DECOUPLING AND THE SPILLOVER EFFECTS

WP 13-21 Selios D. Bekiros European Universiy Insiue, Ialy The Rimini Cenre for Economic Analysis (RCEA), Ialy DECOUPLING AND THE SPILLOVER EFFECTS OF THE US FINANCIAL CRISIS: EVIDENCE FROM THE BRIC MARKETS Copyrigh belongs o he auhor. Small secions of he ex, no exceeding hree paragraphs, can be used provided proper acknowledgemen is given. The Rimini Cenre for Economic Analysis (RCEA) was esablished in March 2007. RCEA is a privae, nonprofi organizaion dedicaed o independen research in Applied and Theoreical Economics and relaed fields. RCEA organizes seminars and workshops, sponsors a general ineres journal The Review of Economic Analysis, and organizes a biennial conference: The Rimini Conference in Economics and Finance (RCEF). The RCEA has a Canadian branch: The Rimini Cenre for Economic Analysis in Canada (RCEA- Canada). Scienific work conribued by he RCEA Scholars is published in he RCEA Working Papers and Professional Repor series. The views expressed in his paper are hose of he auhors. No responsibiliy for hem should be aribued o he Rimini Cenre for Economic Analysis. The Rimini Cenre for Economic Analysis Legal address: Via Angherà, 22 Head office: Via Paara, 3-47900 Rimini (RN) Ialy www.rcfea.org - secreary@rcfea.org

DECOUPLING AND THE SPILLOVER EFFECTS OF THE US FINANCIAL CRISIS: EVIDENCE FROM THE BRIC MARKETS Selios D. Bekiros * European Universiy Insiue, Deparmen of Economics, Via della Piazzuola 43, I-50133 Florence, Ialy & Rimini Cenre for Economic Analysis (RCEA), Via Paara, 3, 47900, Rimini, Ialy ABSTRACT Even hough he global conagion effecs of he financial crisis have been well documened, he ransmission mechanism as well as he naure of he volailiy spillovers among he US, EU and he BRIC markes has no been sysemaically invesigaed. To examine he dynamic linear and nonlinear causal linkages a sepwise filering mehodology is inroduced, for which vecor auoregressions and various mulivariae GARCH represenaions are adoped. The sample covers he afer-euro period and includes he financial crisis and he Eurozone deb crisis. The empirical resuls show ha he BRICs have become more inernaionally inegraed afer he US financial crisis and conagion is furher subsaniaed. Moreover, no consisen evidence in suppor of he decoupling view is found. Some nonlinear causal links persis afer filering during he examined period. This indicaes ha nonlinear causaliy can, o a large exen, be explained by simple volailiy effecs, alhough ail dependency and higher-momens may be significan facors of he remaining inerdependences. Keywords: sock markes; nonlinear causaliy; filering; GJR-GARCH; mulivariae GARCH models; spillovers JEL classificaion: C14; C58; G15; C51; C52 * This research was suppored in par by by he Marie Curie Inra European Fellowship (FP7-PEOPLE-2009-IEF, N 251877) under he 7 h European Communiy Framework Programme. I am graeful o faculy members and seminar paricipans a he Deparmen of Economics, European Universiy Insiue (EUI) for helpful commens and discussions. The usual disclaimers apply. Tel.: +39 055 4685916; Fax: +39 055 4685 902; E-mail address: selios.bekiros@eui.eu 1

1. INTRODUCTION The global financial crisis of 2007-2010 and he subsequen Eurozone sovereign deb crisis served as a caalys owards furher invesigaion of he spillover effecs among he US, Eurozone and Asian sock markes. These inerdependencies could provide evidence wheher here is a seemingly growing inegraion in inernaional markes wih imporan implicaions for porfolio diversificaion. Only a limied number of sudies are available on he conagion effecs of he US subprime crisis and is repercussions. Angkinand e al. (2010) explored he spillovers from he US financial crisis o many developed economies by uilizing a srucural vecor auoregressive framework, and heir resuls indicaed ha he inerdependences increased dramaically when crisis emerged. Addiionally, Yilmaz (2010) invesigaed he exen of conagion across Souheas Asian equiy markes and found evidence of direc linkages among hem by means of variance decomposiion analysis of he sock reurns. Fidrmuc and Korhonen (2010) invesigaed he ransmission of he subprime crisis o China and India, wih he applicaion of dynamic correlaions, and concluded ha i had a significan effec on heir business cycles. Moreover, a growing ineres is developing wih regard o he spillover effecs beween he four emerging markes of Brazil, Russia, China and India and he US and Eurozone markes. According o Wilson and Purushohaman (2003) he four emerging markes, also called he BRICs, are he key performers in he world economy in he las wo decades and would become he dominan economies wihin he nex fory years. They encompass over 25% of he world's land coverage and 40% of he world's populaion and hold a combined GDP of 18.486 rillion dollars. The imporance of he BRICs has been recognized as hey currenly influence economic developmens in oher developed and emerging counries. The BRICs engine of growh is he rising Chinese economy, due o is expor driven policy and he accumulaed foreign invesmen from he developed counries, while India, Russia and Brazil seem o closely follow he pace of he Chinese economy. The magniude of heir growh has alered he share of economic performance globally. In he period 1988-2008, he BRICs GDP share in he world economy increased overall from 7% o 15%, while among he counries Chinese GDP raised from 1.7% o 7.1% and Brazil and Russia conribued 2.7 % each. According o recen saisics by he World Bank, he GDP of China in 2005 was $5.3 rillion, compared o $2.2 rillion using marke exchange raes. Brazil and Russia produce more han India, bu he laer is expeced o grow a he rae of 5% per year for he nex hiry years. Tarzi (2000, 2005) sudied he flow of foreign porfolio equiy invesmens and foreign direc invesmen o emerging markes beween 1986 and 1995 and repored ha sock marke capializaion in emerging counries grew from $171 billion o 1.9 rillion and he marke share held in capializaion increased 2

from 4% o 11%, mosly aribued o he BRICs. In he 1990s he foreign invesmen increased from 7% o 21% as a GDP raio in he developing counries, mos of which flew ino Brazil, China, and India. Moreover, Russia afer he fall of he Sovie Union and he crisis of 1997 has achieved price sabiliy wih a dropping inflaion from 215% in 1994 o 8.3% in 1998, and now is considered as an aracive marke for asse diversificaion. Finally, a massive increase is observed in he BRIC sock marke indices mosly during he las decade. Indicaively, Brazil s Bovespa Index rose by 342%, China s Shanghai Composie Index by 75%, India s Sensex Index by 250% and Russia s Micex Index increased by 638%. This evidence furher subsaniaes heir key role in he fuure of global economy. Causal links among sock markes could have imporan implicaions for hedging, rading sraegies and financial marke regulaions. The presence of long-erm linear and nonlinear relaionships may be uilized o achieve gains from inernaional porfolio diversificaion as well as o reduce sysemaic marke risk. The recen empirical evidence is invariably based on he linear Granger causaliy es (Granger, 1969). Bu, as noed by Hsieh (1989, 1991) and many ohers, financial ime series exhibi significan nonlinear feaures. Hiemsra and Jones (1994) claim ha a nonlinear nonparameric Granger causaliy es based on he work of Baek and Brock (1992) may be more effecive in revealing nonlinear causal relaionships in sock prices. In he presen sudy a mulisep filering mehodology is applied for examining dynamic relaionships. Firs, he linear and nonlinear dynamic linkages beween he US, European and BRIC sock markes, are explored via he parameric Granger causaliy es and he modified Baek-Brock es. Then, afer VAR filering of he reurn series, he residuals are examined by he modified Baek-Brock causaliy es. This sep ensures ha any remaining causaliy is sricly nonlinear in naure, as he VAR model has already purged he residuals of linear causaliy. Finally, in he las sep, he hypohesis of nonlinear non-causaliy is esed afer conrolling for condiional heeroskedasiciy in he daa using many specificaions of mulivariae GARCH models such as wih asymmeric impac of uncondiional shocks or a condiional correlaion marix. This approach allows capuring he shor-run movemens and he volailiy spillover mechanism, assuming ha spillovers are realizaions of inernaional news affecing he global equiy markes. The aim of he sudy is o es for he exisence of boh linear and nonlinear causal relaionships among he US, European and he BRIC sock markes. The invesigaed ime period sars from he inroducion of Euro and covers diverse regimes including he rise and fall of he echmarke bubble and he financial crisis of 2007-2010. I also includes he EU deb crisis, associaed wih he widening of bond yield spreads and he rise of credi defaul swaps, concerning Eurozone counries such as Greece, Ireland and Porugal. This crisis had a significan effec on he global sock 3

