Information transmission and spillover in currency markets: A generalized variance decomposition analysis

The Quarterly Review of Economics and Finance 47 (2007) 312–330

Information transmission and spillover in currency markets: A generalized variance decomposition analysis Elyas Elyasiani a,∗,1 , Ahmet E. Kocagil b,2 , Iqbal Mansur c,3 a

Fox School of Business and Management, Temple University, Philadelphia, PA 19122, United States b Global Quantitative and Financial Research, Fitch Ratings, New York, NY 10004, United States c School of Business Administration, Widener University, Chester, PA 19013, United States Received 3 November 2004; received in revised form 19 May 2006; accepted 20 May 2006 Available online 8 August 2006

Abstract This paper investigates the dynamics in the British Pound (BP), Deutsche Mark (DM), Swiss Franc (SF), and Japanese Yen (JY) futures using Generalized Variance Decomposition analysis over the 1985–2005 period. The results support the interdependence hypothesis against the segregation model with the degree of susceptibility to foreign shocks varying across currencies; internal forces are more dominant for BP and JY, whereas DM and SF are more exposed to external shocks. The results also reveal that SF was tightly linked to the DM before the demise of the latter, whereas it became aligned with the BP afterwards. The inter-currency effects lessened in the post-1987 crash period and then reversed course in the beginning of the new century. © 2006 Board of Trustees of the University of Illinois. All rights reserved. JEL classification: C3; F3; G1 Keywords: Intraday; Currency futures; Generalized variance decomposition

∗

Corresponding author. Tel.: +1 215 204 5881; fax: +1 215 204 1697. E-mail addresses: [email protected] (E. Elyasiani), [email protected] (A.E. Kocagil), [email protected] (I. Mansur). 1 A part of this paper was completed when the first author was on a study leave from Temple University and a visiting professor at the Jerusalem School of Business Administration, Hebrew University. 2 Tel.: +1 212 908 0271. A portion of this article was completed while the second author was affiliated with the Pennsylvania State University, University Park, PA 16802, United States. 3 Tel.: +1 610 499 4321. 1062-9769/$ – see front matter © 2006 Board of Trustees of the University of Illinois. All rights reserved. doi:10.1016/j.qref.2006.05.004

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1. Introduction Questions concerning extent and stability of shock spillover across currency markets are of interest because these market features have serious implications for currency trading strategies, currency risk diversification, and formulation of exchange rate policies by central banks. Increased globalization of financial markets, due to advancement in the information technology and loosening of the restrictions on cross-market capital movements, provides further motivation for study of such interdependencies. Channels of shock spillover include optimizing behavior of individual rational agents in the private sector, and public announcement of news, among others (see Engle, Lin, & Ito, 1992 for details). Information may be disseminated across markets as rational economic agents attempt to extract the information privately held by agents in other markets, by observing the latter’s trade patterns (King & Wadhwani, 1990).4 In this scenario, market changes reflect idiosyncratic shocks and pricing errors spilled over from one market to another, as well as changes in the market fundamentals. Moreover, shocks to one market, may lead to simultaneous reaction of participants in the same and other markets producing co-movements in these markets. Similarly, a government may choose to respond to policy actions adopted by another government, or two or more governments may co-ordinate the timing and the nature of their policy changes, creating a co-movement between the markets of these countries. Particular cases include a reaction to a change in the interest rate or exchange rate target announced on the part of the U.S. Federal Reserve by the Europeans and the Japanese Central Banks. These reactions are generally brisk and bring about swift changes in several financial markets in the short-run, as well as changes in the real sector in the longer-run.5 Coordinated interest rate and/or exchange rate changes among U.S., European, and other industrialized countries to set a specific set of exchange rates, would also produce simultaneous changes in the markets of the countries implementing the policy, as well as those of other countries. These changes engender interdependencies across markets, at least in the short-run (see Engle et al., 1992). The issue of interdependence across markets has been widely investigated both in the long-run and short-run settings. Studies of co-dependence in the long-run are generally carried out using the cointegration framework, while techniques used for analysis of short-run spillover include correlation, vector auto-regression, impulse response function, and variance decomposition. Studies investigating cointegration among currencies are numerous. These include, e.g., Baillie and Bollerslev (1989, 1990), Hakkio and Rush (1989), MacDonald and Taylor (1989), Barnhart and Szakmary (1991), Copeland (1991), Lai and Lai (1991), Sephton and Larsen (1991), Lajaunie and Naka (1992), Kroner and Sultan (1993), Crowder (1994), Diebold, Gardeazabal, and Yilmaz (1994), Lajaunie, McManis, and Naka (1996), and Elyasiani and Kocagil (2001). The general finding is the paucity of cointegration among currencies, except for temporary deviations at times of policy changes, world events, and shifts in currency regimes. 4

This is dubbed the “private information hypothesis”. Using intraday data, Becker, Finnerty, and Kopecky (1995) find that certain U.S. macroeconomic news significantly impacts futures prices of German, Japanese, and British interest rates. Similarly, using intraday data, Becker, Finnerty, and Friedman (1995) report that U.S. and U.K. markets respond swiftly to the U.S. macroeconomic news. These authors conclude that international market linkages are attributable to reaction of foreign traders to public information in the U.S. This is along the lines suggested by the “public information hypothesis”. Stevenson (2002) uses daily data to examine the sensitivity of the stock markets in seven European countries to interest rate changes by the German Central bank (Bundesbank). He reports that non-German equity markets did indeed react significantly to a large number of the Bundesbank rate changes, demonstrating the prevalence of cross-border information spillover. 5

