Trading location and equity returns: Evidence from US trading of British cross-listed firms

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  • Int. Fin. Markets, Inst. and Money 19 (2009) 729741

    Contents lists available at ScienceDirect

    Journal of International FinancialMarkets, Institutions & Money

    journal homepage: intf in

    Trading location and equity returns: Evidence from UStrading of British cross-listed rms

    Jun Chen, Yiuman Tse , Michael WilliamsDepartment of Finance, College of Business, University of Texas at San Antonio at San Antonio, One UTSA Circle,San Antonio, TX 78249, United States

    a r t i c l e i n f o

    Article history:Received 2 May 2009Accepted 27 May 2009Available online 6 June 2009

    JEL classication:F30G15

    Keywords:Trading locationCross-listed stocksADRs

    a b s t r a c t

    Our study examines market sentiment and the importance of trad-ing location in British American Depository Receipts (ADRs) tradedin the US. Perfect integration between UK markets and UK ADRs isruled out given that UK ADRs exhibit an intraday, U-shaped volatil-ity curve. Both a variance decomposition analysis and an EGARCHmodel show that UK ADR returns are driven more by US marketreturns than US-traded UK ETF returns. These results indicate theexistence of US market sentiment for UK ADRs and that tradinglocation inuences pricing behavior.

    2009 Elsevier B.V. All rights reserved.

    1. Introduction

    While previous literature agrees that international nancial markets have become increasinglycorrelated and integrated, recent studies show that equity returns are correlated more (less) with themarkets where they are traded (where their businesses are located). However, the literature is unclearas to what might cause country-specic market sentiment. As a continuation of the existing litera-ture, our study examines intraday return and volatility characteristics of British American DepositoryReceipts (ADRs) listed on the New York Stock Exchange (NYSE) from January 2002 to December 2005.Doing so allows us to determinewhether there is USmarket sentiment for British ADRs, whether trad-ing location impacts equity pricing behavior, and what may cause trading location to inuence equitypricing behavior.

    Corresponding author. Tel.: +1 210 458 2503; fax: +1 210 458 2515.E-mail address: (Y. Tse).

    1042-4431/$ see front matter 2009 Elsevier B.V. All rights reserved.doi:10.1016/j.intn.2009.05.001

  • 730 J. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 729741

    Under perfect market integration, the pricing of cross-listed nancial assets should be predom-inantly driven by fundamental information. Under partial market segmentation, non-fundamentalfactors such as trading location may have signicant impacts on cross-listed asset returns (Bodurthaet al., 1995). The majority of previous literature nds evidence supporting country-specic marketsentiment, indicating that trading location matters.

    For example, Werner and Kleidon (1996) examine the intraday trading patterns of British cross-listed rms traded on both US and UK markets. In this study, cross-listed equities are comparedagainst a control group of single-market equities. Their results show that London and New York trad-ing of British cross-listed rms is partially segmented. One explanation for their nding is that USinvestors are unable to perfectly substitute ADRs for the underlying shares traded in London. Thisnon-substitutability leads to cross-border order ow competition which affects trading activity andreturns behavior.

    Tse (1999) studies tradingactivity in10-year JapaneseGovernmentBond futures contracts tradedontwo markets: the London International Financial Futures and Options Exchange (LIFFE) and the TokyoStock Exchange (TSE). Tse nds thatwhile the two exchanges bond contracts are nearly identical, LIFFEtraders do not view trading on the TSE as an extension of LIFFE trading. Rather, investors prefer to tradein an environment withwhich they are familiar. A consequence of this homemarket preference is thattwomarkets canbe segmenteddespite underlying similarities in both tradingmechanismand contractspecication (see also Tse, 1998).

    A similar argument is proposed by Chan et al. (2003)who study equity trading on the Jardine Groupwhose share listings changed from the Hong Kong Stock Exchange to the Singapore Stock Exchange.Post Hong Kong delisting, the authors nd that returns for Jardine Groups shares are more stronglycorrelated with the Singapore exchange than with the Hong Kong exchange. These equity correlationspersist even though Jardines core business remained in Hong Kong. These results show that returnsare mainly inuenced by activity and information in the trading market than on the home exchange.

    Several papers, e.g., Grossmann et al. (2007), Suh (2003), Suarez (2005), and Wahab and Lashgari(1992), have examined price discrepancies betweenADRs and their underlying shares. Consistentwiththe ndings of Bodurtha et al. (1995) regarding closed-end funds, Grossman et al. and Suh nd thatADR returns are driven by US market sentiment.

    As a continuation of the existing literature, our study examines the link between trading loca-tion and equity returns. Specically, we examine intraday data for British ADRs traded on the NYSEto determine whether UK ADR returns are affected by trading location thus leading to US marketsentiment.

