9.16454


UNITED

KINGDOM

null

hypothesis

alternate


critical

value

r=0 r>= 0 4.75190 4.51436 4.08111 4.44157 15.8921

r>1 r>=2 2.31877 2.26452 2.03519 1.99598 9.16454


19

GRANGER CAUSALITY

Now we run Granger Causality tests for the seven economies not displaying

cointegration to establish a causal relationship between the stock prices and

exchange rates. Granger approaches the question of whether X causes Y by

looking at how much of current Y can be explained by past values of Y and then

by checking whether adding the lagged values of X can improve the results. X is

said to Granger Cause Y if X helps in predicting Y and Y is said to Granger Cause

X if Y helps in predicting X. It is very important to mention that saying that X

Granger Causes Y does not mean that Y is the direct result of X. Rather Granger

Causality simply implies a chronological ordering of movement in the series.

Granger causality test requires the series to be stationary; else we would be

running a spurious regression. We have already established the unit root

property of the series. Thus to induce stationarity, we shall take first difference

of the series. Therefore we shall now be trying to find causality between

changes in stock prices and changes in exchange rate. To do so, we shall

estimate the following equations.

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + �� (1)

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + �� (2)

20

The lag length is selected by choosing the model which yields the optimal level

of SIC.

For India, South Korea and United Kingdom, the optimal number of lags to test

for Granger Causality came out to be four days. While for Brazil and Canada,

the optimal number of lags turned out to be three days. In case of Australia,

the optimal number of lag came out to be just one day. Lag length determined,

we proceed to estimation of the aforementioned equation.

The next step is to finally test for Granger Causality, i.e. whether Stock Returns

Granger Cause Exchange Returns and whether Exchange Returns Granger

Cause Stock Return. For this we test the joint significance of the coefficients of

the lagged variables of the other series. To do this, we run a Wald Test to test

the joint significance of

�� = �� = �� = … … = �� = 0

in equation (1), and the joint significance of

�� = �� = �� = … … = �� = 0

in equation (2).

21

The f-statistics reported by the Wald Test are presented in table 6. If the f-

statistic is significant, then the coefficients are said to be jointly significant and

one variable is said to Granger Cause the other.

From the results presented in appendix A.1, we find evidence for bidirectional

causality for India, Brazil, Japan and South Korea between changes in stock

prices and changes in exchange rate. On the other hand, for Australia, we find

unidirectional causality from changes in exchange rate to change in stock price.

For Canada, there is evidence for unidirectional causality with changes in stock

price causing changes in exchange rate. Finally in the case of the United

Kingdom, we find evidence neither for bidirectional nor unidirectional causality

between changes in stock price and changes in exchange rate.

Now we move on to the important step of checking the robustness of our

model by including other exogenous variables. We undertake this task by

experimenting with different specifications of the model and observe whether

they have an impact on the results we obtained for causality.

It is well known that interest rates are closely related to exchange rates and

stock prices. The reason is very obvious. Interest rates are extremely important

in determining global capital flows. High interest rates attract capital inflows

which lead to an appreciation in the currency of the host economy and a

22

depreciation in the currency of the source economy. Secondly, interest rates

also impact the stock markets profoundly. Low lending rates create liquidity,

which provides easy money to be invested in the stock market. Also low

interest rates reduce the capital cost of business projects and thus increase the

revenue earned on projects apart from making previously unviable projects,

viable. All this impacts the stock market positively.

Thus, we include the difference between the domestic overnight call money

rate and the overnight call money rate for the US as an exogenous variable

while performing the Granger Causality test. The difference of the rates is

taken since only the difference is able to capture the relative profitability of

investing in either the host or the source economy. We take the difference

from the US rate in appreciation of the size and importance of the US economy

and it being an important source global capital flows. Also, since all the

currencies are expressed in terms of the USD, the case for the inclusion of the

US rate seems a pretty obvious one.

Next we include the S&P 500 Composite Index as another exogenous variable.

This US index is included for the ability of the US stock market in exerting

influence on the stock indices all over the world. The point about importance

of the US economy need not be stressed again.

23

But before including these variables, we must check them for stationarity. The

results are provided in the appendix A.3. As we can see, the S&P 500

Composite index contains a unit root. Thus to include the S&P index in the

Granger Causality, we induce stationarity by taking first difference of the series

since the test requires the variables to be stationary. The interest rate

difference on the other hand throws up different results. We find it to be

stationary in the case of India and Brazil. While we find it to contain a unit root

for South Korea, the Philippines, Australia, Canada, Japan and The United

Kingdom. Since the variables need to be stationary, for the cases where we

find it to be containing a unit root, we take first difference of the series.

Now we proceed to the ‘experimentation’. To do this we run our regression

again, we three different specifications. First we run it by including both the

interest rate difference (∆��) and the S&P index (∆�&�� ).

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ∆�&�� + ∆�� + �� (3)

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ∆�&�� + ∆�� + �� (4)

Then we run it by including only the S&P index.

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ∆�&�� + �� (5)

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ∆�&�� + �� (6)

24

Lastly, we run the regression by including only the interest rate differential.

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ∆�� + �� (7)

Δ�� = �� + Σ ��Δ�� + Σ ��Δ�� + ∆�� + �� (8)

In the cases where the interest rate difference is stationary, regress ��

instead of ∆��.

After obtaining the results for the Granger Causality for all the three

specifications (which are provided in the appendix A.1 along with the case

when no exogenous variables where included), we compare the results with

the previously obtained results where none of the exogenous variables were

included. We check whether the inclusion of the exogenous variables leads to

any significant change in our results. Finally, we choose that specification

which throws up the most appropriate results of the four different

specifications. The results for the VAR estimation are provided in appendix A.2.

The final results for Granger Causality with the appropriate specification are

provided in table 4. In case of India, there is not any significant difference in

the f-statistics provided by the Granger Causality test by running the difference

specifications. We still find evidence of bidirectional causality between changes

in stock price and changes in exchange rate. In the case of Brazil, we do

observe the f-statistics changing significantly by the inclusion of the exogenous

25

variables, but again our conclusion of bidirectional causality stands. Like the

case of India, for South Korea too, the f-statistics hardly change and the

evidence of bidirectional causality is validated. In the case of Australia, while

previously we had detected unidirectional causality only with exchange rate

changes causing changes in stock price, we now get different results under

different specifications. When we include both exogenous variables, we fail to

detect any causality whatsoever. Same holds true when we include only S&P

index. Interestingly, when we include only interest rate differential, we find

evidence of unidirectional causality, but now from changes in stock price to

changes in exchange rate. For Canada, the conclusion over unidirectional

causality running from changes in stock price to changes in exchange rate

stands, even though the f-statistics change considerably. The Japanese

economy had displayed bidirectional causality and the inclusion of both

exogenous variables does not change that conclusion while changing the f-

statistics slightly. However when we included only the interest rate difference,

the evidence for causality running from changes in exchange rate to changes in

stock price becomes tenuous, and leads to its rejection. Finally for the United

Kingdom, the conclusion of the absence of any causality stands, even though

the f-statistics do change a bit.