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Studies of currency market dynamics, however, are more limited. Examples of such studies are Baillie and Bollerslev (1991), and Engle et al. (1992) who find spillover effects in volatility between different exchange rate markets.6 A noteworthy point is that investigation of the intradaily dynamics between different currency markets has received inadequate attention.7 This is especially important because it is widely known that absorption of new information in spot and futures currency markets is generally a matter of hours, rather than days (Ederington & Lee, 1993; Tanner, 1997).8 The use of high frequency data makes tests regarding currency dynamics more insightful for, at least, two reasons. First, high-frequency data allow markets to delineate equilibria in shorter time windows. For markets with rapid clearing, market adjustment occurs much more frequently than once a day. As a result, lower frequency data, such as daily, would fail to reveal the prevailing dynamics, regardless of their strength. Second, for a given length of time, high frequency data provide for a larger number of observations, and possibly equilibria, than low frequency data, allowing statistical inferences to be drawn for a narrower time interval, such as a year, and making the results more reliable. As a consequence, the effect of omitted factors such as regime shifts and government intervention in the market will be restricted to the event year, preventing it from clouding the overall picture (Crowder, 1996; Elyasiani & Kocagil, 2001). This paper aims to investigate the prevalence, extent, symmetry, and stability of spillover across currency markets within a short-term horizon. To investigate the prevalence of spillover, a test of the market segregation will be carried out. The extent of inter-currency spillover will be measured using the generalized variance decomposition (VD) technique. Symmetry of the inter-currency effects, and stability of the degree of market integration over time will also be examined within the same framework. This study improves upon the previous work on the nature of information transmission in financial markets in several ways. First, the study utilizes the generalized VAR technique, developed by Koop, Pesaran, and Potter (1996) and Pesaran and Shin (1998), to investigate the spillover of shocks. The major advantage of this approach over the commonly used orthogonalized variance decomposition and impulse response analysis is that, unlike the latter, it is invariant to the ordering of the variables in the VAR system. Moreover, since in generalized VAR no a-priori restriction or ordering scheme is imposed on the variables, the variance decomposition carried out in this framework is expected to yield results that are more sensible in assessing the linkages among the currency markets.9 Second, the analysis here focuses on currency futures markets (rather than equity markets) and examines short-term dynamics, as opposed to the long-term cointegration procedure. It is entirely possible that short-term interdependencies do and long-term interdependencies do not exist. In this case, VD analysis offers a more accurate picture of the information

6 Studies of dynamics across national stock markets are numerous. For example, see Eun and Shim (1989) and Theodossiou and Lee (1993) and references therein. 7 An exception is Wasswerfallen and Zimmermann (1985), which examines the properties of intra-daily Swiss Franc/US Dollar exchange rate. The impact of data frequency on econometric properties of foreign exchange series is acknowledged and investigated by Goodhart, McMahon, and Ngama (1993): they report that unit root properties of the series do indeed differ for tests based on data with higher and lower frequencies. 8 Becker, Finnerty, and Friedman (1995) also find that U.K. stock futures respond almost immediately to the U.S. macroeconomic news. 9 Orthogonalized VAR is sensitive to ordering of variables while structural VAR imposes a priori restrictions on the VAR coefficients. Dekker, Sen, and Young (2001) and Yang, Kolari, and Min (2003), among others, have argued, that the generalized variance decomposition framework provides a more accurate and realistic description of market linkages.

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flow in the currency markets.10 Third, the use of low frequency data in the extant studies obscures the interaction among currencies, which may be short-lived due to the high speed of information transmission. The current study removes this problem by employing intra-daily data. Finally, the longer sample horizon used here (1985–2005) allows us to check whether the results reached here or in other studies based on shorter time periods and/or carried out under alternative methodologies are robust or sample- and methodology-sensitive. The analysis produces some interesting results: (i) market interdependence holds in all cases, (ii) the degree of vulnerability to outside shocks varies considerably across currencies, (iii) currency interaction appears to be asymmetric for some currencies and near symmetric for others, and (iv) currency market interdependence weakened, rather than deepened, in the post-mid 1990s but strengthened after the introduction of the Euro. The findings of this study are complementary to Elyasiani and Kocagil (2001) who adopt a cointegration framework to examine long-term interdependence of the currencies. The rest of the paper is organized as follows. Section 2 presents the methodology. Section 3 describes the data, followed by empirical findings in Section 4. Section 5 summarizes and concludes the paper. 2. Methodology 2.1. The generalized vector autoregressive methodology The VAR methodology provides a multivariate framework where changes in a particular variable are related to the autoregressive process of all dependent variables as well as contemporaneous values of all exogenous variables. Both variance decomposition and impulse response functions (IRF) are obtained from the same VAR system. Variance decomposition functions demonstrate how each of the currencies considered contributes to changes in a given currency. Variance decomposition analysis divides the forecast error variance of a currency return into proportions attributable to shocks in other currency returns, as well as its own. Similarly, IRF map out the dynamic response of a currency return to a one-period shock to another currency return. A vector autoregressive model of order p, or VAR (p), can be expressed as: Rt = C0 +

p 

Φi Rt−i + ψDt + ut ,

t = 1, 2, . . . , T,

(1)

i=1

where Rt is a m × 1 column vector of currency returns, Dt is a q × 1 vector of deterministic or exogenous variables, C0 is 1 × m row vector, Φi (i = 1, 2, . . ., p) and ψ are m × m and m × q coefficient matrices to be estimated, p is the lag length and ut is the m × 1 column vector of unobserved disturbances. The unobserved disturbances are assumed to be independently and identically distributed (i.i.d.) with E(ut ) = 0 and E(ut ut ) = Ω for all t. If Rt is covariance stationary, Eq. (1) can be rewritten as an infinite moving average process as follows: Rt =

∝  i=0

Ai ut−i +

∝ 

Bi Dt−i ,

t = 1, 2, . . . , T.

(2)

i=0

10 When cointegration does not exist, which is often the case, error correction can not be formulated. However, variance decomposition can still show the character of the short-term interaction.

