Price, Volume and Volatility Spillovers among New York, Tokyo and London Stock Markets

Transcription

1 INTERNATIONAL JOURNAL OF BUSINESS, 4(), 999 ISSN: Price, Volume and Volailiy Spillovers among New York, Tokyo and London Sock Markes Sangphill Kim and Meng Rui The dynamic relaionship among he U.S., Japan and U.K. daily sock marke reurn volailiy and rading volume is examined using a mulivariae generalized auoregressive condiional heeroskedasic (GARCH) model. Significan reurn spillovers from New York and Tokyo o London, and from New York, London o Tokyo and from Tokyo o New York has been found. Volailiy spillover seems o be far more exensive and reciprocal. A comparison of he resuls from he before and afer Ocober 987 crash period reveals ha naional markes have grown more inerdependen in he sense ha informaion affecing asse prices has become more global in naure. I. INTRODUCTION Finance heory predics ha here are poenial gains from inernaional porfolio diversificaion if reurns from invesmens in various naional sock markes are no perfecly correlaed and he correlaion srucure is sable. I appears ha previous empirical sudies of he inerrelaionship of he major world sock price indexes have no provided consisen resuls. The sizes and signs of correlaion coefficiens varied depending on he choice of markes, he sample period chosen, he frequency of observaions (daily, weekly, or monhly), and he differen mehodologies employed o esimae he marix of correlaion coefficiens. Jaffe and Weserfield [6] sugges ha he reurn correlaion among differen markes is posiive and significan. Eun and Shim [0], who use vecor auoregressions, find subsanial cross-counry ineracions and also record an influenial role for he U.S. marke. King and Walhwani [9], in a significan sudy of he period surrounding he 987 crash, documen a conagion effecs where a misake in one marke is ransmied o oher markes. Hamao, Masulis, and Ng [] use an ARCH model o examine he linkage among he New York, Tokyo and London Sock markes. They find evidence of price volailiy spillovers from New York o Tokyo, London o Tokyo, and New York o London bu find no evidence of spillover effecs in Sangphill Kim, Deparmen of Managemen, Universiy of Massachuses, Lowell, MA Meng Rui, Deparmen of Accounancy, The Hong Kong Polyechnic Universiy, Hong Kong. Copyrigh 999 by SMC Premier Holdings, Inc. All righs of reproducion in any form reserved.

2 4 Kim and Rui oher direcions for he period prior o he 987 crash. Hamao, Masulis, and Ng [], exend heir previous sudy, and find volailiy spillover effecs of disproporionae size from one marke o he nex and hese paerns have been relaively sable boh prior o and afer he crash. They find, however, some weak evidence of increasing volailiy spillovers from he Tokyo marke o oher markes, which suppors he growing imporance of he Japanese marke during his period. To sudy he relaionship beween he New York and London sock markes, Susmel and Engle [4] use hourly daa o esimae a modified ARCH model. While hey find no evidence of mean spillovers if no overlapping periods are included, hey do noe some weak evidence of volailiy spillovers. Lin, Engle, and Io [], using a signal exracion model wih GARCH processes, find ha Tokyo (New York) dayime reurns are, in general, significanly correlaed wih New York (Tokyo) overnigh reurns. They also observe ha excep for a lagged reurn spillover from New York o Tokyo for he period afer he crash, here are no significan lagged spillovers in reurns or volailiy. As evidenced by he sudies referenced above since he sock marke crash of Ocober 987, here has been subsanial ineres in research on why sock reurns and volailiy are inerdependen across world markes. Two possible inerpreaions for such inerdependence of sock reurns and volailiy are informaional link hypohesis and conagion hypohesis. According o he informaional link hypohesis, news revealed in one counry is perceived as informaive o fundamenals of sock prices in anoher. The inerdependence can be aribued o real and financial linkage of economies. Marke conagion hypohesis suggess ha sock prices in one counry be affeced by changes in anoher counry beyond wha is conceivable by connecions hrough economic fundamenals. According o his view, overreacion, speculaion, and/or noise rading are ransmissible across borders. The liquidiy needs of uninformed raders in New York rigger selling pressure on NYSE, resuling in high volume. Subsequenly, uninformed raders in Tokyo, observing a drop in New York sock prices, may increase selling pressure on he Tokyo sock exchange. In his scenario, correlaion in inernaional sock reurns is caused by he conagious behavior of liquidiy raders across borders. As in King and Wadhwanis aricle [9], rading volume can be a proxy for a ime-varying conagious coefficien. Recenly, some heoreical sudies explicily invesigae he dynamic relaionship among rading volume, sock reurns and volailiy. Blume, Easley, and O Hara [] presen a model in which raders can learn valuable informaion abou a securiy by observing boh pas price and pas volume informaion. In heir model, volume provides daa on he qualiy or precision of informaion