26

Overall it is safe to say that the inclusion of exogenous variable does not have

much impact upon our conclusion for Granger Causality except for the cases of

Australia and Japan.

The next step would be to check the model for econometric problems, mainly

for autocorrelation and structural breaks. To test for autocorrelation, we

perform Autocorrelation Lagrange Multiplier (LM) test upto the 4th

order. The

results are provided in the appendix A.4. India is the only economy for which

we find evidence of autocorrelation of all orders upto four. For Brazil,

Australia, Canada and Japan we don’t find any evidence for any order of

autocorrelation. For South Korea and The United Kingdom, we do find some

evidence of autocorrelation for some orders.

We test for structural break arising out of the current financial crisis using the

Chow Breakpoint test. To do this we test for structural breaks around the date

August 16th

2007 when the crisis had formally hit the foreign exchange market.

The Chow Breakpoint test divides the data into subsamples around the

specified date and checks for the stability of parameters. The results for the

Chow Breakpoint tests are provided in appendix A.5. The tests throw up mixed

results about the stability of the parameters. The only economies to observe

stable parameters before and after the recession for both directions of

causality are Australia and Japan (when interest rate difference is included).

27

While at the other end of the spectrum, Brazil, Canada and The United

Kingdom observe unstable parameters for both directions of causality. India

and South Korea throw up mixed results with either of the causal relationships

displaying instability in parameters. This result is very crucial. It necessitates

the carrying out of our exercise of establishing the relationship between stock

prices and exchange rates specifically for the period of the recession, that is,

from 16th

August 2007 onwards.

28

Table 4, Granger Causality Conclusion (1999-2010)

COUNTRY NULL HYPOTHESIS H0 f-stat critical f-value RESULT

INDIA a ∆St does not Granger Cause ∆Xt 3.88011 F(4,2862) = 2.37 Reject H0

∆Xt does not Granger Cause ∆St 110.630 F(4,2862) = 2.37 Reject H0

BRAZIL b ∆St does not Granger Cause ∆Xt 4.7576 F(3,2863) = 2.6 Reject H0


SOUTH KOREA a ∆St does not Granger Cause ∆Xt 78.4755 F(4,2862) = 2.37 Reject H0


AUSTRALIA b ∆St does not Granger Cause ∆Xt 3.1557 F(1,2865) = 3.84 Fail to Reject H0

∆Xt does not Granger Cause ∆St 2.6694 F(1,2865) = 3.84 Fail to Reject H0

AUSTRALIA d ∆St does not Granger Cause ∆Xt 9.1239 F(1,2865) = 3.84 Reject H0


CANADA b ∆St does not Granger Cause ∆Xt 3.9972 F(3,2863) = 2.6 Reject H0


JAPAN b ∆St does not Granger Cause ∆Xt 7.0535 F(2,2864) = 3 Reject H0

∆Xt does not Granger Cause ∆St 3.0791 F(2,2864) = 3 Reject H0

JAPAN d ∆St does not Granger Cause ∆Xt 4.5035 F(2,2864) = 3 Reject H0

∆Xt does not Granger Cause ∆St 2.9795 F(2,2864) = 3 Fail to Reject H0

UNITED KINGDOM b ∆St does not Granger Cause ∆Xt 1.5255 F(4,2862) = 2.37 Fail to Reject H0


a - no exogenous variable

b - including both S&P 500 and interest rate difference

c - including S&P 500

d - including interest rate difference

29

VECTOR ERROR CORRECTION MODEL

As noted above, since stock prices and exchange rates are found to be

following I(1) processes and there exists a cointegrating relationship between

the two for the Philippines, we need to include an error correction term and

run a VECM to test for Granger Causality. Thus we run the following equation.

∆��. �� = �� + Σ �� Δ ��. �� + Σ �� Δ ��. �� + �� + �� (9)

ΔPHIL. �� = �� + Σ �� Δ ��. �� + Σ �� Δ��. �� + �� + ��(10)

The equation is specified in a manner such that it restricts the long run

behavior of variables to converge to their cointegrating relationships while

leaving room for short term adjustment dynamics. Here �� and �� are error

correction terms, where �� = (��. �� − & ��. ��) and �� =

(��. �� − (��. ��). Provided that ��. �� and ��. �� are

cointegrated with cointegration coefficient δ and η, then �� and �� will be

I(0), even though the constituents are I(1). The coefficients of error correction

terms, �� and ��, capture the adjustment of Δ ��. �� and Δ ��. ��

towards their long run equilibriums. While �� and �� , the coefficients of

Δ��. �� and Δ ��. �� capture their short term movements. The

terms, �� and �� are stationary random processes included to capture the

information which is not provided by the variables included in the model.

30

We choose the optimal lag length by running the model with different lag

length and choosing the model which throws up the lowest SIC, which was four

days. Then as we did in the previous cases, we test with four different

specifications. The results for the estimation of the VECM are provided in

appendix A.6. Finally we test for Granger Causality by running the Granger

Causality/Block Exogeneity Wald Test for all the four cases, whose results are

provided in table 5. In all the cases, we find evidence of unidirectional

causality, with changes in stock exchange return causing changes in exchange

rate. The result for autocorrelation test is provided in appendix A.3.

Table 5, Philippines VEC Granger Causality/Block Exogeneity Wald Test Results (1999-2010)

Specification NULL HYPOTHESIS H0 Chi2

stat Results

No exogenous variables included

∆St does not Granger Cause ∆Xt 290.2977 Reject H0

∆Xt does not Granger Cause ∆St 2.224357 Fail to reject H0

Including S&P 500

and Interest difference.



Including S&P 500.



Including Interest Difference.



critical value at 5% = 9.488 (4 d.f.)