    ADRs are shares of a foreign company which trade on US nancial markets and are supported bya US depository institution. These ADRs facilitate US investors participation in foreign rms withoutadditional cross-border transaction costs. As such, examining ADR trading provides a glimpse into theactions of international investors without the confounding effects of transaction costs. Focusing onUK ADRs also provides two additional benets not consistently addressed in the literature. The rst isthat the ADRs and the US and UK index funds examined in this study are all traded on the US stockmarket. This has the benet of avoiding biases inherent in dualmarket studies such as differing foreignexchange rates and trading hours.

    The second additional benet of choosing British ADRs is that our study avoids the confoundingeffects of different trading platforms. Specically, some of the existing literature regarding marketsentiment employs index futures contracts as a measure of foreign returns. Given that trading mech-anisms may differ between futures and equity exchanges, using index futures as a proxy for foreignequity returns can add platform bias into the analysis. Our selection of ADRs avoids such bias.

    We nd that trading location affects UK ADR pricing behavior, indicating partial market segmen-tation. The rst analysis focuses on intraday British ADR volatility. As seen in other studies of marketsegmentation, UKADR intraday volatility exhibits a U-shaped pattern. That is, volatility ismost intenseduring the opening and closing trading hours. This is despite the fact that the US market opens afterthe UKmarket and that the twomarkets overlap at the rst 2h of the New York trading session. Basedon the analysis ofWerner and Kleidon (1996) and Tse (1999), this U-shaped intraday pattern indicatesthatUKADRs are partially segmented from theUKhomemarket. This result is consistentwith previousliterature that shows that trading location inuences equity returns.

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    This studys second analysis involves estimating a Vector Autoregression (VAR) of returns betweenan S&P500 Index Fund, aUKmarket index fund, and an equallyweightedportfolio of BritishADRs. Boththe US and UK index funds are exchange-traded funds (ETFs) traded in the US. Therefore, the intradayresults will not be biased by the non-synchronous trading problem. By employing Granger causalitytests and examining the VARs Generalized Impulse Response Functions over 30-min intervals, we ndthat US market returns lead British ADR returns while UK market returns do not.

    Performing aGeneralizedVarianceDecomposition on theVAR,wend that S&P500 returns explaina sizeable portion of UK ADR returns variation while UK index fund returns provide little explanatorypower. The returns Granger causality, Impulse Response Function, and Variance Decomposition anal-yses all indicate that US markets impact UK ADRs more than UK markets. These results suggest thatUS investors view British ADRs more as domestic issues than foreign issues. This, in turn, implies thatUS market sentiment for British ADRs exists and emphasizes the importance of trading location onequity returns.

    Granger causality tests on the absolute estimated residuals from the VAR model indicate a sig-nicant volatility interaction effect. Specically, signicant volatility transmission occurs among theUS index, the UK index, and UK ADRs. Given that volatility transmission is associated with infor-mation transmission (Ross, 1989), bidirectional volatility spillover between the US and UK marketssuggests that US and UKmarkets are informationally integrated. This, in turn, implies that informationsegmentation cannot explain US market sentiment for British ADRs.

    Our thirdanalysis employsamultivariateEGARCHmodel toexaminevolatility spillover amongmar-kets. Controlling for asymmetric volatility effects, we nd only unidirectional volatility transmissionfrom the US index to UK ADR markets. This result provides evidence of partial information segmen-tation between the UK index and UK ADR markets. Stated differently, although information transmitsefciently between the US and UK markets, investors in US markets may ignore information owsfrom UK markets and may regard UK ADRs as local equities. The above results again suggest that USmarket sentiment for British ADRs is not explained by UK-to-US information segmentation.

    This study continues in Section 2 with a review of the studys dataset and methodology. Section3 details the studys empirical results while Section 4 summarizes the studys ndings and providessome concluding remarks.

    2. Methodology

    2.1. Dataset

    This study examines USmarket sentiment for UK ADRs (i.e., the effect of trading location on equityreturns) by examining the intraday returns and volatility characteristics of a US index fund, a UK indexfund, and an equally weighted portfolio of UK ADRs all of which trade on US exchanges. The ADRslisted on the NYSE are identied from the Center for Research in Securities Prices (CRSP) data tapesand the NYSE website. Intraday data for each instrument are collected from the TAQ database for theperiod spanning January 2002 toDecember 2005 representing trading activity on theNYSE, Amex, andother US regional exchanges. 30min data windows are selected over more granular periods to ensuresufcient trading activity.

    The UKmarket is represented byMSCIs United Kingdom Index Fund iShares (ticker symbol: EWU)traded in the US. iShares are exchange-traded funds issued by Barclays Global Investors. EWU is acapitalization-weighted index covering 85% of the publicly available UK market capitalization.

