Price volatility

Foreign Exchange Volatility and TradingVolume of Derivatives Instruments:Evidence from the Brazilian Market

Vinicius Ratton BrandiBeatriz Vaz de Melo Mendes

Frederico Pechir GomesMarcelo Bittencourt Coelho dos Santos

ABSTRACT. The main objective of this work is to investigate the em-pirical relationship between trading volume and volatility. The under-standing of foreign exchange market microstructure can provide betterunderstanding of its volatility, which may be useful to support marketintervention decisions made by the authorities responsible for conduct-ing macroeconomic policies. Results show a positive contemporaneousrelationship between unexpected volume and volatility, suggesting si-multaneous influence at the arrival of relevant information. Moreover,they support the idea of market inefficiency, meaning that expectedvolume also reveals a positive correlation with volatility.

Vinicus Ratton Brandi (E-mail: [email protected]) and Frederico PechirGomes (E-mail: [email protected]) are Assessors, both at the Departmentof Financial Systems Guidelines, Central Bank of Brazil, Edificio Sede, 15 Andar,SBS, Quadra 3, Brasilia DF 70074-900, Brazil.

Beatriz Vaz de Melo Mendes is Professor, Mathematics Institute, and AssociateProfessor for Master’s and Doctoral Studies, COPPEAD Institute, Universidade Federaldo Rio de Janeiro, Avda Pedro Calmon, 500, Prédio da Reitora, 2 Andar, CidadeUniversitária, Rio de Janeiro RJ CEP 21941-901, Brazil (E-mail: beatriz@im. ufrj.br).

Marcelo Bittencourt Coelho dos Santos is affiliated with the International ReservesDepartment of Operations, Central Bank of Brazil, Edificio Sede, 5 Andar–Bloco B,SBS, Quadra 3, Brazilia DF 70074-900, Brazil (E-mail: [email protected]).

Latin American Business Review, Vol. 8(1) 2007Available online at http://labr.haworthpress.com

© 2007 by The Haworth Press, Inc. All rights reserved.doi:10.1300/J140v08n01_03 65

RESUMEN. O objetivo principal deste trabalho é investigar a relaçãoempírica entre volume de comercialização e volatilidade. A compreensãoda microestrutura do mercado de divisas pode fornecer uma compreensãomelhor de sua volatilidade, que pode ser útil para respaldar decisões deintervenção no mercado pelas autoridades responsáveis por conduzir aspolíticas macoeconômicas. Os resultados mostram uma relação con-temporânea positiva entre volume inesperado e volatilidade, sugerindouma influência simultânea na chegada de informações relevantes. Alémdisso, eles reforçam a idéia da ineficiência do mercado, significandoque o volume esperado também revela uma correlação positiva com avolatilidade.RESUMO. El objetivo principal de este trabajo consiste en investigar larelación empírica entre el volumen de transacciones y la volatilidad.La comprensión de la microestructura del mercado cambiario puedebrindar un entendimiento mejor sobre su volatilidad, lo que a su vez puedeser de utilidad para respaldar las decisiones tomadas por las autori-dades responsables por la conducción de las políticas macroeconómicas,de intervenir en el mercado. Los resultados muestran una relación cont-emporánea positiva entre el volumen inesperado y la volatilidad, lo quesugiere que existe una influencia simultánea a la llegada de la informaciónimportante. Además, ellos respaldan la idea de la ineficiencia del mer-cado, lo que implica que el volumen esperado también revela unacorrelación positiva con la volatilidad. doi:10.1300/J140v08n01_03 [Arti-cle copies available for a fee from The Haworth Document Delivery Ser-vice: 1-800-HAWORTH. E-mail address: <[email protected]>Website: <http://www.HaworthPress.com> © 2007 by The Haworth Press, Inc.All rights reserved.]

KEYWORDS. Granger causality, foreign exchange market, price-vol-ume relationship and market efficiency

INTRODUCTION

Blume, Easley and O’Hara (1994) suggest that price and volume arejointly determined by the same market dynamics. According to Karpoff(1987), the analysis of the correlation between price and volume maylead to discrimination among different market microstructure hypothe-ses. The understanding of the relationship discussed herein is alsoimportant for purposes of modeling the empirical distribution of specu-lative prices.