31

RECESSIONARY IMPACT

The Chow test for structural breaks revealed the instability of parameters

when we broke down the data into pre and post recession. This necessitated

the testing of the causal relationship during the recession, which is from 16th

August 2007 onwards. To do this we undertook our whole exercise again, but

now on the subset of the whole data from 16th

August 2007 to 31st

March

2010. First we checked the variables for cointegration and the results are

provided in appendix A.7 and A.8. The results were same except for the

Philippines, where the previous evidence of cointegration broke down. This

breakdown in the long run relationship between the two prices might be

because of the instability experienced in the stock markets and currency

markets during the recession.

So now, we were unable to detect cointegration for neither of the economies

under consideration. Thus we proceeded to the estimation of the VAR whose

results are provided in appendix A.9. Then we tested for Granger Causality

under different specifications, as done previously, using four different

specifications. The results of that exercise are reported in appendix A.10.

The results for Granger Causality during the recession with appropriate

specification are provided in table 6. For India during the recession, we detect

unidirectional causality from changes in exchange rate to changes in stock

32

price. In the case of Brazil, when both exogenous variables are considered, we

fail to detect any causality whatsoever. While when we include only interest

difference, we find changes in stock price Granger Causing changes in

exchange price. South Korea and the Philippines display unidirectional causality

running from stock prices changes to changes in exchange rates. We fail to

detect any causality for Australia. The same is the case for Canada when we

include both exogenous variables. But when we include only interest

difference, we find evidence for stock price changes Granger Causing changes

in exchange rate. When we include both exogenous variables for Japan, we

find strong evidence for bidirectional causality. However when only interest

difference is considered, we find evidence only for changes in exchange rate

Granger Causing changes in stock price. Finally, for the United Kingdom, we

find evidence for stock price changes Granger Causing changes in exchange

rate when we include both the exogenous variables. However, when we

consider only interest rate difference we fail to detect any causality.

33

Table 6 , Granger Causality test results (2007-2010)

COUNTRY NULL HYPOTHESIS H0 f-stat critical f-value Result

INDIAb ∆St does not Granger Cause ∆Xt 0.885105 F(4,676) =2.37 Fail to Reject H0

∆Xt does not Granger Cause ∆St 30.77113 F(4,676) =2.37 Reject H0

BRAZILb ∆St does not Granger Cause ∆Xt 0.697377 F(3,675) = 2.6 Fail to Reject H0


BRAZILd ∆St does not Granger Cause ∆Xt 15.9489 F(3,675) = 2.6 Reject H0


S. KOREAa ∆St does not Granger Cause ∆Xt 35.27671 F(4,676) =2.37 Reject H0

∆Xt does not Granger Cause ∆St 1.939398 F(4,676) =2.37 Fail to Reject H0

PHIL.a ∆St does not Granger Cause ∆Xt 79.08529 F(2,679) = 3 Reject H0

∆Xt does not Granger Cause ∆St 0.691745 F(2,679) = 3 Fail to Reject H0

AUSTRALIAb ∆St does not Granger Cause ∆Xt 0.239587 F(1,681) =3.84 Fail to Reject H0


CANADAb ∆St does not Granger Cause ∆Xt 1.947463 F(3,675) = 2.6 Fail to Reject H0


CANADAd ∆St does not Granger Cause ∆Xt 1.947463 F(3,675) = 2.6 Reject H0


JAPANb ∆St does not Granger Cause ∆Xt 3.147687 F(2,679) = 3 Reject H0


JAPANd ∆St does not Granger Cause ∆Xt 1.258694 F(2,679) = 3 Fail to Reject H0


UKb ∆St does not Granger Cause ∆Xt 5.964761 F(4,676) =2.37 Reject H0


UKd ∆St does not Granger Cause ∆Xt 1.518974 F(4,676) =2.37 Fail to Reject H0

∆Xt does not Granger Cause ∆St 0.305808 F(4,676) = .37 Fail to Reject H0

a – no exogenous variables.

b - Including S&P and Interest difference.

c – Including S&P.

d- Including Interest difference

34

SECTION VI

CONCLUSION

In this study we examined the interaction between stock prices and exchange

rates for India, Brazil, South Korea, the Philippines, Australia, Canada, Japan

and the United Kingdom. We used Cointegration, Granger Causality tests and

Error Correction model techniques to establish the relationship. The first step

was to test our data for stationarity using ADF and PP tests. All the data came

out to be containing a unit root. The next step was to apply Johansen

Cointegration techniques to check for the existence of any long run

relationship between the two variables. Save for the Philippines, we were

unable to detect any evidence for any long run relationship between the two

variables for none of the economies. The absence of any cointegrating

relationship led us to apply Granger Causality tests for all the economies,

except for the Philippines. In the case of the Philippines, we constructed a

Vector Error Correction model and then tested for Granger Causality.

The Traditional Goods & Market approach to the relationship predicts that

changes in exchange rates lead changes in stock prices. On the other hand,

according to the Portfolio Balance Approach, changes in stock prices cause

changes in exchange rates. The results obtained present a mixed picture on the

validation of either of the approaches to the relationship. The presence of

35

unidirectional causality, with changes in Stock prices causing changes in

Exchange rates for the Philippines, Australia, Canada and Japan, present

support for the Portfolio Balance approach. However, the evidence of

bidirectional causality or feedback relationship for India, Brazil and South

Korea and the absence of any relationship for the United Kingdom contradicts

that conclusion and offers no resolution over the debate over the validity of

either approach to the relationship. These results are consistent with the ones

obtained by Solnik (1987) in the sense we have failed to detect any significant

impact of exchange markets on stock markets. One conclusion we can make is

that the economies which display unidirectional causality with changes in stock

prices leading changes in exchange rates are all advanced economies, except

for the Philippines. As noted before, the Portfolio Balance approach stresses

on the flow of global capital flows. Thus, we may say that the Portfolio Balance

approach is at work in economies which experience high Capital inflows and

outflows.

The degree of freedom from capital control and liberalization of external trade

along with the relative dominance of global capital flows over foreign trade in

the different economies may account for the varying results we have obtained

for different countries.

36

Finally, we tested whether the conclusion reached above held during the

period of the recession. For the recession, we again find conflicting results.

India and Japan display unidirectional causality, with changes in exchange rates

leading stock prices and thus lending support to the traditional argument. On

the other hand, we find support for the Portfolio Balance approach, with

changes in stock prices causing changes in exchange rates for Brazil, South

Korea, the Philippines, Canada and the United Kingdom. We fail to detect any

relationship for Australia. But one must not read too much into the results

obtained for the period of the recession, due to the high volatility, instability

and government intervention in these markets during the recession.