    The equally weighted UK ADR portfolio (denoted by EADR) consists of eight UK ADRs traded onthe NYSE. These eight ADRs are chosen for their relatively high level of trading activity and are thefollowing companies: AstraZeneca, British Petroleum, Cadbury Schweppes, Diageo, GlaxoSmithKline,HSBC Holdings, Scottish Power Limited, and Unilever. The equally weighted portfolio thus consists ofa simple average of individual ADR returns. Note that each of the eight individual UK ADRs traded atleast once per hour over the entire sample period.

    USmarket activity is proxied by S&P 500 Index Fund iShares (IVV). The IVV is preferred over the SPYETF given that IVV volume is a closer match to the volume activity of UK ADRs. For example, averagedaily dollar volume for individual equities within the UK ADR portfolio is $39.7 million for the full

  • 732 J. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 729741

    sample period. Daily dollar volume for IVV and SPY over the same period is $57.1 million and $4437million, respectively. Thus, IVV has a more comparable trading volume to the average UK ADR thanthe SPY. We note that EWU has a much lower volume, $3.0 million, than ADRs and IVV.

    2.2. Vector autoregressive modeling

    To examine leadlag relationships, a Vector Autoregression (VAR)with four lags is estimatedwhereIVV, EWU, and EADR returns are simultaneously modeled. This VAR(4) is represented as follows:

    EADRt=a1 +4

    i=1b1iEADRti +


    c1iIVVti +4

    i=1d1iEWUti + g1Dopen + k1Dclose + 1,t


    IVVt = a2 +4

    i=1b2iEADRti +


    c2iIVVti +4

    i=1d2iEWUti + g2Dopen + k2Dclose + 2,t


    EWUt = a3 +4

    i=1b3iEADRti +


    c3iIVVti +4

    i=1d3iEWUti + g3Dopen + k3Dclose + 3,t


    Note that Eqs. (1a)(1c) contain two indicator variables. Specically, each equation contains Dopenand Dclose which equal one for the rst 30 (9:30 a.m. to 10:00 a.m.) and last 30min (3:30p.m. to4:00p.m.) of trading activity, respectively. These indicator variables are included to eliminate anyopening and closing trading effects on returns. From the VAR(4) above, Granger causality tests areperformed by usingWald coefcient restriction tests on both own-to-own and cross-VAR parameters.

    We also use Generalized Impulse Response Functions (GIRFs) to examine the response of IVV, EWU,and EADR returns over time. Unlike traditional IRFs, GIRFs do not rely on arbitrary coefcient restric-tions. This gives our GIRF analysis the advantage of being free from ordering assumptions (see, e.g.,Cheung and Yuen, 2002). Generalized Variance Decompositions are employed to examine explanatoryrelationships between IVV, EWU, and EADR returns.

    To examine volatility and hence information transmission, a second VAR(4) is estimated using theabsolute residuals captured from Eqs. (1a)(1c). This model is represented as follows:

    |1,t | = e1 +4

    i=1f1i|1,ti| +


    g1i|2,ti| +4

    i=1h1i|3,ti| + p1Dopen + q1Dclose + 1,t (2a)

    |2,t | = e2 +4

    i=1f2i|1,ti| +


    g2i|2,ti| +4

    i=1h2i|3,ti| + p2Dopen + q2Dclose + 2,t (2b)

    |3,t | = e3 +4

    i=1f3i|1,ti| +


    g3i|2,ti| +4

    i=1h3i|3,ti| + p3Dopen + q3Dclose + 3,t (2c)

    As in Eqs. (1a)(1c), Eqs. (2a)(2c) include opening and closing indicator variables to control foropening and closing market effects on volatility. Wald coefcient restriction tests are employed toexamine both own-to-own and cross-market volatility transmission.

    2.3. Multivariate EGARCH model

    The volatility Granger causality tests above assume that volatility transmission is symmetric. Tobetter understand volatility transmission, we employ a multivariate EGARCH model to allow for bothvolatility interdependencies and asymmetries (Nelson, 1991; Engle and Ng, 1993). Specically, we

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    employ aM-EGARCH(1, 1) model where the conditional mean system is identical to Eqs. (1a)(1c) andthe conditional variance system is represented as follows:

    t =(


    )|t1N(0,Ht), Ht =

    21,t 1,21,t2,t 1,31,t3,t1,21,t2,t 22,t 2,32,t3,t

    1,31,t3,t 2,32,t3,t 23,t


    ln(21,t) = 1 + h1Dopent + k1Dcloset + 1G1,t1 + 1G2,t1 + 1G3,t1 + 1 ln(21,t1) (3b)

    ln(22,t) = 2 + h2Dopent + k2Dcloset + 2G1,t1 + 2G2,t1 + 2G3,t1 + 2 ln(22,t1) (3c)

    ln(23,t) = 3 + h3Dopent + k3Dcloset + 3G1,t1 + 3G2,t1 + 3G3,t1 + 3 ln(23,t1) (3d)