markes. In ha conex, i is worh invesigaing wheher hese crises may have changed he direcion and srengh of he causal relaionships among he examined markes. To enhance robusness in he resuls, he oal period is furher segmened ino disjoin sub-periods. Overall, his paper conribues o he lieraure in inernaional volailiy spillovers and inerdependencies by focusing on he BRIC markes ha have hus far received minor aenion in previous empirical sudies. I examines he ransmission of he US subprime crisis o he BRIC economies and invesigaes, hrough he analysis of heir equiy markes, as o wha exen hese markes have been affeced by he crisis. This sudy also invesigaes he impac of he Eurozone sovereign deb crisis on he linkages beween he Euro area and he BRICs by using he German financial marke as a proxy for he Eurozone. Currenly, he BRIC sock markes are of grea ineres o inernaional porfolio managers as hey are affeced by he currency markes due o global rade compeiiveness. Specifically, he financial markes of US, Germany, China and India are linked via he currency markes and rade and invesmen agreemens. The rade links of he US and Eurozone wih Russia and Brazil are raher small, albei heir sock markes are inerrelaed hrough he global oil and energy demand. Beyond he conagious affecs of he US crisis on he BRICs, he presen sudy also explores he so called decoupling phenomenon. This is based on he assumpion ha he emerging markes are now he major drivers of world economic growh as opposed o he US economy. However many recen sudies, e.g., Frank and Hesse (2009), Dooley and Huchison (2009) and Pula and Pelonen (2009), found no suppor for he decoupling view. As he BRIC economies have he fases growing markes, i would be ineresing o see wheher empirical evidence suppors he decoupling assumpion. In general, he improved knowledge of he direcion of inerdependencies and volailiy spillovers before and afer he financial crisis could provide valuable informaion o inernaional porfolio managers, mulinaional corporaions and policymakers in managing heir financial risks. The paper develops as follows: secion 2 provides a deailed overview of he lieraure on global marke inegraion and spillovers. The employed causaliy ess are analyzed in secion 3. The mulivariae sepwise filering mehodology is inroduced in secion 4. Secion 5 describes he daa and provides a preliminary saisical analysis. Secion 6 presens he empirical resuls and secion 7 he economic and policy implicaions. Finally, secion 8 provides he concluding remarks. 2. LITERATURE REVIEW The lieraure on sock marke inerrelaionships and inegraion is fairly rich. Since he beginning of 90s, he deregulaion of capial movemens lead o a sysemaic inerrelaion of he 4

major financial markes. This dependence indicaed a growing similariy in reacions owards macroeconomic policies or financial crises. However, he empirical evidence is diverse depending on he daa, mehodology and heoreical models used. Some previous works by Arshanapalli and Doukas (1993) and Hamao e al. (1990) showed ha inernaional sock markes are srongly inegraed. On he conrary, Roca (1999) and Smyh and Nandha (2003) showed ha global markes are weakly inerlinked. Moreover, he majoriy of sudies indicae ha he US marke leads oher developed markes (King and Wadhwani, 1990). Ye, here is subsanially less lieraure on sock marke linkages beween developed markes and emerging markes and very few concerning he BRIC economies (Garza-García and Vera-Juárez, 2010). Mos of he spillover sudies for he emerging markes have been conduced for Cenral and Easern Europe (Gilmore, and McManus, 2002; Gündüz and Haemi, 2005), Lain America and Asian counries (Choudhry, 1997; Chrisofi and Pericli, 1999; Chen e al., 2002). Lee e al. (2004) invesigae he linkages beween he daily reurns and volailiy of he NASDAQ and Asian markes using EGRARCH and VAR-based mehodology, and found srong evidence of volailiy spillovers from he US o Asia. Hamao e al. (1990) sudied he shor-erm inerdependence of price reurns and volailiies across he Tokyo, London, and New York sock markes. Shiller e al. (1991) repored ha Japanese marke paricipans are in general affeced by rading in New York, bu no vice versa. Benne and Kelleher (1988), Hamao, e al. (1990) and Susmel and Engle (1990) showed ha US reurns appear o cause he oher counries and ha lagged spillovers of price volailiy are found beween he major markes. Global conagion was observed during he Ocober 1987 crash in New York, according o King and Wadhwasi (1990). Rivas e al. (2006), who sudied he response of he Lain American sock markes agains European sock marke movemens via he applicaion of a VAR model for he period 1990-1998, repored variaions depending on he degree of invesmen allocaion of he European counry o Lain America. Moreover, using he Baek-Brock nonparameric causaliy es, Huner (2003) examined he inerdependencies of he emerging markes of Argenina, Chile, and Mexico. Using he same nonlinear es Ozdemir and Cakan (2007) examined he dynamic linkages among he sock marke indices of he US, Japan, France and he UK and found ha here is a srong bidirecional causal relaionship beween he US and he oher counries. Dornau (1998) and Peiro e al. (1998) using simple linear causaliy ess, analyzed informaion ransmission among he US, Japanese and German sock markes, while Baur and Jung (2006) examined he spillover effecs beween he US and German sock markes. The laer found ha boh marke reurns have a conemporaneous effec on each oher, bu ha here are no lagged spillovers from he previous day. 5

Some sudies have deal wih he lead lag relaionships among Asian markes and he naure of inerdependencies wih he developed economies. For insance, Phylakis and Ravazzolo (2003) found no linkages or ineracions among many Asian Pacific-Basin sock markes and Japan and US for he period 1980 1998. Najand (1996) used linear sae space models and deeced saisically significan linkages among he sock markes of Japan, Hong Kong and Singapore afer he 1987 US sock marke crash. In addiion, an increase in sock marke inerdependence afer he 1987 crisis was repored by Arshanapalli e al. (1995) for he emerging markes of Malaysia, Philippines, Thailand and he developed markes of Hong Kong, Singapore, he US and Japan for he period 1986 1992. Sheng and Tu (2000) invesigaed inerrelaions among 11 major sock markes in he Asian-Pacific region and he US in he pre- and pos-asian crisis period in 1997-1998 via he uilizaion of mulivariae coinegraion and error-correcion models. They showed ha long-run coinegraion relaionships emerged during and no before he period of he financial crisis. Finally, Weber (2007) revealed volailiy causaliy among he Asian-Pacific region markes for he period 1999 2006. Evenually, as here is subsanially less lieraure on sock marke linkages beween he developed markes and he BRIC economies, i should be ineresing o examine he naure and direcion of causaliy among hem. 3. CAUSALITY TESTING The convenional approach of causaliy esing is based on he Granger es (Granger, 1969), which assumes a parameric, linear model for he condiional mean. This specificaion is simple and appealing as he es is reduced o deermining wheher he lags of one examined variable ener ino he equaion of he oher, albei i requires he lineariy assumpion. In his seup, vecor auoregressive residuals are sensiive only o causaliy in he condiional mean while co-variables may affec he condiional disribuion in nonlinear paerns. However, Baek and Brock (1992) noed ha he parameric linear Granger causaliy es has low power agains cerain nonlinear alernaives or higher momens. As a resul, nonparameric causaliy ess have been proposed in he lieraure direcly emphasizing on predicion wihou imposing a linear funcional form. Hiemsra and Jones (1994) proposed a causaliy-in-probabiliy es for nonlinear dynamic relaionship which is applied o he residuals of vecor auoregressions and i is based on he condiional correlaion inegrals of lead lag vecors of he variables. This is a modified version of he Baek and Brock es ha relaxes he assumpion of i.i.d ime series and insead allows each series o display weak (or shor-erm) emporal dependence. I deecs he nonlinear causal relaionship beween variables, including 6