3 INTERNATIONAL JOURNAL OF BUSINESS, 4(), abou pas price movemens, and hus raders who include volume measures in heir echnical analysis may be expeced o perform beer in he marke han hose who do no. Wang [7] analyzes dynamic relaionships beween volume and reurns based on a model wih informaion asymmery. His model shows ha volume may provide informaion abou expeced fuure reurns. For his paper, he focus is on rading volume because auhors beliefs in his paper reflec he view ha rading volume is a good proxy for he degree of heerogeneiy in invesors opinions and beliefs. Mos sudies repored a posiive relaionship beween volailiy and rading volume in he domesic sock marke. According o he mixure-of-disribuion hypohesis (see for example Clark [4] and Tauchen and Pis [5], his posiive relaionship is ofen aribued o he rae of informaion, which drives boh volailiy and volume. Lamoraux and Lasrapes [] use his framework o es wheher here are any GARCH effecs remaining afer he condiional volailiy specificaion expands o include he conemporaneous rading volume, a proxy for informaion arrival. They find ha for individual socks, volailiy persisence falls significanly once conemporaneous rading volume is included. The principal objecive of his sudy is o disenangle he wo possible inerpreaions by examining he effec of rading volume on iner-marke dependence on sock reurns. Firs, if correlaion beween inernaional sock reurns is caused by inernaional conagion of liquidiy raders senimens or by resoluion of heerogeneous inerpreaions of foreign news, such correlaion will be posiively influenced by foreign rading volume. Second, if inernaional reurn inerdependence is associaed wih he informaion conaining in sock price changes in one marke o anoher marke, hese inerdependence are likely o be posiively influenced by foreign price volailiy bu no by foreign rading volume. The use of rading volume allows for he assessmen of he wo possible channels of inernaional ransmission of inernaional sock reurn and volailiy by examining he causal relaionship among he correlaion of inernaional sock reurns, rading volume, and volailiy. In his paper, he dynamic relaionship among he U.S., Japan and U.K. daily sock marke reurn volailiy and rading volume is examined using a mulivariae generalized auoregressive condiional heeroskedasic (GARCH) model. The vulivariae GARCH model uses informaion from he hisory of more han one marke. According o Conrad, Gulekin, and Kaul [5], mulivariae models provide more precise esimaes of he parameers because hey uilize informaion in enire variance-covariance marix of he errors. The remainder of he paper is organized as follows: Secion discusses daa. Mehodology is presened in Secion 3. Secion 4 repors he empirical resuls and Secion 5 concludes he paper.