66 LATIN AMERICAN BUSINESS REVIEW

The present work focuses on the Brazilian FX market. Our aim is toinvestigate the dynamic relationship between trading volume in the for-eign exchange derivatives market and volatility in the BRL/USD ex-change rate. It is worth mentioning that the complete understanding ofthe FX market microstructure is a main concern for the monetary au-thorities in trying to conduct macroeconomic policies. Indeed, it canlead to the identification of FX volatility determinants, which may beuseful for supporting market intervention decisions (BIS, 2005).

Our results show a positive contemporaneous relationship betweenunexpected volume and volatility, suggesting a simultaneous influenceat the arrival of relevant information. Moreover, they support the idea ofmarket inefficiency, suggesting that expected volume also reveals apositive correlation with volatility.

The remaining part of this paper is organized as follows. The secondsection presents a brief review of the literature. The third section detailsthe data sample used to perform the analysis. Then, the fourth sectiondescribes the methodology and shows the results. Finally, the fifth sec-tion states our concluding remarks.

LITERATURE REVIEW

Classical finance theory1 assumes that efficient price discovery (pricechanges in efficient markets) may only happen when there is an imme-diate diffusion of new and relevant information. Therefore, assumingthat the occurrence of new information may also motivate new trades, itis possible to find a bi-directional causality relationship between priceand volume, given that these two variables are dependent on the sameoriginating process.

More recently, different theoretical frameworks have been used toexplain the co-movement between trading volume and price volatility.One of them refers to the inventory effect, which means that marketmakers manage their liquid position in response to unexpected changesin specific macroeconomic variables that determine the supply and de-mand of financial assets, and they move their bid-offer spreads aroundthe supposed expected/fair values in a way to induce the order flow.2

Conversely, information asymmetry models3 assume that new infor-mation is gradually diffused by market participants, affecting the pricediscovery process through intermediate equilibria, until reaching afull-information environment. Since agents may react to the arrival of

Brandi et al. 67

new relevant information, thereby probably causing shifts in their de-mand curve,4 their actions will produce a positive correlation betweenvolume and volatility. Therefore, one can assume that past volume mayprovide valuable information concerning absolute price changes.

Another theoretical explanation is based on the mixture of distribu-tion hypothesis (MDH), which assumes that both volume and volatilityare driven by an unobservable common factor that reflects the arrivaland diffusion of new public information, and therefore establishes apositive correlation between unexpected volume and volatility. In linewith previous works,5 Tauchen and Pitts (1983) developed the seminaltheoretical work in this literature, where they propose a model in whichvolume varies over time due to a rise in the number of traders, the entryof new information or the existence of heterogeneous expectationsamong traders.

Corroborating the assumptions of the MDH, Frankel and Froot(1990a, b) examine the relationship among the dispersion in expecta-tions (survey forecast), volatility and volume, finding strong evidencethat the dispersion parameter, related to the divergence among mar-ket players’ beliefs, affects both volume and volatility. Fosters andViswanathan (1990) also present models where the dispersion of expec-tations causes not only a rise in price volatility but also excess volume.

The literature concerning the price-volume relationship in the stockmarket is plentiful.6 Gallant, Rossi and Tauchen (1992), for instance, in-vestigate this relationship in the US stock market based on a time seriescovering the period from 1928 to 1985. Their results indicate that trad-ing volume is positively and non-linearly related to the size of pricechanges, which supports the hypothesis of the existence of a vol-ume-volatility relationship for both unconditional and conditional dis-tributions in cases where the returns are adjusted to an excess of kurtosisand stochastic volatility.

For the Brazilian market, Tabak and Guerra (2003) examine theprice-volume relationship for 20 stocks traded on the Bovespa.7 Using alinear bivariate VAR model, they find that price Granger-causes vol-ume. Non-linear tests indicate strong evidence of bi-directional Grangercausality between the two variables.