second- or higher-order momen effecs, by esing wheher he pas values influence presen and fuure values. In wha follows, he wo causaliy ess are formally described. 3.1 Linear causaliy The linear Granger causaliy es (Granger, 1969) is based on a reduced-form vecor auoregression (VAR) model. If y = y 1,..., y is he vecor of endogenous variables and l he l number of lags, he VAR(l) model is given by y l = Dy + ε s= 1 s s (1) where D is he l l parameer marix and ε he residual vecor, for which Eε ( ) =0 and s ' ε s E( εε ) ε = s = 0 s. In case of wo saionary ime series { x } and { } y he bivariae VAR model is given by x = D( l) x + F( l) y + ε x, y = G( l) x + J( l) y + ε y, = 1,2,..., N (2) where D( l), F( l), G( l ) and Jl ( ) are lag polynomials wih roos ouside he uni circle and he error erms are i.i.d. processes wih zero mean and consan variance. The es wheher y sricly Granger causes x is simply a es of he join resricion ha all coefficiens of he lag polynomial Fl ( ) are zero, whils a es of wheher x sricly Granger causes y is a es regarding Gl ( ). In he unidirecional case he null hypohesis of no Granger causaliy is rejeced if he exclusion resricion is rejeced, whereas if boh Fl ( ) and Gl ( ) join ess for significance are differen from zero he series are bi-causally relaed. However, in order o explore possible effecs of coinegraion a vecor auoregression model in error correcion form (Vecor Error Correcion Model-VECM) is esimaed using he mehodology developed by Engle and Granger (1987) and expanded by Johansen (1988) and Johansen and Juselius (1990). The bivariae VECM model has he following form T x = p 1 λ y x + D( l) x + F( l) y + ε T y = p 1 λ y x + G( l) x + J( l) y + ε 1 1 1 x, 2 1 1 y, = 1,2,..., N (3) where 1 λ he coinegraion row-vecor and λ he coinegraion coefficien. Thus, in case of coinegraed ime series { x } and { } Gl ( ) via he VECM specificaion. y linear Granger causaliy should be invesigaed on Fl ( ) and 7

3.2 Nonlinear causaliy Le ( ) F x 1 Θ denoe he condiional probabiliy disribuion of x given he informaion se Θ, which consiss of an L -lengh lagged vecor of 1 x lengh lagged vecor of Ly y, y ( y, y,..., y L L L+ 1 1) y for a given pair of lags L and L he following null hypohesis x y H F x y ( ) y y : F x y 0 1 1 L Lx x, ( x, x,..., x L L L + 1 1) L Θ = Θ y (4) m Denoing he m-lengh lead vecor of x ( x, x,..., x ) + 1 + m 1 x and an L - y x x x. Hiemsra and Jones (1994) consider esing, for Z, he claim made by Hiemsra and Jones (1994) is ha he null hypohesis given in Eq. (4) implies for all ε> 0 m m l L l L x x y y P x x < ε x x < ε, y y < ε s L s L L s L x x y y m m l L x x = P x x < ε x x < ε s L s L (5) x x For he ime series of realizaions { x } and { } y, = 1,..., T, he nonparameric es consiss of choosing a value for ε ypically in 0.5, 1.5 afer uni variance normalizaion, and esing Eq. (5) by expressing he condiional probabiliies in erms of he corresponding raios of join probabiliies m+ L m+ L L L x x y y C1( m+ L, L, ε) P, x y x x < ε y y < ε L s L L s L x x y y L L L L x x y y C2( L, L, ε) P, x y x x < ε y y < ε L s L L s L x x y y m+ L m+ L x x C3( m + L, ε x ) P x ε L x s L < x x L L x x C4( L, ε x ) P x ε L x s L < x x (6) Thus, Eq. (5) can be formulaed as ( ε x y ) (,, ε) 2 ( ε x ) (, ε) C m+ L, L, 1 C m+ L, 3 = (7) C L L C L x y Using correlaion-inegral esimaors and under he assumpions ha { x } and { } 4 x y are sricly saionary, weakly dependen and saisfy he mixing condiions of Denker and Keller (1983), Hiemsra and Jones (1994) show ha ( ε x y ) (,, ε, ) C m+ L, L,, n 1 C3( m L, ε, n x ) 2 n + N( 0, σ ( ml,, L, ε x y )) (8) C L L n C 2 4( L, ε, n x y x ) 8

2 wih σ ( ml,, L, ) x y ε as given in heir appendix. One-sided criical values are used based on his asympoic resul, rejecing when he observed value of he es saisic in Eq. (8) is oo large. 4. STEPWISE MULTIVARIATE FILTERING A hree-sep mehodology is inroduced in order o explore he direcion and naure of he dynamic relaionships. In he firs pre-filering sep, he linear and nonlinear linkages are explored via he applicaion of boh he Granger causaliy es and he modified Baek-Brock es on he raw log-differenced ime series of he sock indices. Then, VAR filering is implemened on he reurn series and he residuals are examined pairwise by he modified Baek-Brock es. Afer his sep any remaining causaliy is sricly nonlinear in naure, as he VAR model has already filered ou linear causaliy of he residuals. Finally, he hypohesis of nonlinear non-causaliy is invesigaed afer conrolling for condiional heeroskedasiciy in he daa using mulivariae GARCH models wih various represenaions. This approach allows he enire variance-covariance srucure and he correlaion marix of he sock marke inerrelaionship o be incorporaed. The use of he modified Baek-Brock es on filered daa wih a mulivariae GARCH model enables o deermine wheher he posied model is sufficien o describe he relaionship among he series. If he saisical evidence of nonlinear Granger causaliy lies in he condiional variances and covariances hen i would be srongly reduced when he appropriae mulivariae GARCH model is fied o he raw or linearly VAR filered daa. Many GARCH models can be used for his purpose. However, failure o accep he no-causaliy null hypohesis may also consiue evidence ha he seleced mulivariae GARCH model was incorrecly specified. This line of analysis is similar o he use of he univariae BDS es on raw daa and on GARCH models (Brock e al., 1996; Brooks, 1996; Hsieh, 1989). Moreover, he correc represenaion of he condiional covariances in financial markes is guaraneed by he proper parameerizaion of he univariae condiional variances. In his sudy, in order o accoun for he sylized facs of he sock marke reurns, he asymmeric Glosen- Jagannahan-Runkle (1993) GJR-GARCH(1,1) specificaion is used for modelling he univariae condiional variances in he condiional correlaion represenaions. Hence, i is properly assumed ha he asymmeric behaviour, i.e., skewness of he condiional covariances in he lef ails is guaraneed by he GJR-GARCH parameerizaion of he condiional variances and ha he reurn disribuions exhibi high kurosis due o he presence of fa ails. The mulivariae models used for he sepwise filering are formally described hereafer. 9

Le { y } be a vecor sochasic reurn process of dimension Ν 1 and ω a finie vecor of parameers. Then y = µ ( ω ) + ε 1 2 where µ ( θ) is he condiional mean vecor and ε = ( ω) 1 2 where ( ) H z H ω is a N Nposiive definie marix. Furhermore, he N 1random vecor z have E( z ) = 0 and ( ) Var z = I as he firs wo momens where I N is he ideniy marix. Hence H is he condiional variance marix of y. N 4.1 VEC and BEKK Models In he general VEC model, each elemen of H is a linear funcion of he lagged squared errors and cross-producs of errors and lagged values of H elemens (Bollerslev e al., 1988). The VEC(1, 1) model is defined as where h vech( H) par of a N N =, η vech ( εε ) h = α+λ η +Μ h (9) 1 1 = and vech (). is he operaor ha sacks he lower riangular marix as a ( ) N N+ 1 2 1 vecor. Also, Λ and Μ are square parameer marices and α is a parameer vecor. The large number of parameers, N( N )( N( N ) ) + 1 + 1 + 1 2, implies ha in pracice his model is used only in he bivariae case. To overcome his problem Bollerslev e al. (1988) sugges he diagonal VEC (DVEC) model in which he Λ and Μ marices are assumed o be diagonal, each elemen h depending only on is own lag and on he previous value of εε. ij i j As i is difficul o guaranee he posiiviy of H in he VEC represenaion wihou imposing srong resricions on he parameers, Engle and Kroner (1995) propose a new paramerizaion of H ha imposes is posiiviy, namely he Baba-Engle-Kraf-Kroner (BEKK) model. The full BEKK(1, 1, K) model is defined as: K K = + Λ ε ε Λ + Μ H Μ k 1 1 k k 1 k k= 1 k= 1 H C C (10) where C, Λ and Μ are N N marices bu C is upper riangular. The summaion limi K k k deermines he generaliy of he process 2 and he sufficien condiions o idenify BEKK models are ha, k,11 k,11 Λ Μ and he diagonal elemens of C are resriced o be posiive. To reduce he 2 The BEKK model is a special case of he VEC model (Engle and Kroner, 1995). To avoid observaionally equivalen srucures Engle and Kroner (1995) provide sufficien condiions o idenify BEKK models wih K= 1(Bauwens e al., 2006). 10