    Gi,t = (|ui,t | E|ui,t | + iui,t), ui,t =i,ti,t

    , E|ui,t | =



    where i=1, 2, and 3 for the EADR, IVV, and EWU series, respectively.In this model, the residual vector from the mean system (t) is conditional on an information set

    (t1) spanning one lagged period. The residual vector is assumed to follow a multivariate normaldistribution with zero mean and variancecovariance matrix, Ht (3a). Eqs. (3b), (3c), and (3d) modelthe conditional variances of EADR, IVV, and EWU, respectively. In particular, 1 in Eq. (3b) describesthe volatility spillover from theUS index to the ADRmarket, and 1 describes the spillover from theUKindex to the ADR market. As in the conditional mean system, our M-EGARCH model employs openingand closing indicator variables Dopen and Dclose, respectively.

    Under the assumption of normality, we use the full information likelihood method to estimate theM-EGARCH(1, 1) model which has the following log-likelihood function:

    L() = 0.5(NT) ln(2) 0.5

    (ln |Ht |) + tH1t t (4)

    whereN is the number of equations (3a)(3e), T is the number of time periods, andHt is the covariancematrix (3a) consisting of elements (3b)(3d).

    Inourempirical analysis, a two-stepmethod is implemented toestimate the log-likelihood function.That is, we rst estimate a univariate EGARCH model to determine initial parameter values for themultivariate model. These initial parameter values are then used in estimating the multivariate log-likelihood function (Eq. (4)).

    As stated above, the benet of using amultivariate EGARCHmodel is that asymmetric volatility canbe explicitly measured. Specically, negative signs on the estimated 1, 2, and 3 parameters indicateasymmetric volatility in the EADR, IVV, and EWU series, respectively. Further, our M-EGARCH modelallows us to examine volatility transmission using the sign and statistical signicance of the i, i, and i parameters (Nelson, 1991).

    3. Empirical results

    3.1. Open-to-close and close-to-open returns variance

    Previous literature employs returns volatility as a measure of information ow. For instance, Ross(1989) shows that equity price volatility is directly related to the rate of information ow into themarket. One way to measure the relative contribution of open trading hours on information ow isthe ratio of open-to-close to close-to-open returns variance. Specically, if the variance ratio is greaterthan 1, thenmore information is being released during trading hours as opposed to non-trading hours.

    French and Roll (1986) make use of the variance ratio in trying to explain why equity prices aremore volatile during open market hours. They argue that the higher returns variance during tradinghours is predominately related to informed trading. Barclay et al. (1990) reach a similar conclusion intheir study of equity trading on the Tokyo Stock Exchange. They nd that returns variance is approxi-mately 60% higher on Saturdayswhen the exchange is open relative to Saturdayswhen the exchange is

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    Table 1Descriptive statistics of close-to-close, close-to-open and open-to-close returns.

    Equity returns Mean Variance Skewness Kurtosis

    (Panel A) Summary statisticsEADR close-to-close 0.026 0.857 0.062 2.938

    (0.379) (0.789) (0.656)EADR close-to-open 0.077 0.494 0.184 1.893

    (0.001) (0.008) (0.000)EADR open-to-close 0.102 0.442 1.016 11.43

    (0.000) (0.000) (0.000)IVV close-to-close 0.007 1.188 0.242 2.919

    (0.845) (0.000) (0.700)IVV close-to-open 0.031 0.309 0.468 6.031

    (0.077) (0.000) (0.000)IVV open-to-close 0.023 0.951 0.417 5.172

    (0.450) (0.000) (0.000)EWU close-to-close 0.023 1.501 0.075 3.570

    (0.558) (0.166) (0.000)EWU close-to-open 0.021 0.962 0.290 2.900

    (0.488) (0.000) (0.741)EWU open-to-close 0.003 0.997 0.872 8.066

    (0.931) (0.000) (0.000)

    Close-to-close returns Close-to-open returns Open-to-close returns

    Correlations EADR IVV Correlations EADR IVV Correlations EADR IVV

    (Panel B) Correlation coefcientsIVV 0.657 IVV 0.501 IVV 0.751

    (0.00) (0.00) (0.00)EWU 0.760 0.669 EWU 0.606 0.524 EWU 0.701 0.604

    (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

    The table reports descriptive statistics (Panel A) and contemporaneous correlations (Panel B) of daily close-to-close, close-to-open and open-to-close returns for the equally weighted British ADR portfolio (EADR), S&P 500 Index Fund iShares (IVV),and United Kingdom Index Fund iShares (EWU) returns. In each case the measurement of interest is reported above while theassociated p-value is reported below. Note that returns are reported in percentage terms.

    closed. They state that the increased opening returns variance is not a result of increased trading dura-tion. Rather, increased open hours variance is due to private information being released by informedtraders.