With regards to the foreign exchange market, recent empirical evi-dence8 shows that FX rates present significant changes that are notentirely explainable by macroeconomic fundamentals. Taylor (1995)holds that speculative forces contribute to the discrepancy between FXrates’ short-term behavior and behavior related to the macro funda-mentals, which is normally modeled structurally.9 This explains why


the financial approach to FX rate changes and market microstructure in-vestigation has gained academic importance in recent years, despiterepresenting a variable that holds a certain relationship with the funda-mentals, especially in the medium and long terms.

Following this approach, an important work is the one by Galati(2000), in which daily data from January 1998 through June 1999 areused to investigate the price-volume relationship for seven currencies ofemerging economies. Unlike the other currencies analyzed, the hypoth-esis of a positive relationship between volume and volatility for theMexican Peso and the Brazilian Real was rejected.

Furthermore, Bjonnes, Rime and Solheim (2003) analyze the vol-ume-volatility relationship in the Swedish FX market (SEK/EUR rate)and find evidence that the participation of large banks is relevant for ex-plaining the positive correlation between these two variables. The au-thors also argue that heterogeneous expectations consist of an importantaspect to be considered. Bessembinder, Chan and Seguin (1996) alsofind that in both spot and futures market trading, volume varies posi-tively with proxies for information flow. They also find that a rise intrading volume is positively correlated to a proxy for the divergences ofopinion among traders, with a drop in trading volume being consideredunrelated.

In this regard, Lyons (1995), and Sarno and Taylor (2001) point to theimportance of investigating variables such as the information arrival pro-cess, agent behavior, order flow and player heterogeneity. The purpose isto better understand the implications of market microstructure aspects onprice and volatility in the FX market.

DATA DESCRIPTION

Sample Size

The analysis covers the period from June 1, 1999 through June 21,2005 for a total number of 1,464 daily observations. The sample sizeselection was influenced by the exchange rate regime adopted by theBrazilian authorities. Only data observed after the adoption of thefloating rate regime in January 1999 were considered. Additionally, inorder to avoid atypical fluctuations due specifically to the change in theFX regime, June 1999 was chosen as the beginning month.10

Brandi et al. 69

The sample data are composed of daily BRL/USD returns,11 withBrazil’s country risk measured as the daily logarithmic return of theEMBI� Brazil Index12 and the volume, in BRL, of the more liquidFX derivatives13 negotiated at BM&F.14 According to Garcia and Urban(2004), the derivatives market is more liquid than the spot market.Considering the period from 1993 to 2003, they verified that the tradingvolume of the exchange rate futures’ first maturity was 2.7 times higherthan the volume of the spot market. According to the same authors,Granger causality tests between prices in both markets find that the pricediscovery process for the BRL/USD exchange rate takes place in the fu-tures market, probably because of its higher liquidity and transparency.

The sample was also divided into two sub-periods: Period 1, fromJune 1, 1999 to July 26, 2002, with 758 observations, and Period 2, fromAugust 9, 2002 to June 21, 2005, with 695 observations. This divisionpermitted the implementation of a complementary analysis by eliminat-ing a 15-day period of unusual and extremely high volatility observed inthe second semester of 2002, caused by speculations involving the pres-idential election process. In addition, given that the Central Bank ofBrazil, through the regulation “Circular 3.156,” reduced the maximumlimit for the FX exposures of financial institutions from 60% to 30% ofthe regulatory capital, the division adopted herein allows for the com-parison between distinct regulatory environments.

Volatility

In the analysis, exchange rate volatility is used as the dependent vari-able represented by two different measures–the BRL/USD exchangerate absolute daily return |Rt| and the square of the daily return Rt

2. Qua-dratic returns are used in order to emphasize the ones of large magni-tude, regardless of their signs.

To control for the expected conditional volatility, which might not bedetermined by the arrival of new information or by changes in marketagents’ expectations, the regressions that evaluate the contemporaneousrelationship between volume and volatility are estimated based on theGARCH-M (GARCH-in-Mean) process introduced by Engle, Lilienand Robins (1987), and which includes as a predictor of the conditionalmean past values of conditional standard deviation or conditional vari-ance related to the GARCH process.