4 44 Kim and Rui II. DATA The daa se comprises daily marke price index and rading volume series for he hree larges sock exchanges: New York, Tokyo and London. For he New York Sock Exchange, we use he NYSE composie index. The daa cover he period of January, December 30, 995, and consis of 6,568 observaions for each series. For he Tokyo Sock Exchange, we use he Nikkei 5 Index, which is aken from he PACAP daabase of he Universiy of Rhode Island, is using. The TSE daa cover he period of January 4, 975, o December 30, 995, and consiss of 5,696 observaions. For London, we use he FT-SE 00 index. The index covers he period of January 4, 984, December 30, 995, and consiss of 3,03 observaions for each variable. The NYSE composie index includes all common socks raded on he NYSE and is weighed by is marke value. The index is a measure of he changes in he aggregae marke value of NYSE common socks, adjused o eliminae he effec of capializaion changes, new lisings and delisings. The Nikkei 5 Index is a share-price weighed index like he Dow Jones average in he U.S. (as simple averages of he componen socks prices adjused by a divisor o accoun for non-marke facors, righs, and changes o he consiuen issues). Firs published in 950, he Index comprises 5 of he larges capialized socks on he Firs Secion of he Tokyo Sock Exchange and represens roughly 65 percen of he oal marke capializaion for he Tokyo Sock Exchange. 3 The FT-SE 00 index, was sared on January 3, 984, by he Sock Exchange (SE) and Financial Times (FT), incorporaing he op 00 U.K. companies, accouning for abou 70 percen of he oal marke value of all U.K. equiies. I shows a very close hisorical correlaion wih mos broad index of he marke. FT-SE 00 is calculaed as a weighed-average index. Base value is 000 a he opening of business on January 3, 984. The FT-SE00 daa is obained from he Daasream daabase. In recen years here has been a proliferaion of empirical sudies documening anomalous seasonal regulariies in securiy reurns. These include calendar effecs relaed o he ime of day [Harris [3]]; he day of he week [French [], Jeffe and Weserfield [5], Keim and Sambaugh [8]]; he urn of monh [Ariel []]; and he urn of he year [Lakonishok and Smid [0]]. These paerns appear o conflic wih he heoreical noions of efficiency and raional expecaions in he marke for securiies. We employ a hree-sep procedures developed by Gallan, Rossi, and Tauchen [3] o adjus for seasonal regulariies. Then, we use hese adjused daa o es for he robusness of he dynamic relaions. In sep one, he original sock reurn series on dummy variables for day of he week (one for each day from Tuesday hrough Friday), dummy variables for pre-holiday, dummy

5 INTERNATIONAL JOURNAL OF BUSINESS, 4(), variables for urn-of-he year, and dummy variables for urn of he monh. This is he locaion regression ha adjuss for documened shifs in mean due o seasonal endencies. If y is he series o be adjused and x conains he adjusmen regressors, he locaion regression is y = x 'β + u. () Denoing he residuals from he locaion regression by u, he second sep involves performing he variance regression by regressing log(u ) on he same se of adjusmen variables used in he locaion regression: log(u ) = X γ + ε. () This regression makes adjusmens for seasonal regulariies in he variance. Denoing he prediced value from he variance equaion by ˆθ, he final adjusmen we make o ge he adjused series is he linear ransformaion: y a = a + b[ µ / exp(ˆ θ / )], (3) where a and b are chosen in such a way ha has he same mean and variance as y (he log of he original series). This ransformaion makes he unis of measuremen of adjused and unadjused daa he same, making i easier o inerpre he resuls. The NYSE opens is rading a 9:30 a.m. and coninues rading unil 4:00 p.m.; London operaes from 9:00 a.m. o 5:00 p.m. Trading on he TSE is divided ino a morning session from 9:00 o :00 a.m.; followed by an afernoon session from :00 o 3:00 p.m. On April 9, 99, he sar of he afernoon session was moved forward 30 minues o :30 p.m. This rading srucure is unique o he TSE. There is no overlap beween he operaing hours of he Tokyo Exchange and London or New York exchanges, bu here is a wo-and-one-half hours overlap beween he London and New York markes. The following figure illusraes he relaionship of business hours among hese hree markes: London Time 0:0 4:00 04:00 08:00 :00 6:00 0:00 Tokyo ime 06:0 0:00 4:00 8:00 :00 0:00 06:00 New York Time 6:0 0:00 4:00 04:00 08:00 :00 6:00 London Marke a y

6 46 Kim and Rui Tokyo Marke New York Marke 3 where,, and 3 denoe London sock marke business hours, Tokyo sock marke business hours, and New York sock marke business hours, respecively. The London and New York overlap of hours consiss of he las wo-and one-half hours of he LSE s operaing hours and he firs wo-and onehalf hours of he NYSE s operaing hours. III. METHODOLOGY I has long been recognized ha he volailiy of sock prices is ime-varying and clusered. GARCH models are capable of capuring he hree mos empirical feaures observed in sock reurn daa: lepokurosis, skewness, and volailiy clusering. To examine he cross-marke dependence on reurn, rading volume, and volailiy, we exend he specificaion of he GARCH process o accoun for possible variaions in he effec of volailiy spillover across markes This mulivariae seing akes ino accoun he cross-secional correlaions in residual as well as he condiional heeroskedasiciy; i.e., he mulivariae GARCH model uses informaion from he hisory of more han one marke. Hence, if he sysem of equaions is esimaed simulaneously, hen full efficiency can be aained. According o Conrad, Gulekin, and Kaul [5], mulivariae models provide a greaer precision in he esimaes of he parameers because such models uilize informaion in he enire variancecovariance marix of he errors. Furher, he generaed regressor problem associaed wih univariae models is avoided in mulivariae models because i esimaes all parameers joinly. Modeling he reurns of he hree markes simulaneously has several advanages over he univariae approach ha has been used so far. Firs of all, such modeling eliminaes he wo-sep procedure found wih he univariae approach, hereby avoiding problems associaed wih esimaed regressors. Second, i improves he efficiency and he power of he ess for cross-marke spillovers. Third, i is mehodologically consisen wih he noion ha spillovers are essenially manifesaions of he impac of global news on any given marke. The following mulivariae GARCH model is posied for he join processes governing he daily raes of reurn for he Japan, U.S. and U.K. markes:

7 INTERNATIONAL JOURNAL OF BUSINESS, 4(), r a + Φ r + e (4) = p p e Ω ~ N(0, H ) (5) or vech(h ) = a + bvech( ε ε ') + cvech(h ) (6) H = Γ' Γ + F H K L ' k l kfk + k= l= ' l G e l e ' l G l (7) where he reurns vecor is denoed by r. The residual vecor is given by, wih is corresponding condiional covariance marix { H } = h. e is represened by a column vecor of forecas errors of he bes linear predicor of r condiional on pas informaion, denoed by Ω, and including he P lagged values of r and rading volume. Where vech(.) denoes he column-sack operaor of he lower porion of a sysmmeric maix, ε is an n vecor of innovaion, a is (/N(N + ) ) parameer vecor, and b and c are (/N(N + ) (/N(N +) marices of consan parameers. The mulivariae GARCH(,) specificaion has (/N (N + ) + (/N(N +)) parameers in he condiional variance and covariances. Thus, for racabiliy, reasonable resricions on he parameers are necessary. According o Engle and Kroner [8], various resricions may be imposed in his paramerizion o make esimaion easier. A more parsimouioun represenaion can be obained by imposing a diagonal resricion on he mulivariae GARCH parameers marices so ha each variance and covariance elemen depends only on is own pas values and predicion errors (Bollerslev, Engle, and Wooldridge [3]). The mos generally used mulivariae GARCH is he so-called diagonal-vec model of order p,q, where he condiional covariance marix is given by p T H = A + A ( ε ε ) + B H (8) i i i i= i= where he symbol sands for he Hadamard produc, ha is, elemen-byelemen muliplicaion. All quaniies in he above equaion are d d marices, excep for ε, which is a d column vecor. The marices A, A i and B i of a q i i ij ij, ' e

8 48 Kim and Rui diagonal-vec model are resriced o being symmeric o ensure ha he condiional covariance marix H is symmeric. In he bivariae case, he diagonal model is simply as follows: The mean equaion marix is x y c = c d + h e i f j x g x k y y ε + ε (9) where x = reurns on sock exchange one, x = rading volumes on sock exchange one, y = reurns on sock exchange wo and y = rading volumes on sock exchange wo. The condiional covariance marix is h h h,,, c = c c a a ε, 0 ε, ε a 33 ε,, b b h 0 h b 33 h,,, (0) In oher words, his presenaion is obained by assuming ha marices are diagonal. The diagonal-vec model inroduced by Bollerslev, Engle and Wooldridge [3] appears o provide good model fis in a number of applicaions. However, oher models may be highly desirable in specific applicaion for he following wo reasons: () he diagonal-vec model does no ensure posiive definieness of he condiional covariance marices V, and () he diagonal-vec model requires a large number of parameers, and whereas a more parsimonious model migh be desired. The BEKK model, inroduced by Engle and Kroner [8], has he following condiional covariance marix srucure: V T T T ε )A + = AA + A ( ε B V B () T Because of he presence of a paired ransposed marix facor for each of he d d marices A,A,B, symmery and non-negaive-definieness of he condiional covariance marix V is assured. Noe ha A, A,B need no be symmeric. In he bivariae case, he BEKK model is as follows:

