THE UNIVERSITY OF TEXAS AT SAN ANTONIO, COLLEGE OF BUSINESS Working Paper SERIES

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1 THE UNIVERSITY OF TEXAS AT SAN ANTONIO, COLLEGE OF BUSINESS Working Paper SERIES March 11 h, 2009 Wp# 0063FIN Where does Volailiy and Reurn Come From? The Case of Asian ETFs Yiuman Tse Deparmen of Finance The Universiy of Texas a San Anonio One UTSA Circle San Anonio, TX 78249, USA yiuman.se@usa.edu Tel: Jose A. Guierrez The Universiy of Texas a San Anonio One UTSA Circle San Anonio, TX 78249, USA Valeria Marinez Fairfield Universiy Fairfield, CT 06824, USA Deparmen of Finance, Universiy of Texas a San Anonio, San Anonio TX USA Copyrigh 2008, by he auhor(s). Please do no quoe, cie, or reproduce wihou permission from he auhor(s). ONE UTSA CIRCLE SAN ANTONIO, TEXAS BUSINESS.UTSA.EDU

2 Where Does Reurn and Volailiy Come From? The Case of Asian ETFs Jose A. Guierrez a, Valeria Marinez b, Yiuman Tse c,1 a Universiy of Texas a San Anonio, San Anonio, TX 78249, Unied Saes b Fairfield Universiy, Fairfield, CT, 06824, Unied Saes c Universiy of Texas a San Anonio, San Anonio, TX 78249, Unied Saes We analyze reurn and volailiy of Asian ishares raded in he U.S. The difference in rading schedules beween he U.S. and Asia offers a unique marke seing ha allows us o disinguish various reurn and volailiy sources. We find Asian ETFs have higher overnigh volailiy han dayime volailiy, explained by public informaion released during each local marke s rading session. Local Asian markes also play an imporan role in deermining each Asian ETF reurn. Noneheless, reurns for hese funds are highly correlaed wih U.S. markes, indicaive of he effecs of invesor senimen and locaion of rade. Finally, reurns in he U.S. marke Grangercause reurns in all six Asian markes analyzed. JEL Classificaion: F30; G15 March 2009 Keywords: Inernaional ETF; ishares; reurns; variance; diversificaion 1 Correspondence auhor: Yiuman Tse, One UTSA Circle, College of Business, The Universiy of Texas a San Anonio, San Anonio, TX Phone: (210) Fax: (210) yiuman.se@usa.edu. We are graeful o hree anonymous reviewers and he paricipans a he 2008 FMA meeing in Dallas for valuable commens and suggesions. We would also like o hank Barclays Global Invesors for providing he ishares daa. Tse acknowledges he financial suppor from a summer research gran of U.S. Global Invesors, Inc. and he College of Business a The Universiy of Texas a San Anonio

3 1. Inroducion Where does volailiy come from? In previous work, volailiy is explained by public informaion, privae informaion, or noise rading. 2 Because in mos markes all hree effecs ake place a he same ime, deermining which of hese is he source of volailiy can be a difficul ask. There have been various aemps o disenangle hese effecs by aking advanage of exising marke characerisics. For example, Fleming, Kirby, and Osdiek (2006) analyze volailiy for weaher sensiive agriculural and energy markes. Under heir assumpions, if public informaion ha affecs hese markes is released a regular inervals hroughou he day, hen rading and non-rading variances should be equal per uni of ime. If here is higher variance during he rading hours hen i can be aribued o privae informaion and noise. If rading period variance is normally higher han non-rading period variance, and he difference beween hese drops during he weaher sensiive season, hen we can aribue he higher variance o public informaion. Noneheless, if he privae informaion flow also rises in he weaher sensiive season, hen i may be difficul o disenangle hese wo effecs. We exend he work in Fleming e al. (2006), and add value o exising work by analyzing volailiy sources for index-racking Asian Exchange-Traded Funds (ETFs). According o Chordia, Roll, and Subrahmanyam (2002, pg 128), privae informaion is unlikely o be prevalen a he index level. Public informaion should also be considerably more imporan han noise 2 For more informaion, see Oldfield and Rogalski (1980), French and Roll (1986), Amihud and Mendelson (1987), Barclay, Lizenberger, and Warner (1990), Soll and Whaley (1990), Harvey and Huang (1991), Chang, Fukuda, Rhee, and Takano (1993), Chan and Chan (1993), and Fung, Lien, Tse, and Tse (2005)