Volume

According to Tauchen and Pitts (1983), it is possible to perceive arise in volume due to an increase in the number of traders from a risecaused by the arrival of new information. The rise related to the numberof participants is seen as the expected volume E(Vt), which must beassociated with the market’s liquidity and should have a modest impacton volatility. Conversely, the unexpected volume e(Vt), is influencedby the arrival of new information and is important for explaining thevolume-volatility relationship, as observed by Bessembinder and Seguin(1992) and Hartmann (1999).

The traditional methodology of Box-Jenkins (Box and Jenkins, 1978)is used to split the data set’s total volume into expected and unexpectedvalues. The purpose here is to obtain a parsimonious modeling of thecomplete time series. Fitted values are defined as the expected values,and the residuals are the unexpected values. Hartmann (1999) rec-ommends the use of ARIMA (9.1.1) for the JPY/USD series. How-ever, in the present work no evidence was found supporting bettermodeling than ARIMA (2.2), which is the same conclusion reached byBjonnes, Rime and Solheim (2003). As the series of volume is station-ary around a linear trend, we added a time variable to the ARIMA (2.2)process. The ARCH structure was treated through a GARCH (1.1)model.

Descriptive Statistics

Return and volume time series present the well known features al-ready established as stylized facts of financial series, such as an excessof kurtosis, fat tails and serial correlation, as presented in Table 1. Thenormality hypothesis is rejected for all the variables by the Jarque-Beratest (Jarque and Bera, 1980) at the 1% significance level. It should be re-membered that, in this work, the EMBI� variable represents the log re-turn of the EMBI� Brazil.

In order to avoid spurious results in our linear analysis, the unit rootwas checked according to the augmented Dickey-Fuller test (ADF)(Dickey and Fuller, 1979), with the lag length defined by the Akaikeinformation criterion (AIC). The unit root hypothesis is rejected for allvariables presented in Table 1 at the 1% significance level.

Brandi et al. 71

EMPIRICAL ANALYSIS

Contemporaneous Relationship

The contemporaneous relationship between volume and volatility isanalyzed according to the following regression:

s et t t t t tC h E V e V EMBI= + + + + +-+b b b b0 1 1 2 3( ) ( ) (1)

where �t represents the volatility on date t, and ht � 1 is the estimate int �1 of the conditional volatility on date t. 0, 1, 2 and 3 are the coef-ficients of the explanatory variables, and �t is a residual adjusted by aGARCH (1, 1) process. In the regressions with the absolute return as thedependent variable, h is given by the conditional standard deviation. Inthose having the square of the returns as the dependent variable, h isgiven by the conditional variance.

The models were estimated by maximum likelihood assuming inno-vations with normal distribution fitted by a GARCH (1, 1) process. Re-siduals do not present any particular pattern, a fact corroborated by thetraditional formal tests. Yet, it was verified that the explanatory vari-ables of the equation (1) do not present any significant correlation. Aspreviously mentioned, with the aim of controlling for the expected con-ditional volatility, which is not associated with the arrival of newprominent information models, regressions are estimated by aGARCH-M process.

The EMBI� variable is included to eliminate the country risk com-ponent of the exchange rate volatility, as documented by Garcia andLowenkron (2003). According to these authors, Brazil is one of the


TABLE 1. Descriptive Statistics

Average SD Asymmetry Kurtosis

|Rt| 0.007181 0.007784 3.171493 23.98995

Rt2 0.000112 0.000374 17.26186 449.4540

E(Vt) 815.7922 2,248.480 2.048654 9.941701

e(Vt) 543.1619 3,746.084 1.862322 10.50383

EMBI+ �0.000652 0.025987 0.617596 5.644192

Note: Average, Standard deviation, Asymmetry and Kurtosis of absolute return series, quadratic return, ex-pected volume, unexpected volume and EMBI+ return.

countries that verify the presence of the phenomenon known as cousinrisks, defined as the existence of a positive correlation between thecountry risk and the exchange rate risk. They argue that whenever acountry faces an unbalance in its exchange rate exposure,15 an increasein the expectation of exchange rate depreciation or in the exchange raterisk intensifies the perception of future insolvency, elevating the sover-eign’s credit risk. On the other hand, it is reasonable to assume thatevents that alter the probabilities of default of a determined country willinfluence both exchange rate volatility and its level.