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Eq. (2) represents Rt as a linear combination of the current and past one step-ahead forecast errors. The m × m coefficient matrix Ai is obtained by using the following recursive substitution: Ai = Φ1 Ai−1 + Φ2 Ai−2 + . . . + Φp Ai−p ,

i = 1, 2, . . . ,

(3)

with A0 = Im (an identity matrix of order m), and Ai = 0 for i < 0, and Bi = Ai ψ In Eq. (2), the errors are serially uncorrelated but they may be contemporaneously correlated. The presence of contemporaneous correlation implies that the covariance matrix of innovations is not diagonal. If the covariance matrix is non-diagonal, the shock in one market may work through the contemporaneous correlations in other markets. In order to identify the distinct response patterns that the VAR system exhibits, it may be necessary to transform the error term. The traditional approach used for this purpose, suggested by Sims (1980), is the application of Cholesky decomposition by selecting an mxm lower triangular matrix V to obtain the ortogonalized innovations. After making the transformation, Eq. (2) can be rewritten as: Rt =

∞ 

(Ai V )(V

−1

i=0

ut−i ) +

∞ 

Bi Dt−i ,

t = 1, 2, . . . , T,

(4)

i=0

The m × 1 vector of the orthogonalized impulse response function of one standard error shock to the jth currency on Rt+n is given by: ψorth (n) = An Vej ,

n = 0, 1, 2, . . .

(5)

where ej is an m × 1 selection vector such that the jth element is equal to one and other elements are zeroes. The Cholesky decomposition assumes that a shock in the first market, in a pre-specified ordering, has an immediate impact on all other markets in the VAR system; the second market has an immediate impact on all markets except the first one, and so on. Therefore, the innovation accounting result based on Cholesky factorization is not invariant to the ordering of the variables. Cooley and LeRoy (1985) have criticized the VAR methodology because of its atheoretical identification scheme. As an alternative to the unrestricted VAR, Bernanke (1986), Blanchard and Watson (1986), Sims (1986) and Blanchard and Quah (1989) developed a structural VAR model. In contrast to unrestricted VAR, the structural VAR model attempts to identify the VD and IRF functions by imposing a priori restrictions based on economic theories. The problem is, however, that the constraints due to short-run economic relationships are more controversial than those due to the long-run dynamics. In addition, as the model gets larger, the number of restrictions also increases. The limitations of the unrestricted VAR and the structural VAR led to the development of the generalized VAR, with an advantage that it does not require orthogonalization of shocks and it is invariant to the ordering of the variables in the VAR system. The generalized VAR approach is ideally suited to examine the intra-daily spillover of risk and returns in the currency markets. This is because in this case, it is difficult to impose any a priori restrictions or pre-specify any variable ordering scheme that is based on economic theory. The idea was developed by Koop et al. (1996) for non-linear impulse response analysis and was applied to unrestricted VAR and cointegrated VAR by Pesaran and Shin (1998). The generalized impulse response analysis and the variance decomposition also rely on Eq. (2). Assuming that ut has a multivariate normal distribution, the scaled generalized impulse response

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function can be presented as:11  ej , ψgen (n) = (σjj )−1/2 An

n = 0, 1, 2, . . . .

317

(6)

The generalized impulse response function can provide insight into how significantly an innovation, often measured by one standard error shock in a particular currency market at time t, may affect other currency markets through dynamic interaction at time t + n. The above generalized impulse response functions are used to derive the forecast error variance decompositions defined as: n    2 e i Al e j σ −1 gen i=0   , i, j = 0, 1, 2, . . . , m (7) θi,j (n) = iin Al ei e A i=0 i l where θ denotes the forecast error variance decomposition. Due to non-zero covariance between  gen θ (n) = 1 (Pesaran & Shin, 1998). The the shocks, the following relationship will hold: m j=1 i,j generalized forecast error variance decomposition shows to what extent return variability in one currency market can be explained by the innovations from other markets in the VAR system. As a result, the variance decomposition analyses provide an important insight into the relative importance of each market in the system. The generalized VAR produces an expression that is independent of ordering of variables or any other a priori restrictions on the variance–covariance matrix of the reduced form residuals. Similarly, the generalized VAR does not attempt to recover any structural shocks. Instead, the analysis describes how the system behaves after a specific historical shock, taking into account the correlation among the shocks. Since the historical shocks are not orthogonal, the sum of forecast error variance decompositions does not add up to 100%.12 3. Data and stationarity The data set employed in this study is based on hourly observations on U.S. Dollar denominated futures quotes for four major foreign exchange contracts from the Chicago Mercantile Exchange: British Pound (BP), Deutsche Mark (DM), Swiss Franc (SF), and Japanese Yen (JY).13 Firstnearby futures prices are used because of their high trade volume and high liquidity. Hourly data offers two advantages. First, it allows statistical analysis of relatively longer periods of time, e.g., a year, than a higher-frequency data. Second, it avoids any potential problems, such as lack of data, due to slowness or lack of trading activity in smaller time intervals. The 1-h interval data set spans the period between 1/3/1985 and 12/31/2005 and includes a total of 36,048 observations. All variables are in log difference form. As Brenner and Kroner (1995) show, stochastic trends are common in financial data. In particular, financial price series are generally integrated of order one. It is well-known that, under 11

For derivation of the triangular orthogonalization and generalized IRF procedures, see Pesaran and Shin (1998). For recent applications of the generalized variance decomposition and further description of this procedure, see e.g., Wang, Kutan, and Yang (2005), Darrat and Zhong (2005), Yang, Min, and Li (2003), and Yang, Kolari et al. (2003). As an alternative to this procedure, Swanson and Granger (1997) have introduced a method for testing structural models based on overidentifying restrictions and data-determined ordering of the errors. Bessler and Yang (2003) use this framework to examine the causal structure among innovations in international stock markets. This approach also avoids the sensitivity of the results to the ordering of the variables. We would like to thank an anonymous referee for bringing these two papers to our attention. 13 The data source is Tick Data. 12