4 rading in he index marke. 3 This noion, coupled wih he 12-hour difference in schedule beween he U.S. and Asian markes, enables us o isolae he effecs of public informaion. In addiion o volailiy, we also analyze Asian ETF reurns. Various auhors such as Froo and Dabora (1999) find reurns are no only deermined by he underlying asses hey represen bu are also influenced by he inernaional marke in which hey rade. Our resuls show ha Asian ETFs have higher overnigh volailiy han dayime volailiy. We aribue his finding o he release of public informaion which primarily occurs during each of he local marke s rading session. Inraday reurns for he Asian ETFs are significanly Granger-caused by he U.S. marke reurns, bu no he reverse. A closer look a ETF volailiies shows significan bi-direcional Granger causaliy beween he U.S. and all Asian markes used in his sudy, wih a much sronger volailiy spillover from he U.S. o he Asian ETFs. We also find ha Asian local markes play an imporan role in deermining Asian ETF reurns; however, reurns for hese funds are highly correlaed wih U.S. marke reurns. Overall, he impac of public informaion in local Asian markes has a significan impac on ETF reurns. However he U.S. marke plays a deerminan role in explaining Asian ETF reurns and volailiies, suggesing he effecs of invesor senimen and locaion of rade. 2. Lieraure Review There are numerous sudies explaining he sources of volailiy observed in differen markes. Soll and Whaley (1990) argue ha volailiy of dayime reurns is relaed o he release of public informaion during he day. Jones, Kaul, and Lipson (1994a), find ha volailiy is 3 Mos previous sudies incorporaing variance raio ess do no consider he noise rading componen a significan source of volailiy

5 higher on days when exchanges are open han when exchanges are closed, even if no rades occur during open rading ime. French and Roll (1986) posi ha he greaer rading period variance is due o more privae informaion released during his ime period, since raders are more likely o obain his informaion and ac on i during rading hours. Barclay, Lizenberger, and Warner (1990) aribue he higher weekend volailiy on he Tokyo sock exchange o he release of privae informaion. Chan, Fong, Kho, and Sulz (1996) discover ha volailiy paerns for Asian and European sock are consisen wih he arrival of public informaion, bu no privae informaion. Hoque, Kim, and Pyun (2007) use variance raio ess in eigh Asian emerging sock markes and conclude ha six of he eigh are mean-revering, suggesing ha hese markes are no weak-form efficien. Barclay and Hendersho (2003) find ha, for NASDAQ socks, he raio of privae o public informaion is higher during he day han i is during afer-hours rading, when here end o be fewer informed rades and more liquidiy rades. By aking advanage of he naural characerisics of differen financial insrumens we are able o isolae he differen volailiy sources and beer undersand he origin of volailiy in financial markes. Such is he case of Fleming e al. (2006), which analyzes volailiy for weaher-sensiive agriculural and energy markes. This marke seing allows he auhors o differeniae beween he differen sources of volailiy. While privae informaion and noise rading are more likely o occur during he rading session, public informaion on hese producs is evenly disribued hroughou he day. Fleming e al. find ha here is a srong relaionship beween prices and public informaion ha canno be explained by pricing errors or changes in rading aciviy. Thus volailiy in hese markes is driven by public informaion

6 In he curren analysis, we ake advanage of he rading schedule differences for inernaional invesmens, o isolae he public informaion of local foreign markes from privae informaion released during he U.S. rading session. However, when i comes o he analysis of foreign invesmens ha rade ouside heir home counry, here are oher facors ha come ino play. Many of hese invesmens no only reflec public informaion from heir home counry, bu also display characerisics of he inernaional marke in which hey rade. This phenomenon is commonly referred o as locaion of rade or invesor senimen. Evidence of he invesor senimen effec is found in he work of Bodurha, Kim, and Lee (1995), Froo and Dabora (1999), Chan, Hamed, and Lau (2003), and Wang and Jiang (2004). Bodurha e al. (1995) find ha he premiums for he differen inernaional closed-end funds end o move ogeher, reflecing he varying senimen of U.S. invesors. Froo and Dabora (1999) sudy he rading of various company socks ha rade in muliple markes. Afer adjusing for exchange raes, hey conclude he same sock rades a differen prices in differen markes, aribuing heir resuls o counry specific invesor senimen. Cha and Ho (2000) examine he relaionship beween developed equiy markes and four Asian emerging equiy markes. The auhors conclude ha he links beween developed and emerging markes have srenghened considerably since he crash of Chan e al. (2003) analyze he rading aciviy of he Hong Kong based company Jardine Group before and afer he sock was de-lised from he Hong Kong Exchange in Afer delising, he core business of he group is mainained in Hong Kong and mainland China, while mos of he group s rading akes place in Singapore. They discover ha afer delising he group s sock from he Hong Kong marke, reurns are more correlaed wih he Singapore marke and less correlaed wih he Hong Kong marke, consisen wih counry-specific invesor senimen. Wang and Jiang (2004) analyze Chinese - 5 -