Considering the three periods studied, we test six models of regres-sion, as shown in Table 2.

In general, the results indicate that at the 1% significance level thereis a positive relationship between volatility and the expected andunexpected volume. Only the expected volume in the regression thatuses the absolute value of return presents no significance for period 1.Based on the adjusted coefficient of determination (R2 Adjusted) for thethree periods in the analysis, the model considering the absolute returnas the volatility measure presented the best fit. The F test, which is also

Brandi et al. 73

TABLE 2. Contemporaneous Relationship Between Volatility and Volume

Explanatory Variables

Period DependentVariable

h C E(Vt) e(Vt) EMBI� R2

Adjusted

All R 0.955079*(0.08792)

�0.00064(0.00034)

1.83E-7*(3.98E-5)

1.20E-7*(2.30E-8)

0.08357*(0.0058)

3.3307

1 R 1.02404*(0.1113)

�0.00064(0.00041)

9.76E-8(8.77E-8)

2.84E-7*(4.07E-5)

0.08045*(0.00764)

0.2413

2 R 1.1679*(0.1927)

�0.00145(0.0009)

1.92E-7*(5.41E-8)

9.94E-8*(3.08E-8)

0.07441*(0.01036)

0.3104

All R2 0.3370*(0.026)

�2.92E-6**(1.21E-6)

4.72E-9*(2.72E-10)

9.16E-10*(1,82E-10)

0.01964*(0.0007)

0.1750

1 R2 0.1577*(0.0292)

�4.79E-6(1.67E-6)

4.02E-9*(5.86E-10)

2.74E-9*(3.91E-10)

0.0179*(0.0010)

0.0879

2 R2 0.5428*(0.0748)

�9.36E-6*(5.82E-6)

2.25E-9*(7.46E-10)

1,12E-9*(4.02E-10)

0.0135*(0.016)

0.2465

Note: Coefficients of equation (1) estimated under GARCH (1.1) process, for different periods and volatilitymeasures. The h variable as square root of conditional variance for regressions with absolute return andthe own conditional variance for quadratic return. Standard Error in parenthesis.*Statistically significant at 1% level.**Statistically significant at 5% level.

seen as a test of significance of R2, rejects, at the 1% significance level,the null hypothesis that R2 is zero for each regression.

Concerning the unexpected volume, the results corroborate previoustests performed in international markets, suggesting that both answersimultaneously to the arrival of new information, which agrees with theMDH. Galati (2000), on the contrary, examined the BRL/USD marketand verified a negative correlation between those variables, but withoutany statistical significance. According to our evaluation, his result mighthave been influenced by the short sample period, which was comprised oftwo different exchange rate regimes and the overshooting of the ex-change rate usually observed after regime transition. As the author pointsout, the negative correlation elapses from the turbulence period in inter-national markets, which affirms that the correlation should be positiveonly during periods of normality, but negative in periods of stress.

Regarding the expected volume, a significant positive correlationwith volatility was observed. The MDH states that an increase in the ex-pected volume elapses solely from an increase in the number of marketplayers, showing no relationship to market volatility. As a conclusion,we could question the efficiency of the Brazilian foreign exchange mar-ket, as its price is influenced by factors not related to the arrival of newinformation, such as an expected variable. This result may be corrobo-rated by the common notion that this market presents low liquidity. Assuggested by Tabak and Guerra (2003), such a result could lead to theconclusion that liquidity risk may be an important issue when buildingrisk management models. A theoretical explanation could be based oninformation asymmetry models, which assume that the gradual diffu-sion of information among market agents could result in a spurious cor-relation between expected volume and volatility.

In line with the hypothesis of cousin risk, the EMBI� showed a sig-nificant positive correlation in all the regressions.

Causality Test

The lagged relationship is investigated using Granger causality(Granger, 1969). Although this test does not provide an idea of cause-effect in the economic sense, it can be very useful for indicating thelagged relationship between variables, providing valuable informationabout the microstructure of financial markets. The definition of the laglength can be expert-based by using intuition to perceive what would bethe longest period of time that the movement in price of a variable willforecast movements in the price of another variable. We have deter-


mined a maximum lag of 10 business days and the optimal lag lengthk was chosen by AIC by implementing a bivariate VAR analysis. Aspreviously stated, the series are stationary, as indicated by an ADF test,which validates the conventional test of Granger based on VAR withvariables on the level.