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this condition the use of the ordinary least squares (OLS) method of estimation is inappropriate (Granger & Newbold, 1974). Hence, before the model is estimated, data series must be tested for stationarity in order to investigate the presence of unit roots. The Augmented Dickey and Fuller (ADF) test with and without trend introduced by Dickey and Fuller (1981) are employed for this purpose and are applied to the natural log of currency prices. The non-stationary series are then differenced and variance decomposition analysis is conducted using the stationary series.14 The Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) are used to determine the optimal lag structure of the system. VAR of order 6 is estimated throughout the analysis.15 Variance decomposition is carried out individually for each year in the sample, 1985–2005. Calculation of the variance decomposition for each year allows us to determine the changes in the dynamics of a given currency and the relative shares of its determinants over time. 4. Empirical results Variance decomposition analysis is performed using hourly data in order to account for intraday dynamics across the currencies.16 Several questions constitute the main focus of our analysis. The first question is the degree to which the variations in a given currency futures are driven by the internal dynamics of the currency itself versus the shocks from other currency markets. At an extreme, these variations are determined entirely by own-market forces (segregated markets). Most commonly, however, shocks to one currency do affect the others engendering co-movements. To examine this issue, the null hypothesis of segregation will be tested against the alternative of interdependence.17 A second question is whether the interactions between currencies are mutual and symmetric or manifest a unidirectional leadership–followership (dominating versus dominated) pattern. To investigate this matter, symmetry of the inter-currency effects will be examined for every pair of the currencies considered. A third question concerns durability. Do currency shocks exert a transient (short-lived) effect on one another such that the inter-currency effects are manifested merely when the shocks are triggered, or do the effects sustain themselves over a window of time? To address this question, the patterns of the inter-currency effects are examined at several time-lags, from t-1 to t-24, or four business days, in order to determine the time-profile of these effects. A final issue concerns stability; are relative shares of the internal and external forces stable over time or do they change from one period to another? To examine this issue, we contrast the annual results over the sample period. We also divide the sample into two sub-periods spanning

14

The ADF test results can be obtained from authors. Individual lag structures are tested for 21 different VAR systems. The AIC and SBC criteria suggest lag lengths between two to six periods. Instead of selecting an individual lag structure for each VAR model, a lag order of six is used throughout the system. The use of longer lags has two advantages. First, the use of shorter lags may preclude the capturing of delayed adjustments in one or more markets due to a change in another market. Second, the data set contains six hourly transactions per day for most currencies. By allowing six lags, the VAR system can accommodate delayed responses to innovations in other markets for the whole trading day. Dekker et al. (2001) also use lag structure substantially longer than that suggested by AIC and SBC criteria. 16 Single equation estimation results are not presented to save space. They can be obtained from authors. 17 Own market forces include, e.g., shocks to the underlying currency from domestic speculation and government interest rate and exchange rate policy decisions. 15

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Graph 1. Variance decomposition of BP 1985–2005 (left hand side scale pertains to BP, and DM; right hand side scale pertains to JY, and SF).

over 1985–1995 and 1996–2005 and carry out a test of equality of variance decomposition shares in the pre- and post-1995 periods. These issues will be discussed next. 4.1. Internal versus external forces Investigation of the relative magnitude of the internal forces versus the external shocks in determination of the forecast errors in currency futures returns, and the speed of shock spillover are of interest because of several reasons. First, the prevalence of spillover establishes the presence of interdependencies among markets and the need to take them into account in currency investment, risk diversification, and exchange rate and interest rate policy formulation. Second, the magnitudes of internal/external forces provide an indication of the intensity of integration among various foreign exchange markets. Specifically, the stronger the internal dynamics are in determination of the movements in a given currency, the weaker the interdependence between this and other currencies and the less susceptible the currency will be to shocks originating in other currencies. If currency markets are not highly integrated, they are more suitable for risk diversification. Third, the speed of shock transmission has serious implications on risk diversification, currency arbitrage profitability, and success in prevention of financial contagion. Specifically, the faster the information gets transmitted, the lower the probability of a gainful arbitrage and the more difficult it will be to prevent a contagion. The annual VD results for the four currencies, British Pound, German Mark, Swiss Franc, and Japanese Yen, over the period between 1985 and 2005, are presented in Graphs 1–4, respectively.18 Note that, if the variables in the VAR system are correlated, it will be difficult to separate the effect of changes in one currency from those of the others. Hence, prevalence of strong multicollinearity among the explanatory variables would be an indication that own and external effects are not easily distinguishable. In this case, one can only discuss the effect of a joint shock to the variables,

18 The sum of the forecast error VD shares in generalized VAR does not add up to 100%, unless the variance–covariance matrix is orthogonal. Since no variance-covariance matrix is found to be orthogonal here, for ease of presentation, all forecast error VDs have been standardized for each of the markets so that the sum is 100%. It should be noted that limiting the VD to four currencies forces the effect of any other currencies to zero. However, based on international trade and currency transactions data, these four currencies are deemed to be the most important explanatory variables for each other. Moreover, extending the model to more currencies is likely to result in multicollinearity and hence, unreliable coefficient estimates and tests. This feature is common to all VD-based analyses.

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Graph 2. Variance decomposition of DM 1985–1999 (left hand side scale pertains to BP, and DM; right hand side scale pertains to JY and SF).

Graph 3. Variance decomposition of JY 1985–2005 (left hand side scale pertains to BP, and DM; right hand side scale pertains to JY, and SF).

associated with a shock to a particular variable. To determine the significance of this problem in the current model, contemporaneous correlations among the variables and the collinearity index values are calculated. Correlations values are found to be mostly small in magnitude, indicating the absence of any serious multicollinearity. More importantly, the Conditional Index values,

Graph 4. Variance decomposition of SF 1985–2005 (left hand side scale pertains to BP, and DM; right hand side scale pertains to JY, and SF).