In conclusion, although we find some evidence for the Portfolio Balance

approach for advanced economies, it is far from unequivocal. This is in line

with the existing literature, with no conclusive evidence in support for either of

the approach. This necessitates the undertaking of further deeper and

widespread studies into the debate.

37

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40

APPENDIX

A.1 , Granger Causality test results (1999-2010)

COUNTRY NULL HYPOTHESIS H0 f-stata

f-statb

f-statc

f-statd

critical f-value

INDIA ∆St does not Granger Cause ∆Xt 3.88011 3.8516 3.8269 3.9029 F(4,2862) =2.37

∆Xt does not Granger Cause ∆St 110.630 99.2359 99.3872 110.4665 F(4,2862) =2.37

BRAZIL ∆St does not Granger Cause ∆Xt 2.77639 4.7576 4.7291 22.8552 F(3,2863) = 2.6

∆Xt does not Granger Cause ∆St 22.8114 2.8421 2.7878 2.8421 F(3,2863) = 2.6

S. KOREA ∆St does not Granger Cause ∆Xt 78.4755 74.9773 75.0605 78.4077 F(4,2862) =2.37


AUSTRALIA ∆St does not Granger Cause ∆Xt 0.81785 3.1557 3.159 9.1239 F(1,2865) =3.84


CANADA ∆St does not Granger Cause ∆Xt 8.19099 3.9972 2.0722 8.1182 F(3,2863) = 2.6


JAPAN ∆St does not Granger Cause ∆Xt 4.49667 7.0535 7.0576 4.5035 F(2,2864) = 3

∆Xt does not Granger Cause ∆St 3.00 3.0791 3.0898 2.9795 F(2,2864) = 3

UK ∆St does not Granger Cause ∆Xt 0.31162 1.5255 1.5254 0.7654 F(4,2862) =2.37





d- Including Interest difference.

41

A.2, results for VAR estimation (1999-2010). Standard errors in ( ) & t-statistics in [ ]

COUNTRY INDIAa

BRAZILb

SOUTH KOREAa

DEPENDENT VARIABLE ∆St ∆Xt ∆St ∆Xt ∆St ∆Xt

C 2.904839 0.000565 58.39044 0.000803 0.047508 0.050350

(1.62161) (0.00292) (36.6210) (0.00135) (0.14002) (0.04568)

[ 1.79133] [ 0.19317] [ 1.59445] [ 0.59429] [ 0.339 28] [ 1.10221]

∆St-1 0.039106 -8.95E-05 -0.09409 -2.96E-06 0.191065 -0.0022

(0.01869) (3.4E-05) (0.02370) (8.7E-07) (0.01874) (0.00611)

[ 2.09263] [-2.65693] [-3.97007] [-3.38146] [ 10.19 72] [-0.3590]

∆St-2 0.018283 -6.02E-06 -0.03467 9.27E-07 -0.0834 0.020456

(0.01751) (3.2E-05) (0.01987) (7.3E-07) (0.01898) (0.00619)

[ 1.04400] [-0.19056] [-1.74496] [ 1.26439] [-4.395 0] [ 3.30443]

∆St-3 -0.02477 7.03E-05 -0.08212 -6.61E-07 -0.10971 -0.00501

(0.01750) (3.2E-05) (0.01976) (7.3E-07) (0.01900) (0.00620)

[-1.41579] [ 2.22891] [-4.15631] [-0.90694] [-5.773 4] [-0.8076]

∆St-4 -0.01518 5.27E-05

0.047471 -0.00351

(0.01746) (3.1E-05)

(0.01775) (0.00579)

[-0.86936] [ 1.67328]

[ 2.67450] [-0.6066]

∆Xt-1 -6.99105 -0.00911 -226.09 0.053326 -0.98154 0.007054

(10.3772) (0.01871) (529.553) (0.01954) (0.05757) (0.01878)

[-0.67370] [-0.48680] [-0.42695] [ 2.72938] [-17.04 8] [ 0.37554]

∆Xt-2 -44.6091 -0.02508 -1240.35 -0.0372 -0.20102 -0.02066

(10.3674) (0.01869) (529.176) (0.01952) (0.06044) (0.01972)

[-4.30283] [-1.34169] [-2.34393] [-1.90550] [-3.325 8] [-1.0477]

∆Xt-3 -213.778 0.007535 -812.55 -0.02935 -0.1764 0.021794

(10.3961) (0.01874) (519.811) (0.01918) (0.06051) (0.01974)

[-20.5633] [ 0.40202] [-1.56316] [-1.53060] [-2.915 3] [ 1.10412]

∆Xt-4 -27.8882 0.021506

-0.17502 -0.00688

(11.1066) (0.02002)

(0.06054) (0.01975)

[-2.51097] [ 1.07403]

[-2.8908] [-0.3482]

∆S&Pt-1

3.710908 -0.00022

(1.05227) (3.9E-05)

[ 3.52657] [-5.56103]

It-1

-2.44006 -5.42E-05

(2.52929) (9.3E-05)

[-0.96472] [-0.58104] a – no exogenous variables.

b – Including S&P and Interest difference.


d – Including Interest difference.

42

A.2 cont. Standard errors in ( ) & t-statistics in [ ]

COUNTRY AUSTRALIAb

AUSTRALIAd

CANADAb


C 0.812537 -0.00018 0.720468 -0.00017 -0.00017 2.127057

(0.71545) (0.00022) (0.84775) (0.00023) (0.00013) (2.27844)

[ 1.13571] [-0.8067] [ 0.8496] [-0.7079] [-1.3302] [ 0.93356]

∆St-1 -0.0962 -9.14E-06 -0.05022 -1.62E-05 -0.03749 -627.602

(0.01659) (5.1E-06) (0.01960) (5.4E-06) (0.01969) (348.309)

[-5.7975] [-1.7764] [-2.5628] [-3.0205] [-1.9041] [ -1.8018]

∆St-2

0.025159 -359.736

(0.01964) (347.370)

[ 1.28116] [-1.0356]

∆St-3

-0.01626 137.4635

(0.01954) (345.563)

[-0.8324] [ 0.39780]

∆St-4

∆Xt-1 -98.3392 -0.04202 -64.2306 -0.04729 1.38E-06 -0.16168

(60.1886) (0.01866) (71.3098) (0.01957) (1.5E-06) (0.02579)