7 companies ha issue A shares in mainland China and H Shares in Hong Kong markes. They find H shares have significan exposure o Hong Kong marke facors and behave more like Hong Kong sock han mainland China sock. Given he resuls of prior lieraure, we aemp o answer he following wo quesions for he case of Asian ETFs which are raded in U.S. markes: Does volailiy come from privae informaion, or pubic informaion? Are reurns characerized by locaion of rade or he underlying asses hey represen? 3. Daa Descripion Exchange-raded funds (ETFs) are diversified securiy porfolios ha rack a sock or bond marke index. They can be raded like sock hroughou he day using marke orders, limi orders, sop orders, margin purchases ec. They rade in boh naional and regional U.S. exchanges. ETFs have become very popular due o heir posiive feaures such as ease of rading, diversificaion benefis, low expense fees, and poenial ax advanage. The poenial ax advanage arises from he fac ha, in conras o open-end funds where he creaion or desrucion of shares resuls in a axable even, ETF invesors are no subjec o ax consequences as a resul of invesor demand or liquidaions. ETFs creae and desroy shares hrough in kind ransacions or ransfers of securiies which are a non-axable even for he fund. The process is as follows: every day marke makers receive informaion on he demand for (excess of) securiies needed o creae (desroy) a paricular ETF s shares. Marke makers hen buy (sell) hese securiies in he capial markes and deposi (redeem) hem wih he cusodian who hen issues (desroys) he appropriae number of ETF shares

8 ishares where creaed by Barclays Global Invesors in Since hen, he dollar value invesed in ETFs has grown o approximaely $417 billion, and a he end of 2006, here were nearly 400 differen ETF funds. Inernaional ishares funds rack he Morgan Sanley Capial Indexes which encompass abou 85% of each counry s marke capializaion. Alhough he ETFs closely rack he index hey represen, hey do no fully replicae he index. As a resul, he ETF and he underlying index will no move in locksep. 5 As repored by Lauricella and Gullapalli (2007), ETF prices are no only deermined by fundamenal informaion of he asses hey represen, bu also by supply and demand in he U.S. marke in which hey rade. In addiion, due o he rading schedule difference beween local Asian markes and U.S. markes, prices in each marke will no reflec he same amoun of informaion. Since he U.S. marke opens and closes a a laer ime during he day, on any given rading day he U.S. markes can incorporae addiional informaion beyond ha released during he Asian rading hours. Rules and regulaions for ETFs, may also affec how closely hey rack he index. For example, he IRS single issue rule poins ou ha an ETF canno hold a single posiion ha represens more han 25% of heir porfolio. Based on his rule, an ETF ha racks any counry index ha holds a single posiion of more han 25% of is porfolio will no be able o fully replicae he index. In his sudy we use daily and inraday daa from January 2002 hrough December 2007 for he following six Asian ishares funds: Hong Kong (EWH), Japan (EWJ), Malaysia (EWM), Singapore (EWS), Taiwan (EWT), Souh Korea (EWY), and for he S&P500 (IVV) ishares 4 For more informaion on ishares, see Tse and Marinez (2007). 5 For he sample of funds used in his analysis he correlaion beween he ETF dayime reurns and corresponding local index reurns ranges from 27% o 61%

9 fund. Daily price daa and local marke index fuures prices come from Commodiy Sysems Incorporaed (CSI). We also use inraday rade daa from he Trade and Quoe daabase (TAQ). Iniially lised on he AMEX, he ishares used in his analysis migraed o he NYSE in November Dayime reurns are esimaed as he log difference beween he closing ( CL ) and opening ( OP ) prices on day. Overnigh reurns are he log difference beween he opening price on day ( OP ) and he closing price on day 1 ( CL 1 ). 24-hour reurns are esimaed as he log difference beween he closing price on day ( CL ) and he closing price on day 1 ( CL ) 1 for each ETF. Dayime reurns = log( CL ) log( OP ) (1) Overnigh reurns = log( OP ) log( CL 1) (2) 24-hour reurns = log( CL ) log( CL 1) (3) Panel A of Table 1 shows average daily dayime, overnigh, and 24-hour reurns, as well as sandard deviaions for each Asian ETF and he U.S. ETF. Consisen wih marke efficiency, dayime reurns and overnigh reurns are boh insignificanly differen from zero. For all Asian markes, overnigh volailiy is greaer han dayime volailiy, bu for he U.S. marke, dayime volailiy is greaer han overnigh volailiy. Daily average dollar volume measured in shares indicaes ha he hree mos acive ETFs are Japan ( million), U.S. ( million), and Korea (33.33 million). [ Inser Table 1 Here ] - 8 -