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defined as the square root of the (Maximum Eigenvalue/Minimum Eigenvalue), are found to be less than 10, confirming the absence of multicollinearilty. Nevertheless, the separation of internal versus external factors in the discussion below should be interpreted with caution. 4.2. The British Pound As seen in the BP graph, the own-market effect of the BP follows a slight upward trend in the late 1980s and 1990s, reaches its peak in the year 2000, and then declines. More specifically, the share of the internal dynamics in determination of the BP futures rose from 38% in 1985 to 72% in 1996, shot up to a peak of 81% in 2000, and has been receding since that period. In general, the dynamics of BP can be separated into three periods between 1985 and 2005. In the first phase, 1985–1995 we observe that the internal effects crawl up, while there is not a notable change in the BP-DM, and BP-SF interdependencies. The impact of the Japanese Yen seems to have reduced by a few percentage points in this period. Increased segregation of BP in the early 1990s is in accord with U.K.’s decision to abandon the European Monetary System (EMS), and was also contributed to by reduced policy co-ordination in the industrialized world. In the 1996–2001 period, we observe a notable rise in the internal dynamics of BP. This rise coincides with the reduction of DM, SF and JY effects of 5–10 percentage points, in each case. This period coincides with a number of major events in Britain, which economically raised Britain’s fences allowing the internal effects to gain steam. The first event was the failure of Barings Bank, which shook the economy unexpectedly, and the shock from which remained in the system for a few years. Second, in 1996, The Bank of England decided to trim the key interest rates which gave the domestic forces and BP an impetus vis-`a-vis other currencies. The EU followed with a similar cut with a 3-year lag, in 1999 leading to a reverse effect. Third, in the same time period (1996–1999) UK struggled with a massive “mad cow” crisis at home, which adversely affected its agricultural industry and its exports. Fourth, the autonomy of BP reached its peak with the escalation of tensions and military confrontations in the Middle East in 1999–2001: the Iraqi war brought UK economically and politically in almost perfect alignment with the US—which was perceived as a drift away from the majority of European countries and rest of the world. The third phase in the BP dynamics is the 2001–2005 period. This period started with the calming down of the political and diplomatic shocks that were induced by the Iraqi war. This period also marks the start of a new era in Europe as the European Union (EU) introduced the new Euro physically to the markets in its member countries, and Western Europe became a market dominated by three closely linked currencies: the Euro, British Pound and Swiss Franc. We note this effect by observing the relationship between the BP and the SF: The SF impact on BP hovers around low 20% between 1985 and 1995, it dips to 9–19% range between 1996 and 2000, and rebounds to 24–32% in the 2001–2005 period, which illustrates the easing up of the BP to external market movements. 4.2.1. The German Mark In the case of DM, the own effect is relatively steady for the 1985–1995 period, with a slight upward slope. The increase in its magnitude in the subsequent period (1996–1999) basically reflects the rise in the autonomy of BP rather than a systemic change in the German economy. This is further confirmed by the rather tight and robust relationship between the SF and DM over the observation period: the SF’s impact on DM in the 25–35% range. In contrast, we note that the JY’s influence on DM loses steam in the 1990s.

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An interesting feature of the DM case is the strong substitutability of the own effect and the BP effect; whenever BP increases in influence, DM’s own influence decreases, and vice versa. With BP and DM serving as major reserve currencies after the Dollar, the two currencies seem to be combating over power. BP’s challenge to DM reaches its high in 1991 (at 28%) when its influence on the latter is very similar in magnitude to the DM’s internal effect (32%). Recall that this is the period that Germany is coping with problems of East and West German unification. After 1992, when UK withdraws from the ERM, DM shakes off the influence of BP to become more self-determined, though it succeeds only slightly. This reflects the gradual increase in dominance of the German economy and the DM in the European markets. The counter movements between DM and BP provided a solid avenue to investors for hedging the exchange rate risk exposure to these currencies. Knowledge of this pattern would have also been useful for policy makers in other industrialized countries in formulation of their exchange rate policies. 4.2.2. The Japanese Yen In the case of the Japanese Yen, the rise of the own-market effect is notably more pronounced: during the time period analyzed, the internal effects demonstrated a rise from 30–40 to 75% in 1999, and to 95–98% in 2000–2001 period. The internal effect bounced back after 2001, and slowly reverted back to 50% by 2005 from its peak in 2000. The higher share of own-market effect in the early 1990s is likely to be due to the fact that Japan is a rather closed economy and it was relatively insulated from the political turbulence in Europe during this period. The sharp decline in the real estate and stock values, and the crisis in the Japanese banking system in the later years of the 1980s, also drove away many international investors, furthered Japan’s isolation, and limited the effect of foreign shocks. The observed dynamics in the 1999–2001 closely reflects the economic events in the country including the passage of the budget at $682.5 billion, in 1999, by Japan’s lower house with a huge spending increases and tax cuts, as well as the subsequent fall in the short term interest rates to near zero levels (0.02%). In early 2000, the government officially acknowledged that the country had entered an economic recession. The situation worsened subsequently when the primer minister Obuchi suffered a stroke and passed away in May 2000. Yoshiro Mori became the new prime minister, and Japan witnessed the biggest corporate failure since World War II soon thereafter (Kyori Life Insurance failure). The political situation worsened when it was discovered that the minister of economy was involved in a bribery scandal in January of 2001, and finally when in March of 2001 Prime Minister Mori announced that he would resign shortly. During this period, Nikkei dropped to a 16-year low and Bank of Japan lowered the interest rates to zero percent. The situation started being stabilized with the formation of a new government headed by Prime Minister Koizumi in 2001, who proposed aggressive economic reforms, asked Japanese banks to solve their bad loan problems within a 2–3 year timeframe, and started thawing and improving the Japanese stance in Asia–Pacific diplomatically and economically. 4.2.3. The Swiss Franc In the case of the SF, the internal dynamics typically account for 30–40% of the overall SF variation, except for the 2000–2005 period, which is fairly similar to the DM dynamics we observed. The spikes in the years 2000–2001 are reflections of a period that is marred by military confrontation in the Middle East and a series of political crises in Japan: these special circumstances led the internal effect to shoot up to the 73–80% levels for SF for 2000 and 2001. In the subsequent years, however, we note a reversion of the own effect to levels in the 40% range.