Basically, Granger’s causality verifies whether the conditional vari-ance of a dependent variable is significantly reduced after the inclusionof past information about another variable and lagged observations ofthe own dependent variable. As explained by Greene (2003), tests com-paring restricted and extended regressions are based on the F test in theequations of the VAR model.

In this study, the extended regression is defined as:

s st ii

k

t i ii

k

t i tV u= + +=

-=

-Â Âa b1 1

(2)

And the restricted regression as:

s g st ii

k

t i tv= +=

-Â1

(3)

The statistical test is defined by:

l =TT

T

v u

u

t ttt

tt

2 2

= 1= 1

2

= 1

- - -ÂÂÂ

T kk2 1

(4)

where ut and vt represent the residuals of the extended and restricted re-gressions, respectively, T is the sample size, represents the volatilitymeasure and V is the volume measure. The statistical test λ has an as-ymptotic F distribution (k, T 2k�1). If the critical value of the F distri-bution for a determined confidence level is lower than the statisticaltest, the null hypothesis will be rejected. The null hypothesis states thatthe new explanatory variable included in the extended regression doesnot Granger-cause the dependent variable.

Table 3 presents the results of the Granger causality test for the ex-pected volume. As can be seen, for absolute returns the exchange ratevolatility Granger-causes E(Vt), which can be explained by agentssearching for a hedge in the derivatives market after observing an

Brandi et al. 75

increase in volatility (risk). Regarding the quadratic returns, the sametendency was verified for period 1 only.

Table 4 presents the results of the Granger causality test for the unex-pected volume. The Granger causality relationship was verified only forperiod 1 at the 5% significance level, with the square returns as thevolatility measure. It is usually verified that there is no lagged relation-ship between volatility and unexpected volume, suggesting that theinformation arrival process has simultaneously influenced both vari-ables, which corroborates with our theoretical assumptions.


TABLE 3. Granger Causality Test Results

All R Granger not cause E(Vt) 6.677*(0.00018)

E(Vt) Granger not cause R 1.443(0.22843)

1 R Granger not cause E(Vt) 5.9386*(0.00053)


2 R Granger not cause E(Vt) 3.14572**(0.02464)


All R2 Granger not cause E(Vt) 1.019(0.39618)

E(Vt) Granger not cause R2 3.70533*(0.00523)

1 R2 Granger not cause E(Vt) 5.61165*(0.00019)

E(Vt) Granger not cause R2 0.65784(0.62147)

2 R2 Granger not cause E(Vt) 0.69660(0.59446)

E(Vt) Granger not cause R2 0.43479(0.78353)

Note: Test statistic and p-value (in parenthesis) of Granger causality test for the three periods in analysisand with the expected volume as measure of volume.*Statistically significant at 1% level.**Statistically significant at 5% level.

CONCLUSION

The classical market efficiency hypothesis assumes that the variationof prices elapses only from the arrival of new prominent information.The mixture of distributions hypothesis (MDH) assumes that volumeand volatility are both driven by a common and unobservable factor thatreflects the arrival of new public information and therefore generates apositive correlation between unexpected volume and volatility. Con-versely, information asymmetry models state that new information isgradually diffused through the financial market, affecting the price dis-covery process through intermediate equilibria, until reaching a finalequilibrium, when complete information is available to all players.

Brandi et al. 77

TABLE 4. Granger Causality Test Results

All R Granger not cause e(Vt) 2.37624(0.09410)

e(Vt) Granger not cause R 0.90498(0.09967)

1 R Granger not cause e(Vt) 2.31302(0.09967)


2 R Granger not cause e(Vt) 0.89216(0.41024)


All R2 Granger not cause e(Vt) 0.65515(0.51952)

e(Vt) Granger not cause R2 1.59506(0.20325)

1 R2 Granger not cause e(Vt) 3.65666**(0.02628)


2 R2 Granger not cause e(Vt) 0.03101(0.96947)


Note: Test statistic and p-value (in parenthesis) of Granger causality test for the three periods in analysisand with the unexpected volume as measure of volume.* Statistically significant at 1% level.**Statistically significant at 5% level.