    Table 1 (Panel A) reports descriptive statistics for close-to-close, close-to-open and open-to-closereturns for UK ADRs (EADR), the S&P 500 Index Fund (IVV), and the UK Index Fund (EWU).We nd thatUS market returns variance is 0.951 and 0.309 for the open and closed trading periods, respectively.Thus, US market returns are relatively more volatile during the open trading hours. Further, the ratioof these two variances, 3.078, is greater than 1, indicating the presence of more information arrivalduring the US markets open hours.

    For both UK ADRs and the UK Index Fund, however, returns volatility show a different patternfrom US market returns. Specically, UK ADRs and the UK Index Fund have variance ratios of 0.895and 1.036, respectively. Given that both of these ratios are close to one and that a 2-h overlap existsbetween US and UK trading sessions, we show that the amount of UK-related information releasedduring US trading hours is comparable to that during US non-trading hours.

    Panel B of Table 1 shows that the daily UK ADR (EADR) open-to-close returns are more contempo-raneously correlated with S&P 500 Index Fund (IVV; 0.751) daily open-to-close returns than those ofthe UK Index Fund (EWU; 0.701). We perform a similar correlation analysis to the one above except on30-min sample windows. We again nd that UK ADRs (EADR) are highly related to the S&P 500 IndexFund (IVV; 0.607) whereas they are weakly linked to the UK Index Fund (EWU; 0.208). In contrast,the correlation between the ADR close-to-open returns and the IVV close-to-open returns (0.501) isless than that between the ADR and EWU markets (0.606). These results indicate that UK ADR returnsare driven more by US market returns than UK market returns during US trading hours. This suggests

  • J. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 729741 735

    Fig. 1. Intraday average volatility. The gure presents average intraday volatility for the equally weighted British ADR portfolio(EADR), S&P 500 Index Fund iShare (IVV), and United Kingdom Index Fund iShare (EWU) returns. Note that two scales areemployed for the purpose of clarity. Specically, the IVV and EWU series are plotted using the left scale while the EADR seriesis plotted using the right scale.

    that trading locationmatters for British cross-listed rms and that there is USmarket sentiment in UKADRs.

    Together, these results suggest that US investors apply different investment strategies in twosequential stages. The rst stage takes place during the US non-trading period when the UK mar-ket is active. During this stage, US traders are attentive to UK information ows and, in response tosuch information, rapidly rebalance their portfolios at the opening of US trading. These rebalancingtrades lead to relatively higher levels of overnight returns volatility.

    The second stage takes place during US trading hours after the overlap in USUK trading. Duringthis stage, US traders switch their focus to US information. This refocusing brings in more informedtrading on US issues and relatively higher volatility during the US open hours. The shift in US investorfocus thus leads to US market sentiment for UK ADRs.

    3.2. Intraday volatility patterns

    Fig. 1 displays average intraday returns volatility for UK ADRs (EADR), S&P 500 Index Fund iShares(IVV), and UK Index Fund iShares (EWU). From this gure, we nd that all three series exhibit a U-shaped intradayvolatilitypattern. Specically, all three seriesbegin the tradingsessionwitha relativelyhigh level of volatility. This volatility begins to decline until 1:00p.m. EST. After this time, volatilityincreases until the end of the trading session. In all three cases, opening volatility is higher than closingvolatility. It is also worth noting that the London stock market closing at 11:30 a.m. EST (or 4:30p.m.London time) has no impact on intraday volatility.

    Previous research documents intraday returns volatility exhibiting U-shaped patterns wherevolatility is most intense during the opening and closing market hours (Wood et al., 1985; Harris,1986, among many others). Admati and Peiderer (1988) provide an explanation for this U-shaped

  • 736 J. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 729741

    volatility pattern using the interaction of informed and discretionary uninformed traders. In theirmodel, informed and uninformed traders prefer to trade at the same time given that each offers theother trading benets. An implication of their model is that two markets trading an equivalent assetwill exhibit two different intraday volatility curves. In particular, the market with the highest volumeand lowest transaction costs will exhibit a U-shaped volatility pattern. The volume and cost inferiormarket, however, will not exhibit a U-shaped volatility pattern.

