Dynamic Interactive Cycles during the 2008 Financial Crisis

Transcription

1 Modern Economy, 010, 1, 1-16 doi:10.436/me Published Online May 010 (hp:// Dynamic Ineracive Cycles during he 008 Financial Crisis Absrac Ioannis M. Neokosmidis, Vassilis Polimenis Deparmen of Economics, Arisole Universiy of Thessaloniki, Thessaloniki, Greece Received February 16, 010; revised March 0, 010; acceped March 30, 010 This paper focuses on he analysis of he 008 financial crisis and how i affecs he global financial markes. We analyze hree major markes (US, UK, and ASIA) ha are represened by he levels of hree broad sock indices S&P 500, FTSE 100 and Hang Seng respecively. Our mehodology is based on coinegraion analysis and Granger causaliy es in order o examine he ineracion beween he markes (informaion flows). Addiionally, we sudy he volailiy ransmission based on mulivariae GARCH analysis. We find significan changes in informaion flows before and during he financial crisis. Keywords: Uni Roo Tes, Coinegraion, Granger Causaliy, Mulivariae Volailiy Processes, Financial Crisis 1. Inroducion Due o is surprising breadh and inensiy, he analysis of he 008 global financial crisis presens a major challenge for economiss and financial expers. Policymakers now consider he definiion of key policy responses and insiuional rules in order o build mechanisms ha will conain cross-marke conagion and preven a reoccurrence of he problem in he fuure. Mos of he recen crises 1 sared from emerging markes, which are presumably more sensiive o liquidiy shocks because of heir underdeveloped and illiquid financial markes and heir large public deficis. Besides he 1987 crash in Wall Sree ha was echnical and shor-lived in naure, he 008 crisis is he firs o be labelled a US crisis on he basis ha i seems o have sared by he massive US real esae delinquencies. An imporan quesion relaed o an inernaional financial crisis is he exisence of conagion (i.e., he inernaional propagaion of counry- or region-specific shocks o oher pars of he world). According o he more open definiion adoped by Forbes and Rigobon [1], conagion is measured as any change in he ransmission mechanisms ha occurs during a volaile period. For example, conagion may esablish iself by a significan increase in cross-marke correlaions. 1 The mos well known financial and currency crises ha have occurred over he las 5 years wih global consequences were, he 199 European moneary uni problems, he peso effec of 1994, and he 1997 Asian flu crisis (which also riggered he 1998 Russian cold ). The 1999 Brazilian devaluaion, he 000 Inerne bubble burs, and he July 001 defaul of Argenina. Ye o dae, here is sill a lo of disagreemen as o wha are he channels hrough which financial upheaval is ransmied across counries, and on he se of measurable facors ha may be used for he precise idenificaion of a conagion even. Undersanding hese facors is imporan because early recogniion of he possibiliy of conagion may help reduce a counry s vulnerabiliy o exernallyoriginaed shocks. In he wake of he curren financial inernaional crash, growing inegraion of financial markes has been of heighened ineres because such inegraion is assumed o generae large, correlaed price movemens across mos sock markes. Ye due o he complexiy and global naure of he curren financial crisis, i is difficul o move beyond he headlines of he financial press and provide an in deph analysis of he mechanism ha links global financial markes during he crisis and generaes he phenomenon of conagion. The analysis may ake place on boh he economics of he crisis, as well as on a purely saisical manner. On he economic fron, in he US for example, he figh has produced wha is ermed a highly accommodaive moneary policy. Wha is ruly mean by his decepively sof phrase is ha since he onse of he financial crisis nearly wo years ago, he Federal Reserve has reduced he cos of funds for big US banks nearly o zero. This has happened by adjusing he ineres-rae arge for overnigh lending beween banks (he so called Fed-funds rae). Having brough he Fed-funds rae o almos zero, he US (and laer he UK) swiched o he more aggressive policy of Quaniaive Easing, which is also described as