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A notable point is the counter balancing nature of the impact of DM and BP on the SF. One can note that typically when the effect of DM increases (decreases) that of the BP declines (increases). This is simply a manifestation of the competition between the two giant currencies of Europe to take the leadership role and to shape the fate of the other European currencies, including the SF. In terms of magnitudes, DM’s effect on SF varies between 23% and 38%, whereas BP’s influence is about 9–31%. It is noteworthy that BP’s impact dipped to 5–6% levels on SF in 1996–1997 when Bank of England went ahead with the interest rate cuts to rise again to higher levels later. These figures provide partial support for the position held by some observers that, for practical purposes, the Bundesbank acts as the central bank of Europe and sets the tone for the movements of the other currencies. The correlation across these currencies also makes the separation of own and cross-currency effects difficult. Interpretation of these effects should be done with caution and with the dominant role of the DM in mind. An overall point about the pattern of the currencies considered here concerns the increasing trend of the internal forces in the second half of the 1990s and their decline afterwards. This phenomenon may reflect a simple mean reversion pattern or may be driven by the interplay of internal and external forces. The driving forces for increased own effects include the Mexican, Asian, and Russian crises while the major forces leading to increased exposure of the currencies has to be the introduction of Euro as the single currency of the European Union and the pressure it exerts on these competing currencies: BP, SF, and JY. 4.3. Tests of hypotheses 4.3.1. Segregation versus interdependence Currency markets may manifest interdependence because agents in one market attempt to extract information about other markets by observing the trade patterns in those markets (the private information hypothesis). Interdependence also occurs when all markets are simultaneously affected by public announcement of certain news (the public information hypothesis), or when governments quickly react to the policies of other governments or co-ordinate their policy actions with each other.19 The absence of interdependence would support market segregation. Full segregation can be tested as a null of zero external effects (own-market share = 100%) against the alternative of non-zero external effect. Table 1 reports the test results for the nulls of own-market share equal to 100%, 90%, and 80% for the four currencies examined (BP, DM, SF, and JY). The null of full segregation (zero external effect) is decisively rejected for all currencies. Similarly, the null of own share constituting 90% of the total variation (near complete segregation) and 80% (near segregation) can be rejected for all of these currencies. The basic conclusion is that currencies considered here display interdependence of varying degrees and, thus, is not perfect hedging tools for each other. This information is useful for banks and private corporations intending to immunize or partially hedge their portfolios from foreign exchange risk exposure. 4.3.2. Asymmetry of the inter-currency effects The issue of asymmetry (leadership–followership) is of interest because it has implications on risk diversification across currencies, and international policy co-ordination. To see the importance 19 See King and Wadhwani (1990) and Harvey and Huang (1991) for explanation of the private information and public information hypotheses. Elyasiani and Mansur (2003) employ these same concepts in describing interdependence in the banking markets of the U.S., Japan, and Germany.

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Table 1 Test results for market segmentation Currency

H1 : Internal = 100%

H2 : Internal = 90%

H3 : Internal = 80%

BP DM JY SF

−14.31 −49.71 −10.56 −21.86

−11.20 −41.74 −8.00 −18.45

−8.08 −33.77 −5.45 −15.04

Three hypotheses test results are contained in the table; complete segregation, near complete segregation, and near segregation. These hypotheses are presented as: H1 : Share of internal factor = 100% (complete segregation). H2 : Share of internal factor = 90% (near complete segregation). H3 : Share of internal factor = 80% (near segregation). Values presented are those of the t-statistics, calculated with 21 degrees of freedom for all currencies, except DM. The critical value at the 5% level is 2.08. For DM, the t-statistics are calculated with 15 degrees of freedom. The critical value is 2.13. Given the limited number of observation, the test results should be interpreted with caution.

of asymmetry, consider an extreme example where shocks to currency (i) have a unidirectional strong effect on currency (j) such that they determine the latter’s behavior entirely. In this case, investors in currency (i) cannot obtain diversification benefit from holding currency (j) while investors in (j) do benefit from diversifying into (i). This example also demonstrates the role of asymmetry in policy co-ordination. Under this scenario, policy makers in country (j) will have to account for policies in (i), while the reverse will not hold true. Specifically, policies in country (i) can have a dominant effect on country (j) and can, fully or partially, neutralize the latter country’s monetary and exchange rate policies. Under this condition, in times of crises, world monetary organizations (The IMF, the World Bank) will have to watch the countries where larger and unidirectional shocks are likely to originate, much more carefully, because of the threat of worldwide contagion. Asymmetry is examined between each pair of currencies for each year of the sample period at several time-lags. For brevity, results of three select years, 1985, 1996, and 2005 are presented. The extent of asymmetry is gauged by the inequality between the share of currency (i) in VD of currency (j) and the share of currency (j) in VD of currency (i), up to a given point in time (a given lag), after the shock is introduced. Six lag points are considered: 1 h after the shock (lag point 1), 2 h (lag point 2), 4 h (lag point 3), 1-day (lag point 4), 2 days (lag point 5), and 3 days (lag point 6). The results are presented in Table 2. The entries in the table display the extent of asymmetry measured by subtraction of share of currency (j) in VD of currency (i) from the share of currency (i) in VD of currency (j). Note that no formal test of significance can be carried out because there is only one observation at each time lag. According to the figures in Table 2, asymmetry is the mode of behavior between some of the currencies but negligible between others. The asymmetry ranges, in absolute terms, between 0.08% and 6.21%. These figures if expressed as a relative to the magnitude of own effect of the denominator country fall to close the zero to 19% range (as of the own effect). The degree of asymmetry is seen to have varied across the years, while it remained relatively constant within each time period. In 1996, the direction of asymmetry remained consistent with the outcome of 1985, with the exception of BP-JY, which approached symmetry. The observed increase in the DM-BP asymmetry between 1985 and 1996 is probably due to the increase in the power of DM over time

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Table 2 Symmetry results for select years: 1985, 1996, 2005 Lag points