10 Panel A provides he daily realized volailiy, measured a five-minue inervals, for each ETF. Andersen, Bollerslev, Diebold, and Labys (2001) and Andersen, Bollerslev, Diebold, and Ebens (2001) improve daily volailiy esimaes by using high-frequency, inraday reurns o consruc daily realized volailiy. The auhors define realized volailiy as he square roo of he sum of inraday squared reurns, and show ha his is a consisen esimaor of acual, bu unobservable, volailiy. Based on his esimae, all six Asian ETFs have higher volailiy (1.742 average) han he U.S. ETF (0.797). These resuls are comparable if we use he sandard deviaion of he 24-hour reurn (repored in Panel A) or he sandard deviaion of he five-minue reurn (repored in Panel B). 4. Empirical Resuls We explore he source of Asian ETFs reurn and variance by analyzing volailiy raios, reurn and volailiy correlaions, locaion of rade and public informaion impac on reurns, and Granger causaliy in reurns and volailiies. 4.1 Volailiy Raios We consruc volailiy raios for each ETF and compare dayime o overnigh reurn volailiy using daily daa. Panel A of Table 2 presens he volailiy raios for each marke wih (VR1) and wihou (VR2) he local holiday effecs. VR1 shows ha all Asian volailiy raios are less han one, and range from (Malaysia and Singapore) o (Japan). Volailiy raios less han one indicae higher overnigh volailiy han dayime volailiy. By applying he Barle es for homogeneiy of variances, we conclude ha he differences beween he Asian dayime and overnigh variances are saisically significan a he 1% level for Hong Kong, Japan, - 9 -

11 Taiwan, and Korea. The higher overnigh volailiy for Asian ETFs is consisen wih he release of public informaion during he rading hours of each of he local Asian markes. The Asian variance raios conras wih he U.S. raio which has a value of 3.339, indicaing higher dayime han overnigh variance. The higher dayime volailiy is consisen wih he release of public informaion during rading hours in he U.S. marke. To furher srenghen hese resuls, we compare dayime and overnigh volailiies excluding holidays for each respecive Asian marke. When Asian holidays are excluded, we expec he observed difference beween dayime and overnigh volailiy o increase which would reduce he values of he observed variance raios. The resuls are in line wih our hypohesis. When we exclude local Asian holidays, variance raios drop across he board in all Asian markes, indicaing a greaer volailiy difference beween dayime and overnigh reurns. I should be noed, however, ha he difference beween he volailiy raios when Asian holidays are included and excluded is economically insignifican. [ Inser Table 2 Here ] A an index level, i is unlikely ha privae informaion would be he driving force, according o Chordia e al. (2002). Thus, he resuls suppor he noion ha he observed volailiy difference in reurns is driven by public informaion released in each local Asian marke. 4.2 Correlaion Analysis Mos sudies ha analyze he relaionship beween world markes aribue high levels of correlaion o he locaion of rade and o world marke inegraion. Bodurha e al. (1995), Froo

12 and Dabora (1999), Chan e al. (2003) deermine ha prices can be influenced by locaion of rade. Bosner-Neal, Brauer, Neal, and Whealey (1990), Paro (2001), Olienyk, Schwebach and Zumwal (1999), and Pennahur, Delcoure, and Anderson (2002), find ha he more world markes are inegraed, he higher he correlaion beween U.S. and foreign invesmens, which ranslaes ino less diversificaion benefis from foreign invesmens. We analyze daily and inraday correlaion beween he Asian and he U.S. S&P 500 ETF reurns and volailiies. As shown in Panel B of Table 2, Asian funds have a daily reurn correlaion wih he S&P 500 fund of a leas 36%, ranging up o a maximum of 68%. For he period of our analysis, he Asian fund wih he highes daily reurn correlaion wih he U.S. marke is Japan, followed by Hong Kong, Taiwan, Korea, Singapore, and Malaysia, wih correlaion coefficiens of 0.688, 0.586, 0.577, 0.573, 0.480, and respecively. In erms of realized volailiy (Panel C), he highes correlaion wih he U.S. is found in Japan (0.709), Hong Kong (0.696), and Taiwan (0.596), and he Asian marke wih he lowes realized volailiy correlaion wih he U.S. is Malaysia (0.266). The high correlaion values sugges ha Asian ETFs have limied diversificaion benefis. 4.3 Asymmeric Comovemen We examine he asymmery in Asian ETF comovemen wih he direcion of he U.S. marke reurns in he following marke index model: + + r Asia, = α Asia + β AsiarUS, + β AsiarUS, + ε Asia, (4) where r Asia, and US r, are he close-o-close log daily reurns for each respecive Asian ETF and he S&P 500 ETF respecively. The + and superscrips denoe U.S. up and down markes, respecively. Equaion 4 disinguishes posiive from negaive U.S. reurns and allows for a