2 I. M. NEOKOSMIDIS ET AL. prining money ou of hin air. This led o an explosion of he size of he US Fed balance shee, mainly hrough he purchase of long-erm securiies, iniially aimed a resaring he flow of credi and o sofen he economic impac of he financial crisis for he US. Such acions were no paralleled elsewhere in Europe or Asia so i is ineresing o undersand he linkage dynamics ha were produced. In he curren paper we employ an inuiive and sraighforward saisical analysis for esing if conagion occurs by simply comparing cross-marke linkages beween markes during a relaively sable period before he urbulen period, wih linkages during he crisis. We examine he shor-run dynamics of reurns and volailiy for socks raded in he US, Briish and Hong Kong sock exchanges during he relaively shor las six year period. The main focus of he sudy is Granger causaliy among he hree markes, which is a saisical concep of causaliy ha is based on predicion. According o Granger causaliy, if a marke Granger-causes (or G-causes ) anoher marke, hen pas reurns of he 1 s marke should conain informaion ha helps predic reurns of he nd above and beyond he informaion conained in pas values of he nd marke alone. We firs find a srongly significan coinegraion coefficien for he index levels before and during he financial crisis period for all marke pairs (US-UK, US-Asia and UK-Asia), which implies a long run equilibrium level of ineracion. We hen proceed o he main finding of he paper: a change in he direcion of he informaion flow during he financial crisis as his is esablished by Granger causaliy. As expeced, due o overlapping operaing hours and he srong ies beween he markes, here is simulaneous ineracion beween he US and UK. Since he Asian markes precede he US wih no overlap, Asian reurns oday ough o include an unrevealed componen also presen in yeserday s US reurns. Ye, before he financial crisis, we can rejec he hypohesis of he US marke causing he Asian markes (a a daily level); his shows a paricularly weak pre-crisis ineracion. Surprisingly, when we es he null ha US G-causes Asia wih a sample which includes he financial crisis, we find ha he US marke includes informaion abou Asia. This provides evidence of a newly produced channel of informaion from he US o Asia and, o our knowledge, he firs saisical verificaion ha he 008 crisis was a crisis ruly made in he US. The opening of his new channel of informaion flow from he US o Asia is a clear indicaion ha during he financial crisis period he abiliy of he US markes o produce, capure and disseminae crisis specific informaion was unmached by he financial markes in oher regions of he world. We finally move o undersand he volailiy ransmission mechanism over ime and across he hree differen This is also relaed o he non-synchronous rading heory of Lo and MacKinlay []. markes during he crisis. Our mehodology is o examine he dynamic relaionship beween he daily sock marke reurns and heir volailiies, for he hree markes above, using a mulivariae generalized auoregressive condiional heeroskedasic (GARCH) model. This is essenially a family of saisical models originally developed by Engle [3-5] and Bollerslev [6,7]. We find ha he markes inerac no only in a reurns level bu o some exend hrough volailiy spillovers. The UK and Asian markes were insignificanly correlaed before and during he crisis. For he US and Asian markes, changing informaion flows due o he crisis, manifesed hrough Granger causaliy for US Asia, is no corroboraed by a change in he significance of he correlaion coefficien. Finally, he US and UK are he only significanly correlaed markes.. Daa Analysis and Descripive Saisics The daase used includes he closing levels of he daily sock marke indices for hree major sock markes (US, UK and Hong Kong). We use he S&P 500 index for he US, he FTSE100 for he UK and he Hang Seng index as a proxy for he Asian markes. Furhermore, we examine he economerics of hese series in wo daa samples. The firs sample, wih daa no conaminaed wih he crisis, runs from April 00 o April 006; i.e., ends before he onse of he financial crisis. The second sample, from April 00 o April 009, includes a leas he firs 18 o 0 monhs of he crisis depending on when one places is beginning. We compue he daily sock reurns for each index as he firs difference of logarihmic levels. Tables 1(a) and (b) repor reurn summary saisics for he wo ime inervals. Table 1(a) includes he ime space before he financial crisis (FC from now on) and Table 1(b) presens he resuls of summary saisics including he period of FC (nd semeser 007-April 009). As we can see from Table 1(a), Asia gives he highes mean reurn while i is characerized by lower volailiy wih posiive skewness and no excess kurosis in compare wih US and UK. US gives he second higher mean reurn wih he second lower volailiy. Addiionally, i is skewed o he righ wih no excess kurosis. The mos risky marke is he UK marke in he ime inerval before he FC, while i seems o give he lower mean reurns wih negaive skewness and excess kurosis. We ge he resuls as hey are shown in Table 1(b), including he ime period of FC in our analysis. The FC gives he opposie side of he coin while ASIA, as i is represened by he Hang Seng index, is shown o be he mos aggressive marke in comparison wih he US and UK. Asia gives he highes mean reurns wih he highes sandard deviaion, while i remained skewed o he righ. US and UK boh exhibi negaive average reurns when he FC period is included in he sample. Finally, all hree markes exhibi excess kurosis.