    Assumingperfectmarket segmentation, it is reasonable to assume thatUKmarket participants havemoreprivate informationonUKrms than their American counterparts. UnderAdmati andPeiderersmodel, little interaction should exist between informedanduninformed traders on theUKADRmarket.Further, this lack of trader interaction implies no intraday volatility patterns for UK ADRs. Yet, Fig. 1provides a clear contradiction to the above logic: UK ADRs exhibit a U-shaped volatility curve just asthe IVV and EWU do. Our results, thus, indicate some interaction between informed and uninformedUK traders on theUSmarket. Our results not only contradict Admati and Peiderersmodel predictionsbut also contradict the assumptions of perfect market segmentation.

    A secondpopularmodel of theU-shaped intradayvolatility is proposedbyBrockandKleidon (1992).They argue that investors cannot rebalance their portfolios when the market is closed and thereforemustwait for the openinghours of thenext trading session. The accumulationof overnight informationwithno tradingopportunities results inheavy tradingat theopen toadjustoptimalportfolios. Similarly,investors are wary of rebalancing their portfolios during the closing hours of the trading session giventhe increased uncertainty regarding tomorrows trading activity.

    Given that there is a 2-h overlapping period between US and UK market trading, US investors areable to rebalance their portfolios at the opening hours of the USmarket in response to UK informationows. Therefore, the U-shaped intraday pattern of volatility in Fig. 1 contradicts Brock and Kleidonspredictions under the perfect market integration hypothesis.

    In sum, our results indicate that the UK ADR market is neither perfectly segmented nor per-fectly integrated with the UK market. Our results conrm the ndings of Werner and Kleidon (1996)who study ADR trading and international market segmentation. As in Fig. 1, Werner and Kleidonnd that ADR markets exhibit convex intraday volatility patterns thus contradicting the perfectmarket segmentation hypothesis. Further, given that Ito et al. (1998) nd that private informationprovision causes U-shaped volatility patterns, we conclude that partial UK ADR market segmenta-tion with the UK home market is not due to the absence of information transmission or informedtrading.

    3.3. Returns transmission

    Table 2 reports both own-to-own and cross-Granger causality tests for UK ADR (EADR), S&P 500Index Fund iShare (IVV), and UK Index Fund iShare (EWU) returns.

    We nd twomain results from Table 2. The rst is that own-to-own Granger causality test statisticsfor British ADR returns are insignicant (p-value of 0.427). In other words, British ADR markets areweak-form efcient given that previous own returns cannot predict future own returns (Chordia etal., 2005). The importance of this nding is that subsequent detections of market sentiment cannot beattributed to market inefciency.

    The second main result in Table 2 is that S&P 500 returns Granger-cause future EADR returns (p-value of 0.000) while EWU returns do not (p-value of 0.095). Given that US market returns predictUK ADR returns whereas UK market returns do not, we conclude that the UK ADR market is moreintegrated with the US index than the UK index (as proxied by EWU) during US trading hours.

    To ensure that our results are robust to model specication, we re-estimate the above VAR modelusing 8 and 12 lags separately. We nd that the results remain unchanged both in terms of statisticalsignicance and qualitative interpretation. Thus, we nd that our results are robust with respect tomodel lag specication.

    Fig. 2 reports 10-periodGeneralized Impulse Response Functions (GIRF) for the EADR, IVV, andEWUreturn series. The Granger causality analysis above only examines the presence or lack of signicantpredictive relationships among the return series. TheGIRF analysis below, on the other hand, examinesboth the magnitude and duration of return impacts among the three series.

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    Table 2Granger causality tests on returns.


    EADR Granger-causes3.85 13.36 137.81(0.427) (0.010) (0.000)

    IVV Granger-causes21.55 14.24 43.82(0.000) (0.007) (0.000)

    EWU Granger-causes7.91 4.99 677.82(0.095) (0.288) (0.000)

    The table reports Granger causality tests on the following VAR(4) which models equally weighted BritishADR portfolio (EADR), S&P 500 Index Fund iShare (IVV), and United Kingdom Index Fund iShare (EWU)returns:

    EADRt = a1 +4


    b1iEADRti +4


    c1iIVVti +4


    d1iEWUti + g1Dopen + k1Dclose + 1,t ,

    IVVt = a2 +4


    b2iEADRti +4


    c2iIVVti +4


    d2iEWUti + g2Dopen + k2Dclose + 2,t ,

    EWUt = a3 +4


    b3iEADRti +4


    c3iIVVti +4


    d3iEWUti + g3Dopen + k3Dclose + 3,t .

    The upper value in the table reports the F-statistic of a Wald coefcient restriction test on four parameterswhile the lower value reports the Wald tests associated p-value.