13 differen coefficien agains each in order o idenify any asymmery in comovemen. See, for example, Lo (2001). 6 [ Inser Table 3 Here ] For each Asian ETF, Table 3 shows ha boh + β and β are each individually significan, and are no saisically differen from one anoher. For example, for he Hong Kong + ETF (EWH), β = ( = 10.99) and β = ( = 13.42), and he null hypohesis ha β + = β is no rejeced wih = Hence, he Asian ETFs correlae wih he U.S. marke, bu here is no asymmeric comovemen beween hem. 4.4 Locaion of Trade vs. Release of Public Informaion To furher sudy he source of Asian ishare reurns, we use a simple regression analysis of conemporaneous variables. We regress each Asian ETF s dayime reurn agains he S&P 500 ishare dayime reurn and he local marke s overnigh reurn, represened by he local index s neares fuures conrac. r Asia = 0 + β1rus, + β 2rIndex, + β 3Holiday Asian,, α + ε (5) where r Asia, is he dayime reurn for each Asian ishare, US r, is he dayime reurn for he S&P 500 ishare, r Index, is he overnigh reurn for each Asian marke s mos represenaive marke index fuures conrac, Holiday Asian, is a dummy variable equal o one when here is a holiday in ha paricular Asian marke and zero oherwise, and ε is he error erm. 6 Based on his index model, Lo (2001) finds ha emerging markes have an up-marke bea of 0.16 bu a down-marke bea of 1.49, indicaing an asymmeric correlaion wih he U.S. marke

14 Table 4 shows ha for Hong Kong, Japan, Singapore, Taiwan, and Korea, U.S. marke reurns explain a greaer porion of Asian ETF reurns han do he reurns from he local marke, as measured by he β 1 and β 2 coefficiens, respecively. For example, in he case of Hong Kong, β = ( = 10.68) and β 2 = ( = 7.83). In he case of Malaysia, however, he above does no hold. For Malaysia, he local marke explains more of he ETF reurns, as measured by β = ( = 4.59) and β = ( = 7.31). [ Inser Table 4 Here ] The significan conribuion of U.S. marke reurns o inernaional invesmen reurns is consisen wih he imporance of locaion of rade and world marke inegraion in explaining foreign invesmen reurns. Moreover, he imporan conribuion of a local marke reurns o he corresponding Asian ETF indicaes ha public informaion released in each local marke also plays an imporan role in explaining hese ETFs reurns. I is worh noing ha hese resuls are qualiaively he same afer conrolling for he foreign exchange rae beween he U.S. and he local Asian marke. Furhermore, holidays do no have a significan effec on reurns for any Asian counry. 4.5 Granger Causaliy in Reurns and Volailiies In Table 5, we analyze he inraday lead-lag relaionship in reurns and volailiies beween he U.S. marke and each Asian marke, measured a five-minue inervals. For he six Asian ishares, as well as for he U.S. ishare, we run he following auoregressive reurn models: r Asia, = a Asia + basia DOpen + c Asia DClose + wasia, jrus, j + x Asia, jrasia, j + ε Asia, j= 1 j= 1 (6a)

15 24 24 US, = aus + bus DOpen + cus DClose + wus, jrus, j + xus, jrasia, j + ε US, j= 1 j= 1 r (6b) where r Asia, is he five-minue reurn for each Asian ishare a ime, US r, is he five-minue reurn for he S&P 500 ishare, DOpen is a dummy variable equal o one during he firs 30 minues of rading and zero oherwise, DClose is a dummy variable equal o one during he las 30 minues of rading and zero oherwise, ε Asia, and ε US, are boh error erms. The 24 lagged reurns are used as regressors o esimae any shor-erm movemen in condiional expeced reurns. The regression errors are correced for heeroskedasiciy and auocorrelaion by he Newey-Wes mehod. Analogous o he Jones, Kaul, and Lipson (1994b) approach, we hen define volailiy as he absolue value of he residuals aken from equaions 6a and 6b Asia, = α Asia + β Asia + γ Asia DClose + y Asia, j ε US j + z Asia, j ε Asia, j j= 1 j= 1 ε DOpen + η Asia, (7a) US, = αus + βus + γ US DClose + yus, j ε US j + zus, j ε Asia, j j= 1 j= 1 ε DOpen + ηus, (7b) where ε and ε are he absolue value residual from equaions 6a and 6b, respecively, Asia, US, DOpen and DClose are defined idenically as hey were in he reurn models above, coefficiens coefficiens ishare, and y Asia, j and US j z Asia, j and US j η Asia, and US, y, measure he persisence in he volailiy of he U.S. ishare, z, measure he persisence in he volailiy of each respecive Asian η are boh error erms. Adding day-of-week dummy variables o equaions (6) and (7) yield comparable resuls. [ Inser Table 5 Here ]