DM-BP

BP-JY

SF-BP

SF-JY

DM-JY

DM-SF

Year 1985 1h 2h 4h 1 Day 2 Days 3 Days

3.789 3.765 3.770 3.770 3.770 3.770

−1.283 −1.323 −1.363 −1.364 −1.364 −1.364

−3.164 −3.124 −3.108 −3.107 −3.107 −3.107

4.653 4.664 4.697 4.697 4.697 4.697

5.063 5.064 5.169 5.170 5.170 5.170

0.499 0.497 0.555 0.555 0.555 0.555

Year 1996 1h 2h 4h 1 Day 2 Days 3 Days

6.101 6.153 6.212 6.213 6.212 6.212

0.227 0.224 0.228 0.086 0.085 0.085

−4.009 −4.077 −4.022 −3.994 −3.993 −3.993

5.062 5.048 5.050 5.167 5.167 5.167

5.338 5.328 5.375 5.451 5.453 5.453

0.929 0.910 0.884 0.865 0.865 0.865

Year 2005 1h 2h 4h 1 Day 2 Days 3 Days

BP-JY

SF-BP

SF-JY

−1.704 −1.715 −1.711 −1.730 −1.731 −1.731

−0.893 −0.894 −0.895 −0.841 −0.841 −0.841

2.634 2.631 2.637 2.631 2.631 2.631

Entries are differences in variance decomposition (VD) shares in percentages. For example, DM-BP is the share of DM in VD of BP less the share of BP in VD of DM. DM data ends in 1999.

and adverse events in the U.K. such as the failure of Barings bank, which weakened its impact on external currencies negatively. In the case of BP-JY, we observe that the direction of asymmetry flip-flops: JY exerted a larger influence on BP than it received in the first and last periods (1985 and 2005, respectively). It is notable, however, that the asymmetry was limited in magnitude in all three periods examined and close to zero in 1996. Similarly, we note that the magnitude of the balance between DM and SF has been less than 1% in the first two periods, which incidentally transpires between the BP and SF after the demise of DM in post-2002 period. Perhaps an explanation of this result is the fact that before the demise of DM, DM and SF were highly integrated currencies with almost symmetric impact flows. This feature currently holds for the relationship between the BP and SF, which have become the two out of the three prevailing currencies in Western Europe. Finally, the table reveals that the SF leads the JY consistently in terms of its influence in all three-observation periods. 4.3.2.1. Durability of the inter-currency effects. It is interesting to know whether the effects of the shocks introduced and the asymmetries manifested are persistent and durable or transient and short-lived. This distinction is important because only permanent shocks are priced in the market. To examine this issue, the time pattern of asymmetry is investigated at several time lags after the shock is injected. Since currency markets clear quite speedily, time-lags are limited to four trading days. The results in Table 2 reveal that, for each given year, the effects do not vary considerably across the time-lags. In other words, the relative strength of the own-market forces versus the external factors is to a good degree steady from the point the shock is triggered until it is fully

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absorbed. The implication is that the effects quick to materialize and long lasting, rather than being short-lived and limited to the initial periods after the shocks are introduced. At least for a window of four trading days the effects seem to sustain their character and manifest durability. 4.3.2.2. Stability of the inter-currency effects. An important question is whether the strength of the linkages across markets changes over time, e.g., in response to market crashes, new monetary systems, or policy co-ordination accords among governments. In particular, a series of events in the late 1980s may have altered the established linkages in the equity and currency markets of the industrialized countries. Events such as the stock market crash of 1987, participation of the United Kingdom in the European Monetary System in 1990, and the German reunification in 1990 have had a profound influence on the international financial system. As an example, some researchers have reported that the stock market crash of 1987 strengthened the linkages across the financial markets of the industrialized countries (Arshanapalli & Doukas, 1993; Fleming, Kirby, & Ostdiek, 1998; Roll, 1989). Similarly, the Mexican crisis of 1995, the Asian crisis of 1997, the Russian bond crisis of 1998, the adoption of the Euro as the single currency of the European Union in 1999, and the terrorist attacks on the World Trade Center in 2001 introduced shock waves in the financial markets of the industrialized world, seriously altering the rules of the game and the workings of the currency markets. It is, of course, likely that currency and stock markets are driven by idiosyncratic forces and, as a result, as one market approaches deeper integration the other moves toward increased segregation. The findings of Klaassen (2000) on foreign exchange markets provide support for this position. Klaassen reports that the major exchange rates in the Western world have indeed, become more loosely, rather than more closely, tied since 1980s due to increased volatility in currency markets. Here, we investigate the stability of the degree of integration of the currency futures markets between the 1985–1995 and 1996–2005 periods. To this end, we specify the null hypothesis as the equality of own-market shares (and hence, stability of the inter-currency effects) between 1985–1995 and 1996–2005 periods. Failure to reject the null would indicate that the own-market share remained unchanged in spite of the aforesaid events in the 1990s and 2001. The test results are reported in Table 3. The diagonal entries in this table represent the own effects and other entries in the first and second rows are the inter-currency effects. Entries in the first and second rows of each section are the mean values of variance decompositions in the preand post-1995 sub-periods. Entries in the third row are the results of the means test (t-stats). Test results show that in 14 out of the 16 cases (88%), the changes in inter-currency effects between the two sub-periods were significant (at 5%). According to these results, all currency markets considered exhibited an overall increase in the internal shares in their variance decomposition between 1985–1995 and 1996–2005 periods. Nevertheless, this finding should not necessarily be regarded as evidence of segregation in the foreign exchange markets because the 1996–2005 period contains two distinct sub-periods in the first half of which the internal effects for the four currencies gained power and in the second half of which the own effects for the three remaining currencies reversed course to demonstrate a declining path. This may simply show a mean reverting behavior. Several contributory factors can help explain these patterns. First, in the aftermath of the stock market crash in 1987, some of the countries involved turned inward intentionally and chose to monitor their currency values more closely in order to insulate themselves from the unwelcome consequences of the outside shocks (such as an international crash) or to at least blunt their effects. Second, increased capital mobility, due to deregulation and technological advancement, heightened market instability, weakening currency linkages as a result (Klaassen, 2000). Third, the high degree of international policy co-ordination called for by the ERM in 1979 and put to