The main objective of this study has been to contribute to the literatureon the microstructure of foreign exchange markets, investigating the em-pirical relationship between trading volume and volatility. It is worth not-ing that the understanding of the FX market’s microstructure can leadto identifying possible causes for a rise in FX volatility, which may beuseful for supporting market intervention decisions made by the authori-ties responsible for conducting macroeconomic policies (BIS, 2005).

Previous research in this field shows a positive correlation betweenvolume and volatility in different markets. A prominent work on theforeign exchange market is that of Galati (2000), in which there is noverification of a significant relationship between volume and volatilityin the Brazilian market.

At the 1% significance level, the results in general indicate a positiverelationship between volatility and the expected and unexpected vol-ume. Concerning the unexpected volume, results corroborate with pre-vious tests performed in international markets, suggesting that bothanswer simultaneously to the arrival of new information, in agreementwith the MDH. Regarding the expected volume, a significant positivecorrelation to volatility was observed, suggesting that the efficiency ofthe Brazilian foreign exchange market cannot be taken for granted. Atheoretical explanation for this could be based on information asymme-try models, which assume that the gradual diffusion of informationamong market agents could result in a spurious correlation between ex-pected volume and volatility.

Granger causality tests indicate that expected volume can be betterexplained by lagged price volatility values, which could be explained byagents searching for a hedge in the derivatives market after an increasein volatility (risk). Regarding the unexpected volume, it has been veri-fied that there is no lagged relationship between volatility and unex-pected volume, suggesting that the information arrival process has asimultaneous influence on both variables, contrary to the assumptionsof information asymmetry models.

Future research in this area could investigate aspects such as non-lin-earity and asymmetry in the relations between volume and volatility.Regarding the data sampling, high frequency data could be used. Fi-nally, others variables, such as proxies for the information arrival pro-cess, agent behavior, order flow or heterogeneity could be incorporatedinto the analysis in order to promote a better understanding of the for-eign exchange market’s microstructure and its implications.


NOTES

1. See Samuelson (1965) and Fama (1970).2. Evans and Lyons (1999) demonstrate that the variable defined in Lyons (2001)

as a variant of the concept of liquid demand is very important for explaining FX ratechanges.

3. Copeland (1976), Morse (1980), and Jennings and Barry (1983).4. The trader behavior–optimism or pessimism–will determine the direction of the

curve shift.5. While the model developed by Clark (1973) sees the volume as a proxy for deter-

mining how fast the information flows, Epps and Epps (1976) use volume to measurethe level of divergence among traders’ beliefs, since prices are revised given the arrivalof new information.

6. Rogalski (1978), Smirlock and Starcks (1988), Jain and Joh (1988). Hiemstraand Jones (1994), Silvapulle and Choi (1999), Saatcioglu and Starks (1998), and Basçi,Özyildirim and Aydogan (1996).

7. Bolsa de Valores de São Paulo.8. See Frankel and Rose (1996), Taylor (1995), Flood and Taylor (1996), Lyons

(1995), and Lyons (2001).9. Models that aim to explain the behavior of FX rates based on the behavior of

macroeconomic variables.10. See Dornbusch (1976) for more information on market movements (overshoot-

ing) following a change in the FX regime.11. Calculated as the logarithmic difference of the last prices provided by Bloomberg.12. The Emerging Markets Bond Index Plus–EMBI� is calculated by JPMorgan

based on the returns of sovereign and quasi-sovereign instruments denominated inUSD (Bradies, Eurobonds, loans, etc.).

13. The instruments used in the analysis are futures and swaps. Futures are responsi-ble for approximately 99% of the total volume.

14. Bolsa de Mercadorias e Futuros, the Brazilian derivatives exchange.15. Measured as the difference between the foreign debt and the international re-

serves, both as a percentage of the GDP.

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Received: January 24, 2006Revised: March 13, 2006

Approved: April 13, 2006

doi:10.1300/J140v08n01_03


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