16 Panel A of Table 5 shows he resuls of Granger causaliy in reurns beween each Asian marke and he U.S., using five-minue inervals. U.S. ETF reurns cause reurns for all Asian markes a any convenional significance level. In conras, only Japan causes U.S. reurns a he 5% level. These resuls are consisen wih hose observed in Table 4, highlighing he imporance of he U.S. marke, in which hese ETFs rade. Panel B presens he resuls of Granger causaliy in volailiy. We find significan bidirecional causaliy beween he U.S. and all Asian markes, wih a much sronger volailiy spillover from he U.S. o he Asian ETFs. To address wheher hese resuls are sensiive o he chosen five-minue inerval, we replicae he resuls wih 10-minue inerval inraday daa. As presened in Table 6, he resuls do no change qualiaively. [ Inser Table 6 Here ] 5. Summary of Findings By aking advanage of he rading schedule difference beween he U.S. and Asian markes, coupled wih he noion ha privae informaion is no likely o be of any significance a an index level, we are able o disinguish beween differen reurn and volailiy sources for Asian ishares. We observe higher overnigh han dayime volailiy, which we accredi o he release of public informaion in each local marke. Asian ETF reurns are explained by boh U.S. reurns (locaion of rade) and local Asian marke reurns. The locaion of rade and invesor senimen effecs are furher suppored by he high reurn correlaion beween Asian and U.S. ETFs. Granger causaliy analysis of inraday reurns shows ha he U.S. causes reurns in all Asian markes. We also find bi-direcional Granger causaliy in volailiy beween he U.S. and

17 he six Asian markes analyzed, while he volailiy spillover is much sronger from he U.S. o he Asian ETFs. Overall, local marke informaion and reurns play an imporan role in explaining Asian ETF volailiy and reurns. Noneheless, reurns and volailiies are heavily influenced by he U.S. marke where hey rade

18 6. References Amihud, Y., & Mendelson, H., (1987). Trading mechanisms and sock reurns: An empirical invesigaion. Journal of Finance 42, Andersen, T.G., Bollerslev, T., Diebold, F.X., & Ebens, H., (2001). The disribuion of realized sock reurn volailiy. Journal of Financial Economics 61, Andersen, T.G., Bollerslev, T., Diebold, & Labys, P., (2001). The disribuion of realized exchange rae volailiy. Journal of he American Saisical Associaion 96, 453, Barclay, M.J., Lizenberger R.H., & Warner, J.B., (1990). Privae informaion, rading volume and sock reurn variances. Review of Financial Sudies 3, Barclay, M.J., & Hendersho, T., (2003). Price discovery and rading afer hours. Review of Financial Sudies 16, Bodurha, J.N., Kim, D.S., & Lee, C.M.C., (1995). Closed-end counry funds and U.S. marke senimen. Review of Financial Sudies 8, Bonser-Neal, C., Brauer, G., Neal, R., & Whealey, S., (1990). Inernaional invesmen resricions and closed-end counry fund prices. Journal of Finance 45, Cha, B., & Oh, S., (2000). The relaionship beween developed equiy markes and he Pacific Basin s emerging equiy markes. Inernaional Review of Economics and Finance 9, Chan, K., & Chan, Y.C., (1993). Price volailiy in he Hong Kong sock marke: A es of he informaion and rading noise hypohesis. Pacific-Basin Financial Journal 1, Chan, K., Fong, W.M., Kho, B.C., & Sulz, R.M., (1996). Informaion rading and sock reurns: Lessons from dually-lised securiies. Journal of Banking and Finance 20, Chan, K., Hamed, A., & Lau, S.T., (2003). Wha if rading locaion is differen from business locaion? Evidence from he Jardine Group. Journal of Finance 58, Chang, R.P., Fukuda, T., Rhee, S.G., & Takano, M., (1993). Inerday and inraday reurn behavior of he TOPIX. Pacific-Basin Financial Journal 1, Chordia, T., Roll, R., & Subrahmanyam, A., (2002). Order imbalance, liquidiy, and marke reurns. Journal of Financial Economics 65, Fleming, J., Kirby, C. & Osdiek, B., (2006). Informaion, rading and volailiy: Evidence from weaher-sensiive markes. Journal of Finance 61, French, K.R., & Roll, R., (1986). Sock reurn variances: The arrival of informaion and he reacion of raders. Journal of Financial Economics 17,