N( 5N+ 1) 2 number of parameers in he BEKK(1,1,1) model and consequenly o reduce he generaliy, a diagonal BEKK model can be imposed, i.e. Λ and Μ in (10) are diagonal marices. k k Maximum likelihood esimaion is used for VEC and BEKK models. Suppose ha he sochasic process has condiional mean, condiional variance marix and condiional disribuion µ ( ω), H ( ω ) and p ( y ξ, I ) respecively, where ξ ( ωη) 0 0 0 1 = is a r-dimensional parameer 0 0 0 vecor and η is he vecor of he parameers of he innovaions disribuion z, ha are assumed 0 i.i.d 3. The densiy funcion is denoed ( ( ) ) g z ω η, where η is a vecor of nuisance parameers. The problem o solve is hus o maximize he sample loglikelihood L T ξ T 1 = 1, = µ η 1 2 1 2 ( ξ) = log f( y, I ) wih f( y ξ I ) H g H ( y ) 1 and he dependence wih respec o ω occurs hrough µ and H. The erm 1 2 H is he Jacobian ha arises in he ransformaion from he innovaions o he observables. The mos commonly employed disribuion in he lieraure is he mulivariae normal (Harvey e al., 1992; Fiorenini e al., 2003) 4. 4.2 Condiional Correlaion Models These models allow boh for individual condiional variances and a condiional correlaion marix beween he individual series. Bollerslev (1990) proposes a class of MGARCH models in which he condiional correlaions are consan and condiional covariances are proporional o he produc of he corresponding condiional sandard deviaions. This resricion grealy reduces he number of unknown parameers and simplifies he esimaion. The CCC model is defined as ( ρ h h ) H = GRG = (11) ij ii jj 1 2 1 2 where G diag h 11 h = NN. I should be noed ha h ii can be defined as any univariae GARCH model, and R= ( ρ ij ) is a symmeric posiive definie marix conaining he consan condiional correlaions wih ρ = 1, i. The classical CCC model has a GARCH(1, 1) specificaion for each ii condiional variance in G. In his sudy he Glosen-Jagannahan-Runkle (1993) GJR-GARCH(1,1) model is applied 3 The i.i.d. assumpion may be replaced by he weaker assumpion ha is a maringale difference sequence, bu his ype of assumpion does no ranslae ino he likelihood funcion. The likelihood funcion for he i.i.d. case can hen be viewed as a quasi-likelihood funcion (Bauwens e al., 2006). 4 The asympoic properies of ML and QML esimaors in mulivariae GARCH models are no ye esablished, and are difficul o derive from hese assumpions. Consisency has been shown by Jeanheau (1998), bu asympoic normaliy of he QMLE is no esablished generally. Gourieroux (1997) proves i for a general formulaion, while Come and Lieberman (2003) prove i for he BEKK formulaion (Bauwens e al., 2006). 11

2 2 2 2 λ β γ δ ii, 1 ii, 1 1 1 h = + Y + h + Y I (12) where λ 0, β 0, γ 0, δ 0, I 1 = when Y 1 1< 0 and zero oherwise. H is posiive definie if and only if all he N condiional variances are posiive and R is posiive definie. The uncondiional variances are easily obained, as in he univariae case, bu he uncondiional covariances are difficul o calculae because of he nonlineariy in Eq. (11). The assumpion of a consan condiional correlaion ofen seems unrealisic in many empirical applicaions. Chrisodoulakis and Sachell (2002), Engle (2002) and Tse and Tsui (2002) propose a generalizaion of he CCC model by making he condiional correlaion marix imedependen. They propose a dynamic condiional correlaion (DCC) model, wih he addiional assumpion ha he ime-dependen condiional correlaion marix has o be posiive definie. This is guaraneed under simple condiions on he parameers. The DCC model of Chrisodoulakis and Sachell (2002) uses he Fisher ransformaion of he correlaion coefficien bu i is only a bivariae model. The DCC model of Engle (2002) is genuinely mulivariae and paricularly useful when modelling high-dimensional daa samples. The DCC model of Engle (2002) is defined as wih H = GRG (13) R 1 2 1 2 1 2 1 2 diag w 11, w NN, Wdiag w 11, w = NN,. The N N symmeric posiive definie marix = ( ij,) is given by W ( 1 α β ) W αu 1 u 1 βw 1 W w = + +, wih u, = ( ε, / h, ). W is he i i ii N N uncondiional variance marix of u, and α and β are non-negaive scalar parameers saisfying α+ β< 1. The correlaion coefficien in he bivariae case is ρ 12 = ( 1 ) w + u u + (( 1 ) )( 1 ) α β α βw 12 1, 1 2, 1 12, 1 ( ) 2 2 11 1, 1 11, 1 22 2, 1 22, 1 α β w + αu + βw α β w + αu + βw (14) One drawback of he DCC models is ha αβ, are scalars, so ha all he condiional correlaions follow he same dynamics. Neverheless, i is necessary in order o ensure ha R is posiive definie. The DCC model can be esimaed consisenly using a wo-sep approach. Engle and Sheppard (2001) show ha in he case of he Engle (2002) DCC model, he loglikelihood can be wrien as he sum of a mean and volailiy par, depending on a se of unknown parameers ω, and 1 a correlaion par ha depends on ω. Thus, he quasi-loglikelihood funcion corresponds o he sum 2 of loglikelihood funcions of N univariae models as T N 1 ( ω ) = ( ) + ( µ ) 2 1 QL1 logh y h. Given ω 1 and under appropriae regulariy T ii i i ii 2= 1i= 1 12

condiions, a consisen, bu inefficien, esimaor of ω can be obained by maximizing 2 T 1 1 ( ω ω 2 1) = ( R + ur u ) QL2 log T 2, where u 1 G ( y µ ) = 1 = (Bauwens e al., 2006). The sum of he likelihood funcions, plus half of he oal sum of squared sandardized residuals ( uu 2, which is almos equal o NT 2), is equal o he loglikelihood for BEKK models. In his sudy, he asymmeric Glosen-Jagannahan-Runkle (1993) GJR-GARCH(1,1) specificaion is used for modelling he univariae condiional variances in boh CCC and DCC models in order o properly accoun for he skewness of he reurn disribuions in he lef ails as well as for he high kurosis due o he presence of fa ails. 5. DATA DESCRIPTION AND PRELIMINARY ANALYSIS The daa comprise daily sock index reurns of he US, Europe and he BRIC sock markes defined as log( ) log( ) =, where 1 r P P P is he closing level on day. Specifically, he New York Sock Exchange (NYSE) index and he German index DAX30 are considered for he Unied Saes and he Euro area respecively, while for he BRICs he Bovespa (Brazil), RTS index (Russia), Bombay Sensex 100 (India) and Shangai SE Composie (China). The indices are denominaed relaive o Unied Saes dollar (USD) o accoun for he exchange risk under he perspecive of he same invesor (Chan e al., 2000) 5. The oal sample spans a ime period from he inroducion of he Euro, i.e., January 5, 1999, o February 28, 2011 (3170 observaions) 6. The invesigaed ime period covers many exreme evens and differen regimes including among oher he rise and fall of he echmarke bubble (or do-com bubble), he financial crisis of 2007-2010 and he Eurozone sovereign deb crisis, iniiaed in early 2010. Specifically, he do-com bubble involved mos of he companies relaed o he new e-business secor in he mid-1990s. However, here are many inerpreaions abou he specific poin in ime in which he mouning bubble sared. The prevailing belief ses he saring dae precisely on he 5 h of December 1996, when he former chairman of he Fed, Alan Greenspan, pronounced he famous irraional exuberance speech a he Washingon D.C.-based American Enerprise Insiue. In March 10, 2000 he echnology NASDAQ Composie index peaked a 5,048.62 (inra-day peak 5,132.52), more han double is value jus a year before, corresponding o he dae 5 The sudy was also conduced wih he sock indices in local currencies in order o invesigae he effec of differen numeraires. The descripive saisics and he causaliy resuls were no significanly differen, albei some minor differences were observed in skewness and kurosis. 6 The Euro currency was inroduced (in non-physical form) on 1 January 1999, when he naional currencies of he Eurozone counries ceased o exis independenly and were locked a fixed raes. Euro values before 1999 usually refer o ECU (European Currency Uni), a baske of EU currencies. Since he firs rading day Euro raised o $1.19. However, by he end of 1999 he Euro had dropped o pariy wih he dollar leading o emergency acion from he G7 o suppor he common currency in 2001. 13