    Table 3Generalized Variance Decomposition of returns.

    Variance decomposition EADR IVV EWU

    EADR 48.60 13.93 1.26IVV 39.11 79.27 2.01EWU 12.28 6.80 96.73

    The table reports the results of a Generalized Variance Decomposition based on the following VAR model comprising equallyweighted British ADR portfolio (EADR), S&P 500 Index Fund iShare (IVV), andUnited Kingdom Index Fund iShare (EWU) returns:

    EADRt = a1 +4


    b1iEADRti +4


    c1iIVVti +4


    d1iEWUti + g1Dopen + k1Dclose + 1,t ,

    IVVt = a2 +4


    b2iEADRti +4


    c2iIVVti +4


    d2iEWUti + g2Dopen + k2Dclose + 2,t ,

    EWUt = a3 +4


    b3iEADRti +4


    c3iIVVti +4


    d3iEWUti + g3Dopen + k3Dclose + 3,t .

    Note that the value in cell(i, j) represents the percentage of variation in variable j explained by four lags of variable i.

  • 738J.Chen





    Fig. 2. Generalized Impulse Response Functions. The gure presents the Generalized Impulse Response Functions estimated from Eqs. (1a)(1c) which model equally weighted BritishADR portfolio (EADR), S&P 500 Index Fund iShare (IVV), and United Kingdom Index Fund iShare (EWU) returns. Panel(i, j) reports the returns response for series i given a 1% shock in seriesj.

  • J. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 729741 739

    Table 4Granger causality tests on volatility.


    EADR Granger-causes93.55 23.05 18.56(0.000) (0.000) (0.001)

    IVV Granger-causes137.04 214.06 99.61(0.000) (0.000) (0.000)

    EWU Granger-causes75.56 105.31 292.95(0.000) (0.000) (0.000)

    The table reports Granger causality tests on the following VAR(4) which models equally weighted BritishADR portfolio (EADR), S&P 500 Index Fund iShare (IVV), and United Kingdom Index Fund iShare (EWU)absolute estimated residuals. Estimated residuals are captured from the VAR(4) described by Eqs. (1a)(1c):

    |1,t | = e1 +4


    f1i|1,ti| +4


    g1i|2,ti| +4


    h1i|3,ti| + p1Dopen + q1Dclose + 1,t ,

    |2,t | = e2 +4


    f2i|1,ti| +4


    g2i|2,ti| +4


    h2i|3,ti| + p2Dopen + q2Dclose + 2,t ,

    |3,t | = e3 +4


    f3i|1,ti| +4


    g3i|2,ti| +4


    h3i|3,ti| + p3Dopen + q3Dclose + 3,t .

    The upper value in the table reports the F-statistic of a Wald coefcient restriction test on four parameterswhile the lower value reports the Wald tests associated p-value.

    Fig. 2 demonstrates that both IVV and EADR respond to return shocks in the other. These shocks areof lessmagnitude but of greater duration than own-to-own return shocks. This indicates that UK ADRsare sensitive to innovations in the US market and vice versa. Further, the IVV to EADR relationship ishigher in magnitude and of longer duration than the EWU to EADR relationship.

    Thus, our results in Fig. 2 support the Granger causality analysis presented in Table 2. Speci-cally, UK ADRs are more sensitive to US market activity than UK market activity. In other words,the GIRF analysis conrms that UK ADR returns are correlated more (less) with the US (UK) marketreturns.

    As a nal examination of returns transmission, Table 3 reports the Generalized Variance Decom-position results for the EADR, IVV, and EWU return series. We nd that own-to-own explanatorypower dominates all but one cross-explanatory relationships. Thus, each series is best explainedby lags of itself and not other series. The one exception to this is for the IVV to EADR relationship.Specically, S&P 500 index returns explain 39.11% of UK ADR returns variation. UK ADR returns donot, however, provide strong explanatory power for S&P 500 index returns. Further, we nd lit-tle unidirectional or bidirectional explanatory power between UK ADRs and the UK index. Theseresults indicate that UK ADR returns are driven by US market returns rather than by UK marketreturns.

    3.4. Volatility transmission

    Table 4 reports both own-to-own and cross-volatility Granger causality tests for the EADR, IVV,and EWU return series. We nd signicant volatility transmission among the US index, the UK index,and UK ADRs (p-values of 0.000). Given that volatility transmission is associated with informationtransmission (Ross, 1989), Table 4 implies bidirectional information transmission between US and

  • 740 J. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 729741

    Table 5Multivariate EGARCH model.