19 Froo, K.A., & Dabora, E.M., (1999). How are sock prices affeced by he locaion of rade? Journal of Financial Economics 53, Fung, J.K.W., Lien, D., Tse, Y., & Tse, Y.K., (2005). Effecs of elecronic rading on he Hang Seng Index fuures marke. Inernaional Review of Economics and Finance 14, Harvey, C.R., & Huang, R.D., (1991). Volailiy in he foreign currency fuures marke. Review of Financial Sudies 4, Hoque, H.A.A.B., Kim, J.H., & Pyun, C.S., (2007). A comparison of variance raio ess of random walk: A case of Asian emerging sock markes. Inernaional Review of Economics and Finance 16, Jones, C.M., Kaul, G. & Lipson, M.L., (1994a). Informaion rading and volailiy. Journal of Financial Economics, Jones, C.M., Kaul, G. & Lipson, M.L., (1994b). Transacions, volume, and volailiy. Review of Financial Sudies 7, 4, Lauricella, T., & Gullapalli, D., (2007). Fas Money Crowds Embrace ETFs, adding risk for individual invesors. The Wall Sree Journal. Lo, A., (2001). Risk managemen for hedge funds: Inroducion and overview. Financial Analyss Journal 57, Oldfield, S., & Rogalski, R., (1980). A heory of common sock reurns over rading and non-rading periods. Journal of Finance 35, Olienyk, J.P., Schwebach, R.G., & Zumwal, J. K., (1999). WEBS, SPDRs, and counry funds: An analysis of inernaional coinegraion. Journal of Mulinaional Financial Managemen 9, Paro, D.K., (2001). Marke segmenaion and inernaional asse prices: Evidence from he lising of world equiy benchmark shares. Journal of Financial Research 24, Pennahur, A.K. Delcoure, N., & Anderson, D., (2002). Diversificaion benefis of ishares and closedend funds. Journal of Financial Research 25, Soll, H., & Whaley, R., (1990). Sock marke srucure and volailiy. Review of Financial Sudies 3, Tse, Y., & Marinez, V., (2007). Price discovery and informaional efficiency of inernaional ishares funds. Global Finance Journal 18, Wang, S.S., & Jiang, L., (2004). Locaion of rade, ownership resricions, and marke illiquidiy: Examining Chinese A- and H- shares. Journal of Banking and Finance 28,

20 Table 1 Descripive Saisics Dayime reurns are esimaed as he log difference beween he U.S. marke's closing and opening price for each ETF. Dayime reurns = log(cl ) - log(op ). Overnigh reurns are esimaed as he log difference beween he U.S. marke's opening price and he previous day's closing price for each ETF. Overnigh reurns = log(op ) - log(cl -1 ). 24-hour reurns are esimaed as he log difference beween he U.S. marke's closing price and he previous day's closing price for each ETF. 24-hour reurns = log(cl ) - log(cl -1 ). Daily volume, reurns, sandard deviaion of reurns, and realized volailiy are obained using daa from January 2002 o December Realized volailiy is obained by aking he square roo of he sum of he squared inra-day reurns sampled a five-minue inervals. Panel A: Daily Avg Reurn (%) Sd Dev of Reurn (%) Volume Realized Dayime Overnigh 24-hour Dayime Overnigh 24-hour ($m) Volailiy Hong Kong Japan Malaysia Singapore Taiwan Korea U.S Panel B: five-minue Inerval Avg Sd Dev Reurn(%) Reurn(%) Hong Kong Japan Malaysia Singapore Taiwan Korea U.S

21 Table 2 Variance Raios and Correlaions Panel A shows he variance raios esimaed as he raio of dayime reurn variance divided by overnigh reurn variance. VR1 is he variance raio of dayime o overnigh reurn variance of each Asian and U.S. ishares. VR2 is he variance raio of dayime o overnigh reurn variance of each ishares excluding holidays in each respecive Asian marke. The es saisic (Barle's Tes of Homogeneous Variances) is disribued as χ 2 (1). Panels B and C show reurn and volailiy correlaions respecively, beween he local Asian marke ETF and he U.S. S&P 500 ETF. Dayime reurns are esimaed as he log difference beween he closing and opening price on day. Dayime reurns = log(cl ) - log(op ). Overnigh reurns are calculaed as he log difference beween he opening price on day and he closing price on day -1. Overnigh reurns = log(op ) - log(cl -1 ). Inraday reurn and volailiy correlaions are esimaed using five-minue inraday inerval reurns. The sample period is January 2002 hrough December Panel A: Variance Raios VR1 Dayime Overnigh χ 2 VR2 Dayime Overnigh χ 2 Hong Kong ** ** Japan ** ** Malaysia ** Singapore * Taiwan ** ** Korea ** ** U.S ** * and ** Saisical significance a 5% and 1% levels respecively Panel B: Reurn Correlaions Dayime Overnigh CL-o-CL Five-Min Hong Kong Japan Malaysia Singapore Taiwan Korea all saisically significan a he 0.01% level Panel C: Volailiy Correlaions Absolue Absolue Realized CL-o-CL Five-Min Volailiy Hong Kong Japan Malaysia Singapore Taiwan Korea all saisically significan a he 0.01% level