[-1.6338] [-2.2517] [-0.9007] [-2.4164] [ 0.94445] [-6.2696]

∆Xt-2

-1.22E-06 -0.04016

(1.1E-06) (0.01985)

[-1.0862] [-2.0228]

∆Xt-3

-3.55E-06 0.011981

(1.1E-06) (0.01966)

[-3.1897] [ 0.60932]

∆Xt-4

∆S&Pt-1 1.647430 -0.00026

-7.40E-05 1.472058

(0.04840) (1.5E-05)

(1.2E-05) (0.21289)

[ 34.0392] [-16.968]

[-6.1499] [ 6.91470]

∆It-1 1.562180 -0.00077 1.735522 -0.0008 -0.00102 -9.86579

(2.77886) (0.00086) (3.29277) (0.00090) (0.00091) (16.1015)

[ 0.56216] [-0.8927] [ 0.5270] [-0.8807] [-1.1202] [-0.6127]

43


COUNTRY JAPANb

JAPANd

UNITED KINGDOMb


C -0.01025 -1.54952 -0.01057 -1.71908 -0.24701 9.93E-06

(0.01303) (3.09539) (0.01339) (3.49721) (1.13112) (6.9E-05)

[-0.7867] [-0.5005] [-0.7895] [-0.4915] [-0.2183] [ 0.14464]

∆St-1 -0.03783 10.05808 -0.03464 11.72662 -0.25311 2.64E-06

(0.01896) (4.50491) (0.01948) (5.08928) (0.01996) (1.2E-06)

[-1.9953] [ 2.23269] [-1.7780] [ 2.30418] [-12.683] [ 2.17587]

∆St-2 -0.01866 5.341871 -0.01998 4.647578 -0.05041 -1.77E-07

(0.01892) (4.49608) (0.01945) (5.07966) (0.01751) (1.1E-06)

[-0.9862] [ 1.18812] [-1.0275] [ 0.91494] [-2.8792] [-0.1664]

∆St-3

-0.06595 1.23E-06

(0.01752) (1.1E-06)

[-3.7642] [ 1.15501]

∆St-4

0.034558 3.05E-08

(0.01754) (1.1E-06)

[ 1.96992] [ 0.02865]

∆Xt-1 -0.00026 -0.07216 -0.00018 -0.03309 235.4590 0.060695

(7.3E-05) (0.01733) (7.5E-05) (0.01952) (306.661) (0.01862)

[-3.5386] [-4.1636] [-2.4562] [-1.6952] [ 0.76781] [ 3.26035]

∆Xt-2 -9.54E-05 -0.00699 -0.00013 -0.02619 136.8260 -0.00915

(7.3E-05) (0.01726) (7.5E-05) (0.01948) (306.923) (0.01863)

[-1.3143] [-0.4052] [-1.7706] [-1.3442] [ 0.44580] [-0.4912]

∆Xt-3

-3.82606 -0.03766

(306.926) (0.01863)

[-0.0124] [-2.0210]

∆Xt-4

-100.815 -0.01413

(306.415) (0.01860)

[-0.3290] [-0.7597]

∆S&Pt-1 0.011263 5.904487

1.744453 -3.28E-05

(0.00088) (0.20977)

(0.08739) (5.3E-06)

[ 12.7601] [ 28.1477]

[ 19.9616] [-6.1759]

∆It-1 0.012898 -10.4511 -0.02348 -29.5182 -4.24236 -1.57E-05

(0.09548) (22.6905) (0.09810) (25.6246) (3.51302) (0.00021)

[ 0.13508] [-0.4605] [-0.2392] [-1.1519] [-1.2076] [-0.0737]

44

A.3, unit root results (1999-2010)

Interest Difference ∆Interest Difference

Country ADF Country ADF

India -5.40373

South Korea -25.43416

Brazil -3.84683

Philippines -29.43486

South Korea -1.14404

Australia* -24.98059

Philippines -2.72894

Canada* -25.26396

Australia* -0.24682

Japan* -25.63841

Canada* -1.45844

United Kingdom* -28.13589

Japan* -1.47009

United Kingdom* -1.60152

ADF ADF

S&P 500 -1.81503 ∆S&P 500 -41.8996

The Critical Values are:

series containing an intercept but no trend

1% level = -3.4324 5% level = -2.8623, 10% level = -2.5672 *series without an intercept of trend

1% level = -2.56577 5% level = -1.940934 10% level = -1.61663

45

A.4, Autocorrelation LM Test Results (1999-2010) Null hypothesis, H0 : No Autocorrelation

Critical value for chi-square with 4 d.f.

at 1% = 13.277, 5% = 9.488 ,

*significant at 1%

a – No exogenous variables

b – Including S&P and interest diff.


d – Including interest diff.

Country Order LM-Stat Result

Indiaa 1 19.2572 Reject H0

2 22.4787 Reject H0

3 11.71892 Reject H0

4 16.53189 Reject H0

Brazilb 1 10.93675 Fail to Reject H0

*

2 5.913935 Fail to Reject H0



South 1 1.999343 Fail to Reject H0 Cntry. Order LM-stat Result

Koreaa 2 2.951343 Fail to Reject H0

Phil. 1

2

3

4

14.108

6.052

2.519

5.339

Reject

Fail to Reject

Fail to Reject

Fail to reject

3 13.4508 Reject H0

4 11.26928 Fail to Reject H0*

Australiab 1 55.52432 Reject H0

2 14.46430 Reject H0



Australiad 1 13.28761 Fail to Reject H0

*




Canadab 1 8.199857 Fail to Reject H0




Japanb 1 47.98933 Reject H0




Japand 1 3.769007 Fail to Reject H0




United 1 29.09026 Reject H0

Kingdomb 2 17.22139 Reject H0


4 28.24224 Reject H0

46

A.5, Chow Breakpoint Test Results (1999-2010)