    1 0.0050 2 0.0021 3 0.0009(0.000) (0.000) (0.000)

    h1 0.0010 h2 0.0002 h3 0.0018(0.749) (0.950) (0.000)

    k1 0.0096 k2 0.0029 k3 0.0002(0.000) (0.000) (0.681)

    1 0.0020 2 0.0005 3 0.0006(0.000) (0.002) (0.000)

    1 0.0009 2 0.0011 3 0.0002(0.000) (0.000) (0.045)

    1 0.0003 2 0.0008 3 0.0018(0.943) (0.000) (0.000)

    1 0.9985 2 0.9991 3 0.9992(0.000) (0.000) (0.000)

    1 0.3757 2 0.9501 3 0.3429(0.000) (0.000) (0.000)

    12 0.5605 13 0.2194 23 0.1922(0.000) (0.000) (0.000)

    The table reports the estimation results of a multivariate EGARCH model of equally weighted British ADR portfolio (EADR),S&P 500 Index Fund iShare (IVV), and United Kingdom Index Fund iShare (EWU) returns. The conditional variance system isrepresented as follows:

    t =(


    )|t1N(0,Ht ), Ht =

    (21,t 1,21,t2,t 1,31,t3,t

    1,21,t2,t 22,t 2,32,t3,t1,31,t3,t 2,32,t3,t 23,t


    ln(2i,t) = i + hiDopent + kiDcloset + iG1,t1 + iG2,t1 + iG3,t1 + i ln(2i,t1),

    Gi,t = (|ui,t | E|ui,t | + iui,t ), ui,t =i,ti,t

    , E|ui,t | =



    where i=1, 2, and 3 represent the EADR, IVV, and EWU series, respectively. The upper value in the table reports the estimatedparameter while the lower value reports the associated p-value.

    UK markets. These results suggest that US and UK markets are informationally integrated furthersuggesting that US market sentiment is not explained by information segmentation.

    Table 5 reports conditional variance estimates from a multivariate EGARCH(1, 1) model where theconditional mean model utilizes Eqs. (1a)(1c). Volatility transmission from the US market to BritishADRs is signicant (0.0009 with p-value of 0.000), while the transmission from the UK market toBritish ADRs is not (0.0003with p-value of 0.943). Given that volatility transmission is associatedwithinformation transmission, these results indicate a statistically signicant ow of information betweenthe US market and British ADRs whereas information ows from the UK market to British ADRs areinsignicant.

    We also nd that bilateral volatility transmission between US and UK markets is signicant (p-value of 0.045 for US-to-UK and p-value of 0.000 for UK-to-US transmission). These results conrmthe volatility Granger causality results reported in Table 4 and indicate that US and UK markets areinformationally integrated.

    All three delta parameters (i) are both negative and statistically signicant at the 1% level. Speci-cally, estimation results report0.3757,0.9501, and0.3429 for the 1, 2, and 3 parameters, respec-tively. Consistent with the interpretation of Nelson (1991) and Engle and Ng (1993), the signicantlynegative delta parameters show that EADR, IVV, and EWU returns volatility respond asymmetrically topast innovations. Inparticular, previousbadnews (representedbynegative innovations) in agivenmar-ket increases volatilitymore so than good news (positive innovations). Thus, information transmissionfails to exist between UK and UK ADR markets when asymmetric volatility is controlled for.

  • J. Chen et al. / Int. Fin. Markets, Inst. and Money 19 (2009) 729741 741

    4. Conclusion

    Our study examines USmarket sentiment and the importance of trading location on equity returnsby studying US-traded UK ADRs from 2002 through 2005. US and UK markets are represented byexchange-traded funds IVV and EWU, respectively, both of which trade in the US.

    UKADR returns volatility exhibits aU-shaped intradaypattern, indicating thatUKADRs arepartiallysegmented from the UK home market. Both a variance decomposition analysis and an EGARCH modelshow that UK ADR 30-min returns are better explained by US market returns than by UK marketreturns. We also nd that signicant volatility transmission exists between the US and UK markets.The overall results indicate that US market sentiment for UK ADRs exists and that trading locationimpacts pricing behavior. Even though information ows efciently between the US and UK markets,US investors may treat UK ADRs as local issues.


    Tse acknowledges the nancial support from a summer research grant from U.S. Global InvestorsInc. and the College of Business at The University of Texas at San Antonio.


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    Further reading

    Hamao, Y., Masulis, W.R., Ng, V., 1990. Correlations in price changes and volatility across international stock markets. Review ofFinancial Studies 3, 281307.

    Trading location and equity returns: Evidence from US trading of British cross-listed firmsIntroductionMethodologyDatasetVector autoregressive modelingMultivariate EGARCH model

    Empirical resultsOpen-to-close and close-to-open returns varianceIntraday volatility patternsReturns transmissionVolatility transmission

    ConclusionAcknowledgementsReferencesFurther reading


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