9 INTERNATIONAL JOURNAL OF BUSINESS, 4(), The mean equaion marix is x y c = c d + h e i f j x g x k y y ε + ε () where x = reurns on sock exchange one, x = rading volumes on sock exchange one, y = reurns on sock exchange wo and y = rading volumes on sock exchange wo. The condiional covariance marix is a a ε, ε, ε, a a + b b b b H = C 0'C0 + H (3) a a ε, ε, ε, a a b b b b Given a sample of T observaions of he reurns vecor, r, he parameers of he mulivariae sysems are esimaed by compuing he condiional log-likelihood funcion for each ime period as L ( Θ) = log π log H e ' ( Θ)H ( Θ)e ( Θ) (5) T L ( Θ) = L ( Θ (6) = ) where Θ is he vecor of all parameers. Numerical maximizaion of he loglikelihood funcion yields he maximum likelihood esimaes and associaed asympoic sandard errors. IV. EMPIRICAL EVIDENCES Table repors saisics for he reurns and rading volumes of he hree sock markes as well as saisics esing for normaliy, auocorrelaion and saionariy. The measures for skewness in he hree sock markes show ha all reurn series are negaively skewed and ha all rading volumes are posiively skewed wih respec o he normal disribuion. The measures for excess kurosis show ha all reurn and rading volume series are highly lepokuric

10 50 Kim and Rui wih respec o he normal disribuion. The Wald and he Kolmogorov- Smirnov saisics rejec normaliy for each of he reurn and rading volume series of all hree sock markes. The es resuls indicae ha he null hypohesis ha reurn series are nonsaionary series is rejeced in all hree sock exchanges, bu he null hypohesis ha rading volume series are nonsaionary is rejeced in he London and Tokyo sock exchanges, no in he New York sock exchange. The Ljung-Box saisic for up o lags, calculaed for he reurn, he squared reurn and rading volumes series, indicae he presence of significan linear and non-linear dependencies, respecively, in he reurns of all hree markes.

11 INTERNATIONAL JOURNAL OF BUSINESS, 4(), Table Preliminary saisics of reurns on New York, Tokyo and London Sock Exchanges Reurns of New York Reurns of Tokyo Reurns of London Toal Before Afer Crash Toal Before Afer Crash Toal Before Afer Crash Crash Crash Crash Observaions sample mean Sandard Devidaion Skewness Kurosis Wald-saisics Kolmogorov Smirnov LB() LB () Minimum Maximum s qunaile Median rd quanile

12 5 Kim and Rui Table (coninued) Denoes significance a he.05 level a leas. All reurns are expressed in percenages. The es saisic for skewness and excess kurosis is he convenional -saisic. LB(n) and LB (n) is he Ljung-Box saisic for reurns and squared reurns respecively disribued as wih n degrees of freedom. The criical value a he.05 level is.06 for lags. The assumed densiy for he Kolomogorov-Smirnov saisic is he normal; sample criical value a he.05 level is χ The conemporaneous correlaion marix of reurn and rading volume among New York, London and Tokyo sock exchanges is presened on Table. The correlaions of reurns range from a high of beween New York and London, o a low of 0.53 beween New York and Tokyo. The correlaions of rading volume range from a high of beween New York and London, o low of 0.75 beween London and Tokyo. The maximum likelihood esimaes of he mulivariae diagonal GARCH model are repored in Panel of Table 3. The full model considers price, rading volume and volailiy spillovers among he New York, London, and Tokyo sock exchanges. In erms of reurn inerdependencies, here are significan reurn spillovers from New York and Tokyo o London; here are also significan reurn spillovers from New York, London o Tokyo and significan reurn spillovers from Tokyo o New York. In erms of reurn and rading volume inerdependencies, here are significan reurn spillovers from New York o reurns of Tokyo. The BEKK GARCH model finds similar resuls.