Country Causality f-Stat Critical Values(5%) Result

Indiaa ∆St — ∆Xt 1.732977 F(9,2848)= 1.88 Fail to Reject H0

∆Xt — ∆St 4.92715 F(9,2848)= 1.88 Reject H0

Brazilb ∆St — ∆Xt 14.68836 F(7,2853)= 2.01 Reject H0

∆Xt — ∆St 4.713201 F(7,2853)= 2.01 Reject H0

South Koreaa ∆St — ∆Xt 20.30255 F(9,2848)= 1.88 Reject H0

∆Xt — ∆St 0.805434 F(9,2848)= 1.88 Fail to Reject H0

Australiab ∆St — ∆Xt 34.8627 F(3,2863)= 2.6 Reject H0

∆Xt — ∆St 31.19437 F(3,2863)= 2.6 Reject H0

Australiad ∆St — ∆Xt 0.935721 F(3,2863)= 2.6 Fail to Reject H0


Canadab ∆St — ∆Xt 2.803662 F(7,2853)= 2.01 Reject H0

∆Xt — ∆St 2.566381 F(7,2853)= 2.01 Reject H0

Japanb ∆St — ∆Xt 10.65451 F(5,2858)= 2.21 Reject H0

∆Xt — ∆St 3.364789 F(5,2858)= 2.21 Reject H0

Japand ∆St — ∆Xt 0.469108 F(5,2858)= 2.21 Fail to Reject H0


United Kingdomb ∆St — ∆Xt 8.626655 F(9,2848)= 1.88 Reject H0

∆Xt — ∆St 2.549293 F(9,2848)= 1.88 Reject H0

Null Hypothesis, H0: No breaks at specified breakpoints (16/8/2007)

a - no exogenous variable

b - including both S&P 500 and interest rate difference

c - including S&P 500

d - including interest rate difference

47

A.6, VECM Estimates for the Philippines (1999-2010)

Sample (adjusted): 4/07/1999 3/31/2010

Included observations: 2866 after adjustments

Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq: CointEq1

PESOt-1 1.000000

PSEIt-1 0.007749

(0.00172)

[ 4.50349]

C -65.8153

(3.69334)

[-17.8200]

Error Correction: ∆PESO ∆PSEI

CointEq1 -0.00294 0.119231

(0.00070) (0.10579)

[-4.19330] [ 1.12702]

∆PESOt-1 0.006194 -2.268547

(0.01866) (2.81874)

[ 0.33189] [-0.80481]

∆PESOt-2 -0.02779 -2.214883

(0.01865) (2.81737)

[-1.48998] [-0.78615]

∆PESOt-3 -0.03229 -1.552042

(0.01866) (2.81779)

[-1.73080] [-0.55080]

∆PESOt-4 -0.00687 2.001873

(0.01779) (2.68767)

[-0.38580] [ 0.74484]

∆PSEIt-1 -0.00211 0.105512

(0.00012) (0.01875)

[-16.9622] [ 5.62819]

∆PSEIt-2 9.04E-05 -0.010846

(0.00013) (0.01977)

[ 0.69051] [-0.54866]

∆PSEIt-3 -7.59E-05 -0.009365

(0.00013) (0.01977)

[-0.58027] [-0.47383]

∆PSEIt-4 0.000140 0.008397

(0.00013) (0.01967)

[ 1.07604] [ 0.42695]

48

A.7, Johansen Cointegration Test Results (Trace statistic) (2007-2010)

null

hypothesis

alternate


critical

value

INDIA r=0 r>= 0 7.28426 6.75659 6.8655 6.86050 20.2618

r>1 r>=2 0.94010 0.96865 0.9602 0.99852 9.16455


null

hypothesis

alternate


critical

value

BRAZIL r=0 r>= 0 10.6961 12.6339 12.557 11.0663 20.2618

r>1 r>=2 2.28847 2.32161 2.2445 2.14689 9.16455


null

hypothesis

alternate


critical

value

S. KOREA r=0 r>= 0 12.4659 11.7323 10.811 9.78403 20.2618

r>1 r>=2 1.71961 1.68362 1.8350 2.01463 9.16455


null

hypothesis

alternate


critical

value

PHIL. r=0 r>= 0 11.2114 12.3652 12.671 13.8722 20.2618

r>1 r>=2 3.80755 3.81330 4.3331 4.30695 9.16455


null

hypothesis

alternate


critical

value

AUST. r=0 r>= 0 8.46029 7.08010 7.0783 7.34520 20.2618

r>1 r>=2 1.66411 1.55727 1.6182 1.57860 9.16455


null

hypothesis

alternate


critical

value

CANADA r=0 r>= 0 11.5497 12.7451 12.267 13.9021 20.2618

r>1 r>=2 1.56667 1.58648 1.6246 2.07003 9.16455


null

hypothesis

alternate


critical

value

JAPAN r=0 r>= 0 8.21395 9.41419 9.3368 9.86616 20.2618

r>1 r>=2 2.22022 2.19047 2.3441 2.58167 9.16455


null

hypothesis

alternate


critical

value

UK r=0 r>= 0 7.26898 6.67626 6.9720 6.74144 20.2618

r>1 r>=2 1.96511 1.89057 1.6826 2.14580 9.16455


49

A.8, Johansen Cointegration Test Results (Maximum eigenvalue) (2007-2010)

null

hypothesis

alternate


critical

value

INDIA r=0 r>= 0 6.34415 5.78794 5.90530 5.86197 15.8921

r>1 r>=2 0.94010 0.96865 0.96027 0.99852 9.164546


null

hypothesis

alternate


critical

value

BRAZIL r=0 r>= 0 8.40765 10.3123 10.3132 8.91945 15.8921

r>1 r>=2 2.28847 2.32161 2.24458 2.14689 9.164546


null

hypothesis

alternate


critical

value

S. KOREA r=0 r>= 0 10.7463 10.0487 8.97597 7.76939 15.8921

r>1 r>=2 1.71961 1.68362 1.83506 2.01463 9.164546


null

hypothesis

alternate


critical

value

PHIL. r=0 r>= 0 7.40387 8.55189 8.33786 9.56530 15.8921

r>1 r>=2 3.80755 3.81330 4.33313 4.30695 9.164546


null

hypothesis

alternate


critical

value

AUSTRALIA r=0 r>= 0 6.79618 5.52283 5.46009 5.76659 15.8921

r>1 r>=2 1.66411 1.55727 1.61822 1.57860 9.164546


null

hypothesis

alternate


critical

value

CANADA r=0 r>= 0 9.98302 11.1586 10.6431 11.8320 15.8921

r>1 r>=2 1.56667 1.58648 1.62462 2.07003 9.164546


null

hypothesis

alternate


critical

value

JAPAN r=0 r>= 0 5.99372 7.22371 6.99269 7.28449 15.8921

r>1 r>=2 2.22022 2.19047 2.34416 2.58167 9.164546


null

hypothesis

alternate


critical

value

UK r=0 r>= 0 5.30387 4.78569 5.2894 4.59564 15.8921

r>1 r>=2 1.96511 1.89057 1.68260 2.14580 9.164546


50

A.9, Results for VAR estimation (2007-2010). Standard errors in ( ) & t-statistics in [ ]