22 Table 3 Asymmeric Comovemen The able shows he resuls from he regression: r Asian, = α Asia + β + Asia r + US, + β - Asia r - US, + ε Asia,, where r Asian, is he log close-o-close reurn for each Asian ishare, r + US, is posiive close-o-close reurn for he S&P500 ishare, r - US, is negaive close-o-close reurn for he S&P500 ishare, and ε is he error erm. T-saisics are presened in parenhesis below he coefficiens. Regression errors are correced for heeroskedasiciy and auocorrelaion by he Newey-Wes mehod. The sample period is January 2002 hrough December Hong Kong Japan Malaysia Singapore Taiwan Korea α (0.92) (0.41) (1.73) (1.14) (1.37) (1.86) β ** ** ** ** ** ** (10.99) (12.39) (6.98) (9.28) (9.66) (12.23) β ** ** ** ** ** ** (13.42) (12.42) (6.17) (9.83) (14.09) (13.43) R 2 Adj H 0 : β + = β - (-0.03) (0.18) (-0.60) (-0.14) (-1.38) (-0.77) * and ** Saisical significance a 5% and 1% levels respecively

23 Table 4 Locaion of Trade vs. Release of Public Informaion The able shows he resuls from he regression: r Asian, = α 0 + β 1 r US, + β 2 r Index, + β 3 Holiday Asian, + ε, where r Asian, is he dayime reurn for each Asian ishare, r US, is he dayime reurn for he S&P500 ishare, r Index, is he overnigh reurn for each Asian marke's mos represenaive marke index fuures conrac, Holiday Asian, is a dummy variable equal o one when here is a holiday in ha paricular Asian marke and zero oherwise, and ε is he error erm. T-saisics are presened in parenhesis below he coefficiens. Regression errors are correced for heeroskedasiciy and auocorrelaion by he Newey-Wes mehod. The sample period is January 2002 hrough December Hong Kong Japan Malaysia Singapore Taiwan Korea α * ** ** (-2.04) (1.53) (-1.39) (-1.33) (-5.11) (-3.54) β ** ** ** ** ** ** (10.68) (17.78) (4.59) (9.40) (9.67) (11.39) β ** ** ** ** * ** (7.83) (8.75) (7.31) (2.64) (2.36) (6.93) β (-0.51) (-1.07) (-1.19) (-1.26) (-0.11) (-1.02) R 2 Adj * and ** Saisical significance a 5% and 1% levels respecively

24 Table 5 Granger Causaliy Beween U.S. and Asian Markes a five-minue Inervals The able shows Wald coefficien ess for Granger causaliy beween he U.S. ETF and each Asian ETF, from January 2002 hrough December Panel A shows causaliy in reurns and Panel B shows causaliy in volailiies. Chi-squared p-values are presened in parenheses below he coefficiens. P-value coefficiens for 12 and 24 lag Q-saisics on residual auocorrelaion are presened a he foo of each panel. Resuls are generaed using wo-equaion VAR sysems wih 24 lags for reurn models and 24 lags for volailiy models, sampled a five-minue inervals. Regression errors are correced for heeroskedasiciy and auocorrelaion by he Newey-Wes Mehod. Panel A: Causaliy in Reurns Hong Kong Japan Malaysia Singapore Taiwan Korea U.S. Granger-causes Asia ** 1,383.8 ** ** ** ** ** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Q(12) Q(24) Asia Granger-causes U.S * (0.25) (0.02) (0.09) (0.44) (0.06) (0.31) Q(12) Q(24) Panel B: Causaliy in Volailiy Hong Kong Japan Malaysia Singapore Taiwan Korea U.S. Granger-causes Asia ** ** ** ** ** ** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Q(12) Q(24) Asia Granger-causes U.S ** ** 71.8 ** 49.6 ** 99.6 ** ** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Q(12) Q(24) * and ** Saisical significance a 5% and 1% levels respecively

25 Table 6 Granger Causaliy Beween U.S. and Asian Markes a 10-minue Inervals The able shows Wald coefficien ess for Granger causaliy beween he U.S. ETF and each Asian ETF, from January 2002 hrough December Panel A shows causaliy in reurns and Panel B shows causaliy in volailiies. Chi-squared p-values are presened in parenheses below he coefficiens. P-value coefficiens for 12 and 24 lag Q-saisics on residual auocorrelaion are presened a he foo of each panel. Resuls are generaed using wo-equaion VAR sysems wih 24 lags for reurn models and 24 lags for volailiy models, sampled a 10-minue inervals. Regression errors are correced for heeroskedasiciy and auocorrelaion by he Newey-Wes Mehod. Panel A: Causaliy in Reurns Hong Kong Japan Malaysia Singapore Taiwan Korea U.S. Granger-causes Asia ** ** ** ** ** ** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Q(12) Q(24) Asia Granger-causes U.S * 22.5 (0.76) (0.12) (0.15) (0.64) (0.01) (0.55) Q(12) Q(24) Panel B: Causaliy in Volailiy Hong Kong Japan Malaysia Singapore Taiwan Korea U.S. Granger-causes Asia ** ** ** ** ** ** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Q(12) Q(24) Asia Granger-causes U.S ** ** ** 60.2 ** 87.6 ** ** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Q(12) Q(24) * and ** Saisical significance a 5% and 1% levels respecively