when he do-com bubble burs (Greenspan, 2007). Moreover, he financial crisis was riggered by a liquidiy shorfall in he Unied Saes banking sysem, which resuled in he collapse of large financial insiuions, he "bail ou" of banks by naional governmens, urbulence and downurns in sock markes around he world (Krugman, 2009). The crisis began o affec he financial secor in 2007 when HSBC, he world's larges bank, wroe down is holdings of subprime-relaed morgagebacked-securiies by $10.5 billion, he firs major subprime relaed loss o be repored. On Sepember 15, 2008, he Lehman Brohers Holdings filed for bankrupcy proecion following he massive exodus of mos of is cliens, drasic losses in is sock, and devaluaion of is asses by credi raing agencies. The filing marked he larges bankrupcy in U.S. hisory. Finally, he sovereign deb crisis in early 2010 concerning Eurozone counries such as Greece, Ireland and Porugal is also invesigaed. I led o a crisis of confidence as well as he widening of bond yield spreads and risk insurance on credi defaul swaps beween hese counries and oher Eurozone members. A he beginning of 2010, a 500 million governmen bond aucion in Porugal raised only 300 million, increasing he cos of insuring agains a Poruguese deb defaul (Blacksone e al., 2010). The failed Poruguese bond aucion furher inensified he fear ha he emerging sovereign deb issues could become a global conagion. These fears led o a weakening of he Euro and a widespread global sock and commodiy sell off in February 2010 and he following monhs. Moreover, Greece was he focal poin of he crisis hrough mid-march 2010 and April. Greek governmen searched for a poenial bailou plan which was evenually decided by Eurozone member saes - in case i failed o raise he necessary money o fill is budge gap hrough he credi markes. The bailou plan for Greece by he EU and IMF failed o reassure invesors, hus leading o an agreemen of an unprecedened defence package of 750,000 by he European Union and he IMF, in order o preven he relenless speculaive aacks on he common currency and evenually resore sabiliy. The crisis inensified owards he end of 2009 when here was an abrup increase in he spreads due o he downgrading of Greece's credi raing by all hree major inernaional credi agencies (Fich, Moody's and S&P). The robusness of he resuls is examined in several sub-periods. The financial crisis is considered as a major breakpoin for he idenificaion of he sub-periods, hence seing a plaform for deparure for causaliy ess. On February 22, 2007 he HSBC, he world's larges bank of 2008, wroe down is holdings of morgage-backed-securiies by $10.5 billion. This was he firs major subprime relaed loss o be repored, hus he aforemenioned dae is used as he crisis breakpoin 7. Overall, he examined sub-periods are he following: P1: January 5, 1999 o February 21, 2007 (2122 7 In addiion o he economic raionale he breakpoin selecion is saisically esed via he applicaion on he sock reurn series of Chow's es (Chow, 1960) for known (imposed) breaks and he cumulaive sum (CUSUM) es (Brown e al., 1975) for unknown poins. The seleced breakpoin has also been verified wih he Bai and Perron (2003) and Zivo and Andrews (1992) ess. 14

observaions) and P2: February 22, 2007 o February 28, 2011 (1048 observaions). In addiion, he enire sample period PToal: January 5, 1999 o February 28, 2011 (3170 observaions) is comparaively invesigaed. The descripive saisics for all ime series are presened in Table 1. The Jarque-Bera muliplier for all sock markes in all periods is saisically significan, hereby indicaing ha he reurn disribuions are no normal. They also exhibi zero mean-reversal and low variance. In general, kurosis for reurns in all periods - wih he excepion of NYSE and DAX in P1 - is larger han normal which implies he presence of fa ails, exreme observaions and possibly volailiy clusering. As indicaed by skewness, he sock index reurns have a longer lef ail, while NYSE and DAX before he crisis (P1) appear o be close o symmeric. Based on he Ljung-Box Q-saisic, he hypohesis ha all correlaion coefficiens of he reurns up o 12 are joinly zero is no rejeced in he majoriy of cases, especially for he BRIC economies. Therefore, i can be inferred ha he reurn series presen nonlinear dependence due possibly o clusering effecs or condiional heeroscedasiciy, a fac ha is furher subsaniaed by he resuls of he ARCH LM-saisic. The differences beween he pre- and pos-crisis periods P1 and P2 are quie eviden in Table 1 where a significan increase in he sandard deviaion can be observed in P2 for all reurns as well as increased fa-ailedness refleced in he higher kurosis. Addiionally, P2 winessed many occasional negaive spikes as i can be inferred from he skewness. Table 1 also repors he conemporaneous correlaion marix for all periods. Significan sample cross-correlaions are noed for NYSE, DAX and BOVESPA indicaing a high inerrelaionship among hose markes, while in general all series are posiively correlaed in boh periods. Low correlaion or uncorrelaedness is also observed mosly for China. More imporanly, afer he crisis emerged (P2) he cross-correlaions among all markes are significanly increased boh beween he US or Eurozone and he BRICs, as well as among he BRIC economies. However, hese resuls should be cauiously inerpreed as linear correlaions canno fully capure he dynamic linkages in a reliable way. Consequenly, a long-erm causaliy analysis is necessary. Nonsaionariy is esed wih he Augmened Dickey-Fuller (ADF) and Phillips-Perron (PP) ess, boh of which are applied o he log-levels and reurns (Table 2). The lag lengh is seleced using he Schwarz Bayesian Informaion Crierion (SIC), while for he PP es he bandwidh is auomaically seleced using Newey and Wes (1994) mehod wih Barle kernel. All variables appear o be nonsaionary in log-levels and saionary in log-reurns based on he repored p-values. In paricular, he ADF and PP ess indicae ha he null of a uni roo canno be rejeced a 1% for he log-levels in all periods, regardless of wheher a consan and linear rend or only a consan is 15

included in he deerminisic componen. Furhermore, boh ess show ha he log-reurns are saionary as he null can be soundly rejeced for all sock indices and periods. The combined resuls from he uni roo ess sugges ha all invesigaed log-levels appear o be I ( 1) processes. Based on hese resuls and in order o idenify he correc model specificaion for he invesigaion of linear and nonlinear causaliy (i.e., VAR or VECM), he race and maximum eigenvalue saisics were furher applied o he log-prices series o explore possible effecs of coinegraion (Johansen, 1988; Johansen and Juselius, 1990). For all pairs he Johansen ess did no idenified any coinegraing vecors and he null of no coinegraion was no rejeced (Table 3). [Please inser Tables 1, 2 and 3 here] 6. EMPIRICAL RESULTS The naure and direcion of causaliies is explored iniially via he applicaion of boh he Granger causaliy es and he modified Baek-Brock es on he raw sock index reurns. Moreover, as described in Secion 4, vecor auoregressive filering is implemened on he reurn series and he residuals are examined pairwise by he modified Baek-Brock es. Any remaining causaliy is sricly nonlinear in naure, as he auoregressive filer has already whiened ou he linear causaliy of he residuals. Finally, nonlinear non-causaliy is invesigaed afer conrolling for condiional heeroskedasiciy hrough mulivariae GARCH filering. Based on he coinegraion resuls in Secion 5, linear and nonlinear causaliy is invesigaed wih a VAR model represenaion. The resuls from he Schwarz Informaion Crierion crierion, aking ino consideraion many lag specificaions for each pairwise VAR modelling, indicae in mos of he cases four lags for he sock index reurn series in all periods 8. For he modified Baek-Brock nonlinear causaliy es in wha follows, he common lag lenghs used are l = l = 1. The es is applied on he VAR residuals derived from he pairwise linear causaliy esing and he disance measure is se o ε= 1.5, as suggesed by Hiemsra and Jones (1994) 9. Moreover, in order o capure he informaion ransmission mechanism beween he US, he Eurozone and BRIC equiy markes and o reduce he effec of non-synchronous rading, he chronological order of he operaing ime of he equiy markes mus be aken ino accoun and specified ime differenials should be used appropriaely. The rading hours of he sock exchanges X Y 8 Each bivariae vecor auoregression (VAR) examined for he causaliy analysis, conained up o welve lags. The Schwarz Bayesian Informaion crierion (SIC) concluded in mos cases on four lags for he US, Eurozone and BRIC sock index reurn series in he invesigaed periods. Overall, no significan differeniaion in he causaliy resuls is observed in assuming oher han he seleced lags for he reurn ime series. 9 In he esimaion ε= 0.5 and ε= 1 were also considered, wih no qualiaive difference in he resuls. In addiion, evidence from he second and hird common lag lenghs did no significanly modified he nonlinear causaliy resuls. 16