COUNTRY INDIAb BRAZIL

b BRAZIL

d


C 21.81043 -0.04339 191.0293 -0.007457 460.4141 -0.01037

-11.9402 -0.02171 -151.535 -0.00442 -216.011 -0.00472

[ 1.8266] [-1.99888] [ 1.26063] [-1.68576] [ 2.1314 ] [-2.19880]

∆St-1 0.044058 -8.03E-05 0.030966 -6.79E-06 -0.0861 -5.52E-06

-0.03811 -6.90E-05 -0.03014 -8.80E-07 -0.0426 -9.30E-07

[ 1.1561] [-1.15938] [ 1.02737] [-7.71495] [-2.0212 ] [-5.93964]

∆St-2 0.030583 3.15E-05 -0.04232 1.69E-06 -0.08259 2.13E-06

-0.03487 -6.30E-05 -0.03114 -9.10E-07 -0.04444 -9.70E-07

[ 0.8770] [ 0.49748] [-1.35905] [ 1.86210] [-1.8585 ] [ 2.19361]

∆St-3 -0.0397 0.000112 -0.0766 -7.15E-07 -0.13667 -6.56E-08

-0.0348 -6.30E-05 -0.0309 -9.00E-07 -0.04402 -9.60E-07

[-1.1406] [ 1.76964] [-2.47935] [-0.79249] [-3.1046 ] [-0.06831]

∆St-4 -0.03312 7.80E-05

-0.03471 -6.30E-05

[-0.9542] [ 1.23679]

∆Xt-1 -14.6969 0.001171 265.7224 -0.189281 -1276.57 -0.17261

-21.1618 -0.03847 -1375.18 -0.04014 -1962.97 -0.04285

[-0.6945] [ 0.03045] [ 0.19323] [-4.71494] [-0.6503 ] [-4.02827]

∆Xt-2 -31.0139 -0.04796 -3053.27 0.068429 -4513.1 0.084204

-21.6194 -0.0393 -1390.87 -0.0406 -1985.57 -0.04334

[-1.4345] [-1.22026] [-2.19522] [ 1.68532] [-2.2729 ] [ 1.94268]

∆Xt-3 -227.006 0.013785 82.57036 -0.086128 -3124.37 -0.05147

-21.8683 -0.03975 -1326.59 -0.03873 -1887.41 -0.0412

[-10.380] [ 0.34677] [ 0.06224] [-2.22402] [-1.6553 ] [-1.24932]

∆Xt-4 -20.2587 0.053361

-23.2145 -0.0422

[-0.8726] [ 1.26446]

∆S&Pt-1 1.23104 0.000354 42.85917 -0.000463

-0.2995 -0.00054 -1.61674 -4.70E-05

[ 4.109] [ 0.6506] [ 26.509] [-9.8131]

It-1 -4.0253 0.011696 -14.6611 0.000729 -43.926 0.001045

-2.5516 -0.00464 -15.3555 -0.00045 -21.881 -0.00048

[-1.577] [ 2.5215] [-0.9547] [ 1.6262] [-2.007] [ 2.1881]


b – Including S&P and Interest difference.


d – Including Interest difference

51


COUNTRY SOUTH KOREAa PHILIPPINES

a AUSTRALIA

b

DEPENDENT VAR. ∆St ∆Xt ∆St ∆Xt ∆St ∆Xt

C -0.00201 0.282759 -67.6272 0.454496 -1.197628 -0.00025

-0.13198 -0.49058 -46.8576 -0.18753 -2.90012 -0.00064

[-0.01525] [ 0.57638] [-1.4432] [ 2.4235] [-0.41296 ] [-0.39145]

∆St-1 -0.00955 -1.68E+00 1.10161 -0.00184 -0.040507 -5.90E-06

-0.03899 -0.14494 -0.0385 -0.00015 -0.04448 -9.80E-06

[-0.2449] [-11.5872] [ 28.613] [-11.951] [-0.91075] [-0.59954]

∆St-2 0.01513 -0.22418 -0.11527 0.002357

-0.0425 -0.15802 -0.05972 -0.00024

[ 0.356] [-1.4186] [-1.9301] [ 9.8622]

∆St-3 0.05015 -0.29336 0.01426 -0.00056

-0.0423 -0.15753 -0.04217 -0.00017

[ 1.183] [-1.862] [ 0.3383] [-3.3149]

∆St-4 -0.0126 -0.2770

-0.0417 -0.15518

[-0.302] [-1.785]

∆Xt-1 -0.0037 0.204129 -7.87E+00 1.109771 -179.118 -0.01695

-0.01045 -0.03882 -9.53307 -0.03815 -201.348 -0.04454

[-0.3546] [ 5.2576] [-0.825] [ 29.087] [-0.88959] [ -0.38042]

∆Xt-2 0.02781 -0.096 2.44E+00 -0.08899

-0.0105 -0.0392 -13.3328 -0.05336

[ 2.635] [-2.447] [ 0.18334] [-1.667]

∆Xt-3 -0.0041 -0.15237 6.87E+00 -0.0281

-0.0106 -0.03941 -8.66771 -0.03469

[-0.386] [-3.866] [ 0.792] [-0.8101]

∆Xt-4 -0.0088 0.06301

-0.0094 -0.03497

[-0.937] [ 1.802]

∆S&Pt-1

0.34233 -4.34E-05

-0.14538 -3.20E-05

[ 2.35469] [-1.349]

∆It-1

2.738288 0.000385

-6.5309 -0.00144

[ 0.41928] [ 0.2665]

52


COUNTRY CANADAb CANADA

d JAPAN

b


C 1.237397 -0.00017 -1.987482 -5.64E-05 -6.82009 -0.03526

-5.03869 -0.00032 -7.37227 -0.00037 -8.35806 -0.0334

[ 0.24558] [-0.5099] [-0.2695] [-0.1516] [-0.8159] [-1.0556]

∆St-1 0.034091 -8.06E-06 -0.128091 -2.57E-06 -0.11796 -0.00029

-0.03125 -2.00E-06 -0.04492 -2.30E-06 -0.05015 -0.0002

[ 1.09106] [-4.0061] [-2.85135] [-1.13650] [-2.3522] [-1.4251]