13 INTERNATIONAL JOURNAL OF BUSINESS, 4(), Table The conemporaneous correlaion marix of reurns and rading volumes among New York, London and Tokyo Sock Exchanges Reurns of New York Volumes of New York Reurns of Tokyo Volumes of Tokyo Reurns of London Toal sample Reurns of New York Volumes of New York Reurns of Tokyo Volumes of Tokyo Reurns of London Volumes of London Before Crash Reurns of New York Volumes of New York Reurns of Tokyo Volumes of Tokyo Reurns of London Volumes of London Afer Crash Reurns of New York Volumes of New York Reurns of Tokyo Volumes of Tokyo Reurns of London Volumes of London Volumes of London

14 54 Kim and Rui Table 3 Mean, rading volume and volailiy spillovers esimaed from a diagonal mulivariae GARCH model. The model o be esimaed is given by he following equaions. Mean equaion marix: y y c = c d + h e i f j x g x k y y ε + ε where x and y are rading volumes and reurns on domesic sock exchange, respecively; x and y are rading volumes and reurns on foreign sock exchange. Condiional covariance marix: h h h,,, c = c c a a ε, 0 ε, ε a 33 ε,, b b h 0 h b 33 h,,, Toal Before Crash Afer Crash Coefficien T-raio Coefficien T-raio Coefficien T-raio Spillover beween New York (domesic) o Tokyo (foreign) c (3.7994) (.3953) (.3458) d (-0.607) (0.7) (-.367) e 6.40E-6 (0.3797) 5.487E-5 (0.8) (-.070) f (8.0575) (8.8446) (.4757) g (-.053) (-.699) (-.73) c (.9768) (.87) (-0.459) h (6.969) (4.86) (0.7738) i (-.59) (-3.4) (0.8576) j (.6657) 0.54 (0.35) (8.7809) k (.588) (0.9556) (5.079) c (8.6583) (3.963) (3.0) c (.4) (.837) (.807)

15 INTERNATIONAL JOURNAL OF BUSINESS, 4(), Table 3 (coninued) c (9.4938) 0.09 (9.0534) (5.7897) a (7.6467) (9.084) 0.07 (7.304) a (.565) (-0.678) (.86) a 0.43 (3.7656) 0.90 (3.97) (9.7705) b (56.53) (05.883) 0.97 (54.3) b (.30) (-.9747) (6.3047) b (9.394) (47.39) (44.0) Toal Before Crash Afer Crash Coefficien T-raio Coefficien T-raio Coefficien T-raio Spillover beween New York (domesic) o London (foreign) c (.6563) (0.4999) (0.706) d (-.6407) (0.365).4E-6 (0.043) e (.666) (0.000) (0.3795) f (5.0633) (3.7435) (3.5473) g ( ) (0.838) (-.39) c (.467) (.6094) (0.594) h (-.5974) ( ) 9.36E-6 (0.0749) i (0.4573) (0.056) (-0.43) j 0.45 (.895) (5.4536) (0.607) k (-0.74) (-0.376) -0.0 (-0.878) c (8.835) (.8896) (4.7) c (4.0) (0.9955) (.6855) c (5.9504) (.979) (4.7) a (8.964) (3.387) (8.9709) a (8.005) (.5493) (.639) a (0.553) (3.453) (8.388) b (35.050) ( ) (384.36) b (58.468) (8.4873) (47.3) b (63.997) (5.) (53.03)

16 56 Kim and Rui Table 3 (coninued) Toal Before Crash Afer Crash Coefficien T-raio Coefficien T-raio Coefficien T-raio Spillover beween Tokyo (domesic) o London(foreign) c (.7606) (.3) (.0058) d (-.57) (-3.8) -.87E-6 (-0.053) e (0.357) (3.4468) (-0.56) f (7.3699) (6.688) 0.73 (4.350) g 0.88 (6.7378) 0.63 (4.09) (5.0673) c (.65) (.468) (0.34) h (0.5866) (0.547).4E-6 (0.045) i (-0.858) (0.46) (0.87) j ( ) (-.777) (4.9) k (4.9307) (.6787) (4.097) c (7.6756) (5.9746) (5.7) c (.566) (.667) (.098) c (4.7387) (.4987) (3.3708) a (7.53) (7.033) (9.9053) a (4.003) (3.4445) (.649) a (7.008) (3.877) (5.935) b (73.86) (4.59) (47.743) b (9.4088) (-.0566) (9.893) b ( ) (0.040) (35.38)