are no perfecly synchronized, hough here are several overlapping hours in each rading day for some markes. I is well known ha rading day sars in he Asia-Pacific region and he order of he opening of he five equiy markes is: China, India, Russia, EU (Germany), Brazil and he las marke o rade is US. Table 4 presens he rading hours sequence wih he use of Greenwich ime as well as US Easern ime wih a chronological order from day 1 o day. The US sock marke shares almos all of is rading hours wih he Brazilian sock marke. Also, he Chinese, Indian, and Russian markes have overlapping rading hours and in general heir rading aciviy can be considered o a large exen concurren. However, he US reurns are known o raders before he opening of he sock markes in China, India, and Russia. Because of hese non-concurren rading hours a hypohesis esing problem arises; if he US and he Asia-Pacific region ha share almos enirely non-overlapping rading hours - were esed concurrenly a ime (corresponding o a weekday in he ime series calendar daa), hen he Granger causaliy ess would mos probably presume he biased unidirecional causaliies, as news coming from US a ime 1 affecs consisenly he oher aforemenioned markes a ime. Similarly, he unidirecional causaliy from he Asian markes a ime o he US of he same calendar day is also pre-assumed and obvious. Thus, in order o remove he esing bias due o he informaion flows, align he ime series calendar daes and evenually avoid inaccurae conclusions, he bi-direcional null hypohesis for he linear and nonlinear causaliy will be esed in he one direcion a ime 1 for he US marke, while for he oher direcion on he concurren series a ime. I is noed ha he linear and nonlinear causaliy exercise performed in his sudy aims a invesigaing he spillover, second-momen effecs (if any) ha drive he marke inerdependencies and no he naural one-period lagged flows of news among he non-overlapping markes. For example, on he basis of he ime series calendar daily daa used, in case of China (he same applies o India and Russia) i will be esed ha H : China does no Granger cause US 0 1, while for he oher direcion H : US does no Granger cause China. 0 10 In general, he differences in closing imes could poenially cause sequenial price responses o common informaion ha could be misaken for causal linkages. Inraday daa could be used o disassociae hese sequenial responses from causal ransmissions wihin a paricular day. Regreably hese daa are no available for he BRIC markes. 10 In boh cases H assumes an obvious dependence and hus is no considered. Addiionally, in erms of he ime index and consequenly 1 he calendar daa order used in he causaliy invesigaion, i is equivalen o es he hypoheses H : China does no Granger cause US and H : US does no Granger cause China 0 + 1 0 + 1 + 1 a lower frequency (weekly) and he resuls analyzed in he nex secions were found similar. 17. The causaliy exercise was also conduced in

Overall, he resuls are repored in he corresponding columns of Table 5. The simplifying noaion ** is used o indicae ha he corresponding p-value of he causaliy es is smaller han 1% and * ha he corresponding p-value of a es is in he range 1-5%; Direcional causaliies will be denoed in ex by he funcional represenaion or for unidirecional and bidirecional linkage respecively. [Please inser Table 4 here] 6.1 Linear and nonlinear causaliy deecion on raw reurns The linear causaliy resuls on raw reurns reveal he srong feedback relaionships US Russia, US India and EU Brazil a he 1% significance level for all periods. In addiion, he srong unidirecional linkages of US Brazil and EU China are observed in all periods. Ineresingly, hese economies appear o lack any causal relaionship in he oher direcion. Moreover, US and China presen a srong bidirecional linkage in P2 and PToal. The fac ha i vanishes in he pre-crisis period P1 suggess ha i was generaed afer he burs of he financial crisis, which predominaely impaced he US sock marke. Finally, EU appears o linearly Granger cause Russia and India in all periods a he 1% significance level, ye he opposie relaionship is only observed a he 5% level in P2 and no deeced a all in P1. Thus, in he period before he crisis neiher Russia nor India Granger caused he German financial marke. Table 5 also repors he resuls of he nonlinear causaliy on he raw reurns. Ineresingly, before VAR filering, persisen nonlinear bidirecional inerdependencies are deeced in all periods for all counries excep China. Specifically, EU appears o weakly cause China only in P2, while US causes China in P2 and PToal a he 1% significance level. In addiion, he srong unidirecional linkage China US is also revealed in PToal. 6.2 Nonlinear causaliy esing on VAR-filered residuals The resuls on raw series sugges ha here are significan and persisen linear and nonlinear causal linkages beween US, EU and he BRICs. Nex, he modified Baek-Brock es is reapplied on filered VAR-residuals o ensure ha any causaliy found is sricly nonlinear in naure. The applicaion of he nonlinear causaliy es poins owards he preservaion of he resuls repored for he non-whiened raw reurns. Ineresingly, he comparison of he summary resuls in Table 5 reveals idenically significan nonlinear relaionships wih few excepions. In paricular, he causal relaionship EU India in P1 has now vanished, while he unidirecional linkages US Brazil and US China in P2 have weakened and are now deeced a he 5% significance level. The naure and source of he deeced nonlineariies may also imply a emporary, or long-erm, causal relaionship 18

among he invesigaed financial markes. Indicaively, second-momen effecs migh induce nonlinear causaliy especially in he ime period afer he financial crisis. Given he saus of he US economy, an innovaion in he US sock marke may seriously affec he exen of dependency and volailiy spillovers across BRICs markes. The volailiy ransmission mechanism can be invesigaed afer conrolling for condiional heeroskedasiciy using a mulivariae represenaion, so ha enire variance-covariance srucure of he marke inerrelaionship is incorporaed. 6.3 Residual nonlinear inerdependencies afer mulivariae GARCH filering Afer GARCH-BEKK filering, only in a few cases he nonlinear linkages are removed. Specifically, he EU China relaionship in P2 is purged as well as he US Brazil in P1 and P2 and he Brazil US, ye only in P1. In addiion, he unidirecional linkage of Russia EU is purged in P1 and P2 and weakened in PToal. The fac ha all oher remaining nonlinear inerdependencies afer VAR filering persis even afer GARCH-BEKK filering, suggess ha volailiy effecs under his paricular variance-covariance represenaion are no he ones inducing nonlinear causaliy. Insead, mos of he nonlinear linkages afer CCC-GARCH and DCC-GARCH filering have vanished. Of course his does no apply o all examined pairs, bu overall he whiened residuals afer filering heeroscedasiciy wih hese models presen differen causal relaionships compared o he GARCH-BEKK 11. Addiionally, he major differences wih he VAR-filered residuals indicae ha he nonlinear causaliy was largely due o simple volailiy effecs ha were no capured by he BEKK formulaion. Indeed, he use of he asymmeric GJR-GARCH specificaion for modelling he univariae condiional variances in boh CCC and DCC models in order o accoun for he skewness and high kurosis of he reurn disribuions, provided wih beer resuls 12. In paricular, for China all linkages have disappeared vis-à-vis he US and EU, while only a few sill remain in he pos-crisis period P2 (and PToal) for he pair US-Brazil and for EU and Brazil, Russia and India. Moreover, he remaining relaionships are considerably weakened in erms of saisical significance. However, his is no indicaive of a general conclusion. Ineresingly, significan nonlinear inerdependencies remain afer he condiional correlaion filering, revealing ha second-momen effecs and volailiy spillovers are probably no he only ones inducing nonlinear causaliy. In case of he US-Russia and US-India pairs, srong feedback relaionships remain in all periods excep for P2. Consequenly, he dependencies in he pos-crisis period were probably due o second-momen effecs, ha is why hey were capured and removed by he GARCH filering. On he conrary, i 11 A GARCH-BEKK specificaion wih Suden- error srucure has also been ried, bu he nonlinear causaliy resuls were no modified. 12 The use of he classical Bollerslev (1986) GARCH(1,1) specificaion for modelling he univariae condiional variance in CCC and DCC proved o be less successful in erms of remaining causaliies, especially for EU and Brazil, Russia and India. 19