∆St-2 -0.050722 1.87E-06 -0.093589 3.32E-06 -0.05964 -0.00013

-0.03107 -2.00E-06 -0.04542 -2.30E-06 -0.0502 -0.0002

[-1.63239] [ 0.9357] [-2.06059] [ 1.4502] [-1.1881] [-0.6285]

∆St-3 -0.004301 -3.09E-06 0.01902 -3.88E-06 -0.02104 -1.70E-05

-0.03063 -2.00E-06 -0.04481 -2.30E-06 -0.04963 -0.0002

[-0.14041] [-1.5690] [ 0.42444] [-1.71830] [-0.4239] [-0.0859]

∆St-4

38.1978 -0.02087

-12.5621 -0.0502

[ 3.0407] [-0.41570]

∆Xt-1 -1481.71 0.008086 -1789.158 0.018484 5.29282 -0.03367

-607.312 -0.0391 -888.667 -0.04482 -12.6867 -0.0507

[-2.43978] [ 0.2067] [-2.01330] [ 0.41242] [ 0.4171] [-0.66421]

∆Xt-2 -219.174 0.068429 -1117.504 0.098811 -11.6406 -0.02831

-604.944 -0.03895 -884.082 -0.04459 -12.5614 -0.0502

[-0.36230] [ 1.7567] [-1.26403] [ 2.21619] [-0.9266] [-0.56395]

∆Xt-3 44.04605 -0.04343 253.5908 -0.05052

-601.848 -0.03875 -880.749 -0.04442

[ 0.07318] [-1.1206] [ 0.28793] [-1.13726]

∆Xt-4

∆S&Pt-1 7.159053 -0.00024

1.108753 0.002821

-0.2578 -1.70E-05

-0.41935 -0.00168

[ 27.7604] [-14.5815]

[ 2.6439] [ 1.68363]

∆It-1 -31.77951 -0.0005 -33.56512 -0.00049 2.458713 -0.22732

-22.9543 -0.0014 -33.594 -0.00169 -38.5466 -0.15403

[-1.38447] [-0.369] [-0.99914] [-0.28692] [ 0.0637] [-1.47576]

53


COUNTRY JAPANd UNITED KINGDOM

b UNITED KINGDOM

d


C -7.29372 -0.0364 0.332348 0.000211 -0.78965 0.000236

-8.3932 -0.0334 -2.68954 -0.0002 -3.2454 -0.00021

[-0.86900] [-1.090] [ 0.123] [ 1.0411] [-0.2433] [ 1.14461]

∆St-1 -0.12335 -0.0003 -0.03373 -2.54E-08 -0.07547 9.23E-07

-0.05033 -0.0002 -0.0337 -2.50E-06 -0.04058 -2.60E-06

[-2.45083] [-1.492] [-1.0010] [-0.01000] [-1.8598] [ 0.35784]

∆St-2 -0.06209 -0.0001 0.009414 -2.59E-06 -0.0392 -1.49E-06

-0.05041 -0.0002 -0.03389 -2.60E-06 -0.04077 -2.60E-06

[-1.23154] [-0.658] [ 0.2777] [-1.01579] [-0.9614] [-0.57293]

∆St-3 -0.01138 7.54E-06 -0.09210 3.48E-06 -0.07925 3.18E-06

-0.04972 -0.0002 -0.03349 -2.50E-06 -0.04041 -2.60E-06

[-0.22885] [ 0.038] [-2.7505] [ 1.37915] [-1.9611] [ 1.23873]

∆St-4

0.066199 -4.29E-06 0.083751 -4.69E-06

-0.0333 -2.50E-06 -0.04018 -2.60E-06

[ 1.9876] [-1.71124] [ 2.0843] [-1.83466]

∆Xt-1 38.05273 -0.0212 36.49851 0.122962 371.7634 0.11534

-12.6177 -0.0502 -529.939 -0.03988 -639.227 -0.04065

[ 3.01582] [-0.422] [ 0.0688] [ 3.08352] [ 0.5815] [ 2.83759]

∆Xt-2 4.990476 -0.0344 158.6251 -0.03386 204.9122 -0.03491

-12.7425 -0.0507 -533.329 -0.04013 -643.728 -0.04093

[ 0.39164] [-0.678] [ 0.2974] [-0.84362] [ 0.3183] [-0.85282]

∆Xt-3 -13.5009 -0.0330 555.0297 -0.03153 535.6102 -0.03109

-12.5973 -0.0501 -533.017 -0.04011 -643.358 -0.04091

[-1.07172] [-0.658] [ 1.0413] [-0.78604] [ 0.8325] [-0.75986]

∆Xt-4

-577.95 -0.08292 -449.606 -0.08584

-528.928 -0.0398 -638.363 -0.04059

[-1.0926] [-2.08333] [-0.7043] [-2.11461]

∆S&Pt-1

2.372273 -5.39E-05

-0.13527 -1.00E-05

[ 17.537] [-5.29847]

∆It-1 -2.06523 -0.2388 -4.78716 -0.0004 -12.1784 -0.00023

-38.6794 -0.1540 -10.9191 -0.00082 -13.1698 -0.00084

[-0.05339] [-1.549] [-0.4384] [-0.48983] [-0.9247] [-0.27995]

54

A.10, Granger Causality test results (2007-2010)

COUNTRY NULL HYPOTHESIS H0 f-stata f-stat

b f-stat

c f-stat

d critical f-value

INDIA ∆St does not Granger Cause ∆Xt 1.339273 0.885105 1.39795 1.752966 F(4,676) =2.37


BRAZIL ∆St does not Granger Cause ∆Xt 15.86576 0.697377 0.650185 15.9489 F(3,675) = 2.6


S. KOREA ∆St does not Granger Cause ∆Xt 35.27671 32.21524 76.95274 35.11324 F(4,676) =2.37


PHIL. ∆St does not Granger Cause ∆Xt 79.08529 79.34672 78.59098 79.80409 F(2,679) = 3


AUSTRALIA ∆St does not Granger Cause ∆Xt 0.538087 0.239587 0.215099 0.515350 F(1,681) =3.84


CANADA ∆St does not Granger Cause ∆Xt 2.564655 1.947463 2.552379 2.603058 F(3,675) = 2.6


JAPAN ∆St does not Granger Cause ∆Xt 1.275765 3.147687 3.128548 1.258694 F(2,679) = 3


UK ∆St does not Granger Cause ∆Xt 1.509938 5.964761 5.590323 1.518974 F(4,676) =2.37





d- Including Interest difference

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