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Table 3 Equality of variance decomposition shares in the pre- and post-1995 sub-periods Sub-periods

Currency innovations BP

By innovations in BP 1985–1995 1996–2005 t-Stat

DM

JY

SF

0.4297 0.6632 −7.2821

0.2284 0.1496 6.4155

0.1276 0.0666 4.5058

0.2144 0.2103 0.3210

By innovations in DM 1985–1995 1996–1999 t-Stat

0.1926 0.0977 6.3405

0.3538 0.4378 −6.6926

0.1616 0.1048 4.0501

0.2919 0.3597 −7.1612

By innovations in JY 1985–1995 1996–2005 t-Stat

0.1340 0.0744 4.2927

0.2002 0.1542 3.7324

0.4684 0.7162 −6.3248

0.1974 0.1477 3.4209

By innovations in SF 1985–1995 1996–2005 t-Stat

0.1834 0.1876 −0.2708

0.2959 0.3636 −6.8611

0.1614 0.1154 3.2643

0.3593 0.5516 −6.5610

Entries in the first and second rows of each section are the mean values of currency shares in variance decomposition in the pre- and post-1995 sub-sample periods. Entries in the third row are the results of the means tests between the values in rows 1 and 2. Values presented are those of the t-statistics, calculated with 21 degrees of freedom. The critical value at the 5% level is 2.08. DM data is from 1985 to 1999 (15 degrees of freedom). The critical value at the 5% level is 2.13. For the second sub-period, DM values are calculated over the time period 1996–1999. The null hypothesis is specified as follows: H0 : Share of currency i in currency j (pre-1995) = share of currency i in currency j (post-1995).

practice in the 1980s, did not continue into the 1990s. For example, the ERM initially limited the fluctuations among member currencies to ±2.25%. However, although the Louvre Accord did not announce any target zones, observers believe that this Act widened the band to ±5% and the range was further extended to ±15% in 1993 after the collapse of the EMS in 1992. The discontinuation of the concerted policy practices prevailing in the earlier years of the ERM opened the door for currency-specific factors to play a paramount role in determining the currency values and curtailed the role of outside forces in this regard. The finding of increased market segmentation in the 1995–2000 period is in accord with Klaassen (2000) and has implications on the size of the gain to be obtained from currency diversification.20 The reversal trend can be attributed to the dramatic appearance of the Euro in the world financial markets in 1999, and the terrorist attacks of 2001. 5. Summary and conclusions The breakdowns in international financial system such as the Mexican crisis of 1995, the Asian crisis of 1997, and the Russian crisis of 1998 are gruesome reminders of the interdependencies

20 Any conflict in the findings here and the extant studies may also be due to the use of intra-day data and generalized VAR.

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in world financial markets. Linkages among markets through security trading activity, hedging arrangements, and interest rate differentials are some of the factors leading to co-movement of different currencies. Given the serious implications of the spillover of shocks across currencies in terms of risk management, arbitrage profitability, and international policy co-ordination, presence and the patterns of the interdependence need to be examined. This paper examines the short-term dynamics in currency futures using the generalized variance decomposition technique. In particular, it sheds light on the existence, direction, and magnitude of spillovers across four major currencies, the BP, DM, SF, and JY. Several results are noteworthy. First, the degree of interdependence of the currencies analyzed is considerable with external forces contributing up to two thirds of the variations in some of the currencies at certain points in time. Second, spillover between some of the currencies considered is typically asymmetric in nature. We note that DM exerted an asymmetric effect on the other three currencies, hence playing the role of a leader, before its disappearance from the scene. BP’s lead over SF seems to have subsided in the recent period, where in a bivariate comparison SF continues to lead the Japanese Yen over time. That said, the relationship between DM and SF in the first two periods and the interaction between BP and SF is within one percentage point in absolute value, and for practical purposes one can consider them as being ‘symmetric’. Third, the contribution of a currency in explaining the variations in another does not stay unchanged over the sample period. In particular, the degree of own-market determination for all currencies increased in the 1995–2000 period indicating a looser link among the markets but declined thereafter in response to the introduction of the Euro, the attack on the World Trade Center, and the adverse impacts of the 2000–2001 Middle East crisis that disturbed the global market equilibrium. Acknowledgements Elyas Elyasiani would like to thank Temple and Hebrew Universities for support. He gratefully acknowledges support for purchase of data from the Cochran Research Center at Temple University. Thanks are also due to Ora Elyasiani for technical assistance, and to Bruce Rader, Eric Tsai, Wanli Zhao, and an anonymous referee of the Journal for helpful comments and suggestions. Names appear in alphabetic order. Ahmet E. Kocagil acknowledges financial support from MICASU University fellowship at Pennsylvania State University. Iqbal Mansur gratefully acknowledges support from SBA Summer Research grant. References Arshanapalli, B., & Doukas, J. (1993). International stock market linkages: Evidence from the pre- and post-October 1987 period. Journal of Banking and Finance, 17, 193–208. Baillie, R. T., & Bollerslev, T. (1989). Common stochastic trends in a system of exchange rates. Journal of Finance, 44, 167–181. Baillie, R. T., & Bollerslev, T. (1990). A multivariate generalized arch approach to modeling risk premia in forward foreign exchange rate markets. Journal of International Money and Finance, 9, 309–324. Baillie, R. T., & Bollerslev, T. (1991). Intra-day and intermarket volatility in foreign exchange rates. Review of Economic Studies, 58(3), 565–585. Barnhart, S. W., & Szakmary, A. C. (1991). Testing the unbiased forward rate hypothesis: Evidence on unit roots, cointegration, and stochastic coefficients. Journal of Financial and Quantitative Analysis, 26, 245–267. Becker, K. G., Finnerty, J. E., & Friedman, J. (1995). Economic news and equity market linkages between the U.S. and U.K. Journal of Banking and Finance, 19(7), 1191–1210.

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