17 INTERNATIONAL JOURNAL OF BUSINESS, 4(), Table 4 Mean, rading volume, and volailiy spillovers esimaed from a BEKK mulivariae GARCH model. The model o be esimaed is given by he following equaions. Mean equaion marix: y y c = c d + h e i f j x g x k y y ε + ε where x and y are rading volumes and reurns on domesic sock exchange, respecively; x and y are rading volumes and reurns on foreign sock exchange. Condiional covariance marix: H a C 0'C0 + a a ε, ε, ε ε ε a a + b b b H,, = a, ε, a b a b b b b Toal Before Crash Afer Crash Coefficien T-raio Coefficien T-raio Coefficien T-raio Spillover beween New York (domesic) o Tokyo (foreign) c (.880) (.9556) 0.39 (.5954) d 6.78E-6 (0.0783) (.5386) (-.476) e -4.8E-6 (-0.37) -.88E-5 (-.3) -3.50E-5 (-.5) f 0.93 (7.566) (.079) (.5546) g (-.383) (-0.65) (-0.876) c (.6) (3.580) (-0.434) h (-.797) (3.9554) (0.945) i (.0553) ( ) 3.6E-6 (0.059) j (0.64) (7.7377) (6.9995) k (3.38) (.39) 0.68 (5.6978) c (8.504) (.475) (.0073) c (4.5903) (-.599) (-0.894) c (0.730) (0.84) (0.000) a (8.8) (6.969) ( ) a (-5.568) 0.67 (.98) (-0.54) a (-0.698) 0.96 (3.6345) (3.4347) Table 4 (coninued)

18 58 Kim and Rui b (53.46) (6.77) (35.965) b (37.336) (3.54) (9.48) b (-0.800) (-5.74) (.38) b (7.9457) (-3.68) (.9696) B (33.076) (.466) (5.955) Toal Before Crash Afer Crash Coefficien T-raio Coefficien T-raio Coefficien T-raio Spillover beween New York (domesic) o London (foreign) c (.4998) (0.644) (0.5394) d (-.379) (0.379) (0.5) e 6.5E-59 (.307) ( ) (0.438) f (4.36) (.8567) (3.49) g (-.865) (0.960) (-.0767) c (.4890) (0.9454) (0.3494) h (-.090) ( ) 4.69E-6 (0.0377) i.48e-5 (0.693) (0.36) (-0.67) j (8.857) 0.34 (4.546) (0.86) k (-0.499) (-0.0) (-.008) c (0.047) (0.573) (4.0679) c (0.095) (-0.796) (0.49) c ( ) ( ) (9.0468) a (-0.40) (0.7067) (40.98) a (-.508).83 (3.45) (.5746) a (-0.3) (-3.056) (.467) b (-.9337) (-.7) (75.946) b (9.6330) (.69) (5.8004) b (6.9537) (.863) (0.0953) b (0.30) (.3335) (-.7956) B 0.7 (8.399) (.563) (.858) Turning o volailiy spillover, i can seem far more exensive and reciprocal. In addiion o is own pas innovaions, he condiional variance in

19 INTERNATIONAL JOURNAL OF BUSINESS, 4(), each marke is also affeced by innovaions coming from he ohers. Thus, here are significan volailiy spillovers from New York and London o Tokyo, from Tokyo and New York o London and from London and Tokyo o New York. A comparison of he resuls from he before and afer Ocober 987 crash period reveals ha naional markes have grown more inerdependen in he sense ha informaion affecing asse prices has become more global in naure. Before he crash, here is significan rading volume spillover from New York o reurns of Tokyo and from Tokyo o reurns of London. Afer he crash, no reurn and rading volume inerdependencies beween any wo markes was observed. V. CONCLUSION The above empirical resuls are consisen wih he hypohesis ha he cause of inernaional ransmission of sock reurns and volailiy is ransmission of informaion from one sock marke o anoher. Before he crash, he correlaion beween inernaional sock reurns migh be caused by inernaional conagion of liquidiy raders senimens or by resoluion of heerogeneous inerpreaions of foreign news. Wih he developmen of communicaion echnology, he more efficien sock markes become, he less conagion effec among naional sock markes. NOTES. On he TSE, half-day Saurday rading (9:00 o :00 a.m.) has undergone various modificaions over he pas 30 years. These modificaions include he following: () he hird Saurday of each monh was closed on a regular basis beginning January 973; () in Augus 983, he hird Saurday closure was replaced by he second Saurday; (3) beginning Augus 986, boh he second and hird Saurdays were closed; (4) for January 989, all Saurdays excep he las Saurday were closed; and (5) he curren scheme of no Saurday rading became effecive February 989.

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