seems ha he causal linkages in he pre-crisis period P1 are no aribued o a volailiy ransmission mechanism, as hey were nor purged in P1 bu mos imporanly remain also in PToal. Thus, he US- Russia and US-India causaliy inerdependencies were no eroded due o volailiy clusering during he pre-crisis period (Bekaer and Harvey, 2003). Moreover, hey are no saisically weakened a all afer he mulivariae GARCH filering. One possible explanaion could be ha hird- or highermomen causaliy may be a significan facor of he remaining inerdependence. Also, he remaining causaliies could be due o ail dependency ha exiss even if volailiy clusering is accouned for. These resuls could also lead o policy implicaions considering porfolio diversificaion in hese financial markes. [Please inser Table 5 here] 7. ECONOMIC AND POLICY IMPLICATIONS An ineresing conclusion wih respec o he globalizaion of he sock markes emerged from his sudy, in ha all markes considered here have become more inernaionally inegraed afer he US financial crisis and he consequen Eurozone sovereign deb crisis. Moreover, i is eviden from he resuls ha mean and volailiy spillover effecs exis no only from he US marke o he developed equiy markes of Europe and Asia as shown in previous sudies, bu hey also exis beween he US he BRIC economies. Anoher finding is ha some differences exis beween he persisence and srengh of he causal linkages in he pre- and pos-crisis period. In view of he fac ha BRICs perain srong linkages wih he global economy hrough rade and financial markes, a conagion effec was furher subsaniaed due he ransmission of he US subprime crisis o he BRIC equiy markes. For insance, he pos-crisis period exhibis highly significan feedback spillovers beween he US marke and he BRICs, wih he excepion of China which is always Granger caused by US and he EU. Resuls from boh periods show ha India and Russian equiy reurns were highly affeced by he movemens in he US marke. For Russia in paricular, a clear evidence of conagion is esablished afer he Lehman broher s bankrupcy. The leading role of he US marke in he world financial sysem is visible hroughou all causaliy ess and in all ime periods, a fac ha is consisen wih earlier findings by Eun and Shim (1989). On he oher hand, he Chinese marke has relaively lile influence on he sock price movemens in he US and EU, paricularly once linear effecs have been removed hrough VARfilering. This finding provides a relaive suppor o he view ha China plays a passive role in ransmiing informaion o oher sock markes. 20

Moreover, he volailiy of US, Chinese, and Indian equiy markes may be inerrelaed hrough invesmen, rade and macroeconomic fundamenals, so ha news abou he US economic condiions mos likely have implicaions for he Chinese and Indian economies and financial markes. However, he rade linkages beween he pairs US-Russia and US-Brazil are raher small. Neverheless, heir sock markes may be linked hrough he impac of world oil and energy demand, which mos likely affecs he Russian and o some exen he Brazilian economy. In general, he US, EU and he BRIC economies are also relaed hrough changes in currency markes which affec heir relaive compeiiveness. In he financial secor foreign exchange volailiy may also induce global porfolio managers o dynamically modify heir invesmen posiions among he six markes 13. One oher reason of he remaining causaliies could be ha speculaive movemens driven by rader fads may be ransmied o and from he US, EU and he BRIC sock markes. Thus, speculaive and noise rading may also lead o conagion effecs across he invesigaed markes. Finally, beyond he conagious effecs of he US crisis on he BRIC equiy markes, he presen sudy explored he so called decoupling phenomenon. I seems ha some evidence in suppor of he decoupling view was found based on he causaliy resuls. Specifically, he assumpion ha he emerging markes can be major drivers of world growh is parially validaed by he deeced feedback linkages. However, decoupling would have been plausible, especially afer he financial crisis period, only if srong unidirecional links were deeced from he BRICs o he US marke and no of he opposie direcion as well. 8. CONCLUSIONS The presen sudy conribues o he lieraure on spillovers among he global financial markes by focusing on he high-growh emerging equiy markes of Brazil, Russia, India, and China - he so-called BRIC economies - ha have hihero received lile aenion. I explores he linkages among he developed US and Eurozone equiy markes and he BRICs, and invesigaes he ransmission of he US subprime crisis o he fases growing economies of he world. The aim of he sudy is o es for he exisence of boh linear and nonlinear causal relaionships among he examined financial markes, wihin he ime period from he inroducion of Euro unil he financial crisis of 2007-2010 and he consequen EU deb crisis. Several ineresing conclusions have emerged from his sudy. In paricular, i was shown ha almos all markes have become more inernaionally inegraed afer he US financial crisis and he consequen Eurozone sovereign deb crisis. Whils he linear causal relaionships deeced on he 13 Alhough in his sudy he sock indices were denominaed relaive he USD o accoun for he exchange risk under he perspecive of he same invesor (Chan e al., 2000), he causaliy resuls in local currencies were no found o be saisically differen. 21

sock reurns have disappeared afer proper filering, nonlinear causal linkages in some cases emerged and more imporanly persised even afer mulivariae GARCH filering boh in he preand pos-financial crisis period. Conagion was furher subsaniaed due he ransmission of he US subprime crisis o he BRIC markes, as well as via rade inerrelaionships mosly during he poscrisis period. In addiion, he leading role of he US marke is shown hroughou all causaliy ess and in all ime periods, whereas he Chinese marke has relaively lile influence on he sock price movemens in he US and EU, paricularly once linear effecs have been removed. US, Chinese, and Indian equiy markes may be inerrelaed hrough invesmen, rade and macroeconomic fundamenals, while he US, Russian and Brazilian sock markes may be linked hrough he energy demand. Finally, beyond he conagious effecs of he US crisis on he BRIC equiy markes, he decoupling phenomenon was also invesigaed. The assumpion ha he BRICs are major drivers of world growh was confirmed by he deeced feedback linkages. However, decoupling was no observed as his would enail unilaeral srong links from he BRICs o he US marke and no of he opposie direcion as well, especially afer he financial crisis period. An ineresing opic for fuure research is he naure and source of he nonlinear causal inerdependencies. I was conjecured ha volailiy effecs migh parly induce nonlinear causaliy. The fied mulivariae condiional correlaion GARCH models accoun for a large par of he nonlineariy, albei no in all cases. Alernaively, oher paramerized GARCH models or srucural models may be employed, he laer of which would incorporae economic facors and macrofundamenals driving he inerdependence of emerging sock markes. Moreover, he probabiliy ha sock reurns may exhibi hird- or higher-momens should no be excluded. Also, he remaining causaliies could be due o ail dependency ha exiss even if volailiy clusering is accouned for. These facors may explain why GARCH filering does no capure all he nonlineariy in sock reurns. The empirical findings could have many implicaions for he efficiency of he BRIC markes. For insance, hese resuls may be useful in fuure research o quanify he process of financial inegraion of hese markes as well as influence heir predicabiliy. The deeced inerdependencies among US, EU and he BRICs could also have imporan implicaions for financial marke regulaions, hedging and rading sraegies. The fac ha here are long-erm links beween hese markes implies ha excess risk-adjused reurns exis. Also he presence of dynamic linear and nonlinear relaionships may be used o achieve gains from inernaional porfolio diversificaion and o reduce sysemaic marke risk. 22

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TABLE 1: DESCRIPTIVE STATISTICS Saisic NYSE DAX BOVESPA RTS BSE SSE PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 Mean 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.002 0.000 0.001 0.001 0.000 0.000 0.000 0.000 Sd. dev. 0.013 0.010 0.018 0.017 0.015 0.020 0.025 0.024 0.029 0.024 0.023 0.027 0.019 0.017 0.023 0.016 0.014 0.021 Variance 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.001 0.000 0.000 0.001 0.000 0.000 0.000 Skewness -0.270 0.007-0.288 0.008-0.099 0.107-0.181-0.070-0.305-0.319-0.280-0.333-0.191-0.521 0.111-0.056 0.537-0.383 Kurosis 9.761 2.674 6.645 4.915 2.357 5.783 5.960 4.170 7.171 8.124 5.049 11.022 6.302 4.154 6.359 4.283 5.287 2.352 JB es 12579.0 * 627.8 * 1920.7 * 3178.9 * 491.0 * 1445.4 * 4691.7 * 1529.8 * 2236.8 * 8739.1 * 2268.3 * 5268.6 * 5244.6 * 1612.2 * 1747.7 * 2414.8 * 2559.0 * 263.6 * Q(12) 36.67 * 15.65 29.24 * 30.76 * 23.92 * 22.35 44.32 * 51.45 * 23.49 * 56.56 * 28.71 * 59.45 * 72.94 * 47.26 * 33.34 * 28.91 * 22.93 * 12.13 ARCH -LM(5) 236.72 * 59.91 * 79.87 * 112.42 * 81.46 * 34.12 * 159.19 * 53.73 * 99.31 * 80.27 * 37.57 * 33.35 * 47.15 * 81.94 * 7.86 * 34.52 * 21.21 * 7.48 * Correlaion marix PToal P1 P2 NYSE DAX BOVESPA RTS BSE SSE NYSE DAX BOVESPA RTS BSE SSE NYSE DAX BOVESPA RTS BSE SSE NYSE - - - DAX 0.596 0.566 0.637 BOVESPA 0.581 0.515 0.435 0.364 0.740 0.712 RTS 0.273 0.386 0.345 0.184 0.238 0.214 0.370 0.584 0.534 BSE 0.228 0.294 0.252 0.312 0.072 0.134 0.118 0.206 0.358 0.476 0.420 0.448 SSE 0.054 0.095 0.106 0.104 0.176 - -0.004 0.002 0.010-0.006 0.043-0.097 0.191 0.215 0.232 0.308 - Noaion: (*) denoes significance a 5% confidence level. The periods are P1: 01/05/1999-02/21/2007, P2: 02/22/2007-02/28/2011 and PToal: 01/05/1999-02/28/2011 26

TABLE 2: UNIT ROOT TESTS Periods PToal P1 P2 ADF PP ADF PP ADF PP Variables ADFc ADFτ PPc PPτ ADFc ADFτ PPc PPτ ADFc ADFτ PPc PPτ NYSE P 0.53 0.71 0.53 0.72 0.95 0.95 0.97 0.97 0.60 0.96 0.62 0.96 r 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * DAX P 0.79 0.67 0.78 0.68 0.96 0.99 0.97 0.99 0.62 0.92 0.63 0.92 r 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * BOVESPA P 0.92 0.57 0.93 0.58 0.99 0.98 0.99 0.99 0.52 0.77 0.53 0.78 r 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * RTS P 0.85 0.76 0.84 0.75 0.99 0.99 0.99 0.98 0.72 0.97 0.74 0.98 r 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * BSE P 0.82 0.54 0.82 0.55 0.99 0.99 0.99 0.99 0.54 0.84 0.55 0.84 r 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * SSE P 0.71 0.82 0.71 0.81 0.99 0.99 0.99 0.99 0.60 0.72 0.59 0.72 r 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * 0.00 * Noaion: Price variables are in logarihms and repored numbers for he augmened Dickey Fuller (ADF) and Phillips-Perron (PP) es are p-values (boh are one-sided ess of he null hypohesis ha he variable has a uni roo). The index c indicaes ha he es allows for a consan, while τ for a consan and a linear rend. The number of lags for he ADF was seleced using he Schwarz informaion crierion. The lag runcaion for he PP es was seleced using Newey and Wes (1994) auomaic selecion wih Barle kernel. (*) denoes significance a 1% confidence level. The periods are P1: 01/05/1999-02/21/2007, P2: 02/22/2007-02/28/2011 and PToal: 01/05/1999-02/28/2011 TABLE 3: COINTEGRATION TESTS Pair Trace saisic Maximum Eigenvalue saisic X NYSE DAX PToal P1 P2 PToal P1 P2 Y r = 0 r 1 r = 0 r 1 r = 0 r 1 r = 0 r 1 r = 0 r 1 r = 0 r 1 BOVESPA 0.95 0.91 0.11 0.27 0.48 0.22 0.92 0.91 0.11 0.27 0.57 0.22 RTS 0.70 0.47 0.50 0.10 0.64 0.28 0.68 0.48 0.81 0.10 0.71 0.28 BSE 0.87 0.43 0.20 0.22 0.58 0.24 0.88 0.43 0.24 0.22 0.67 0.24 SSE 0.59 0.23 0.11 0.52 0.34 0.28 0.68 0.24 0.11 0.52 0.36 0.28 BOVESPA 0.88 0.76 0.27 0.56 0.80 0.20 0.84 0.76 0.22 0.56 0.92 0.20 RTS 0.56 0.32 0.49 0.28 0.17 0.28 0.58 0.32 0.53 0.28 0.17 0.28 BSE 0.42 0.35 0.26 0.86 0.74 0.18 0.42 0.35 0.19 0.86 0.88 0.18 SSE 0.41 0.47 0.11 0.23 0.36 0.16 0.37 0.47 0.11 0.23 0.47 0.16 Noaion: Repored numbers for he race and max. eigenvalue saisics are he MacKinnon-Haug-Michelis (1999) p-values. 27

TABLE 4: TRADING HOURS SEQUENCE OF MARKETS (CALENDAR DATE) Time zone GMT (Greenwich ime) USA (Easern ime) MARKET USA Brazil China India Russia EU (DAX) 14:30-21:00(-1) 15:00-22:00(-1) 9:30-16:00(-1) 10:00-17:00(-1) 1:30-3:30() 4:00-11:00() 7:00-16:00() 9:00-17:30() & 5:00-7:30() 19:30-21:30(-1) & 23:00(-1) - 6:00() 2:15() -11:05() 4:15() -12:35() 23:00(-1) -1:30() Noaion: The rading hours sequence is presened wih he use of Greenwich as well as US Easern ime. The ime index noaion denoes he chronological order from calendar day (-1) o day (). In case of China he marke involves an inraday inermission of one and a half hours. 28

TABLE 5: CAUSALITY RESULTS X Pair Linear Causaliy Nonlinear Causaliy Raw daa Raw daa VAR GARCH-BEKK CCC-GARCH DCC-GARCH Y X Y Y X X Y Y X X Y Y X X Y Y X X Y Y X X Y Y X PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 PToal P1 P2 USA Brazil ** * ** ** ** ** ** ** ** ** ** * ** ** ** ** ** ** * * * * Russia ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** India ** ** ** ** * ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** * ** ** ** ** * ** China ** ** ** ** ** ** ** ** * ** ** * ** EU Brazil ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** * * * * Russia ** * ** ** * ** * ** ** * ** ** * ** ** * ** ** * * * * * * India ** ** ** ** * ** * ** ** * ** ** ** ** * ** ** ** ** * ** * * * China ** ** ** * * Noaion: X Y: rx does no Granger cause ry. Saisical significance represens 5% (*) and 1% (**). The sock indices for each counry are USA: New York Sock Exchange (NYSE); Eurozone (Germany): DAX30; Brazil: Bovespa; Russia: RTS index; India: Bombay Sensex 100; China: Shangai SE Composie. The indices are denominaed relaive o Unied Saes dollar (USD). The periods are P1: 01/05/1999-02/21/2007, P2: 02/22/2007-02/28/2011 and PToal: 01/05/1999-02/28/2011. The synchronizaion issues for he correc esing of he null hypohesis in boh direcions are analyzed in Secion 6. For all pairs he Johansen ess did no idenified any coinegraing vecors and he null of no coinegraion was no rejeced (Table 3). Thus, linear and nonlinear causaliy are invesigaed wih a VAR represenaion. The resuls from he SIC crierion, aking ino consideraion many lag specificaions for he bivariae VAR modelling, indicae in he majoriy of cases four lags for he reurn series in all periods. For he nonlinear causaliy es he common lag lenghs used are l = l = 1. The nonlinear es is applied on he VAR residuals derived from he pairwise linear causaliy esing and he disance measure X Y is se o 1.5 ε=, as suggesed by Hiemsra and Jones (1994). To accoun for he sylized facs of he sock marke reurns, in CCC and DCC represenaions he asymmeric Glosen-Jagannahan-Runkle (1993) GJR-GARCH(1,1) specificaion is used o model he univariae condiional variances. 29