3 I. M. NEOKOSMIDIS ET AL. 3 Table 1. Reurn summary saisics for he represenaive ime series. (a) Summary saisics for index reurns before FC. ASIA US UK Mean Sandard Deviaion Skewness Kurosis Minimum Maximum (b) Summary saisics for index reurns during FC. ASIA US UK Mean Sandard Deviaion Skewness Kurosis Minimum Maximum Mehodology I is well known ha esing for coinegraion is a means for correcly esing hypoheses concerning he relaionship beween wo indices ha have uni roos. In an effor o firsly deermine if he ime series is covariance saionary we employ he Augmened Dickey-Fuller es [8,9] for a uni roo. We will hen es for coinegraion. Firsly, we employ a uni roo es in order o check for nonsaionariy beween our ime series. We hen es for a significan coinegraion coefficien beween each marke pairs. Moreover, we es for Granger causaliy in each pair of he series in order o invesigae he ineracion flows among he markes before and during he financial crisis ime horizon. Finally we apply a DVEC (1, 1) model and a CCC model in order o capure he volailiy ransmission by examining he changes in he correlaion and covariance coefficiens Tesing for Uni Roos We have o deermine he order of inegraion of sock price series before we es for coinegraion. For his propose, we consider an Augmened Dickey-Fuller (ADF) es for each of our ime series. So, he es procedure is described by he following equaions abou he US, UK and Asian markes: k , i i 1, i1 US a p US US u k 1 1, i i, i1 UK a p UK UK u k , i i 3, i1 HS a p HS HS u wih US represening he log level of he S&P 500 index a ime, UK represening he log FTSE100 and he log level of he Hang Seng composie index being measured in HS 3. I is assumed ha ui, ~ iid o, u i, in all sysem equaions. Finally, i is imporan o noice ha, for he fied error erms u ˆ o be as close as possible o whie noise, we have o selec he correc number of lags based on an informaion crierion such as he AIC [10]. The null hypohesis for he ADF es is ha series are inegraed H : 1 0 p 0 agains he alernaive hypohesis of no inegraion, H : p ess in order o accep or rejec he null hypohesis of a uni roo are performed agains criical values from he DF-disribuion [11] and no from he classical -disribuion. Tables (a) and (b) show he es resuls; he null hypohesis of nonsaionariy canno be rejeced for all he markes and for boh ime horizons. So, all ime series (US, UK, HS ) can be assumed o be I(1) which means ha we should ake he firs difference (i.e., coninuously compounded index reurns) in order o achieve saionariy. 3.. Tesing for Coinegraion We concluded on inegraed of order one I(1) level series in he previous secion. In his secion, we es for coinegraion on each pair of processes in order o deermine he exisence of long-run equilibria. A significan coinegraion coefficien implies a long-run equilibrium relaionship. Then, even hough our daa generaing processes conain uni roo, hey are going o move closely ogeher wih he difference beween hem will be saionary [11]. We employ he Engle and Granger es procedure [1] in order o es for coinegraion: 1s sep: We have o es if our series are I(1). nd sep: We run he regressions beween (US /UK, US /HS, UK /HS ) in boh periods (before and afer FC). Our regression models are: US a a UK u (1) US a a HS u () 3 Clearly hen, ΔUS, ΔUK and ΔHS are he daily reurns of he US, UK and HS indices.

4 4 I. M. NEOKOSMIDIS ET AL. Table (a). Uni roo-10 h lag-es for S&P 500, FTSE 100 and Hang Seng ime series before FC ime period. Uni roo es for S&P 500 ime series before he FC ime horizon. D-lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob Noes: S&P 500 represens he US financial sock marke and he es ime inerval is considered o be from April 00 o April 006. The above resuls were based on PcGive oupu where he selecion crieria are obviously shown. Uni roo es for FTSE100 ime series before he FC ime horizon. D-lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob Noes: FTSE 100 represens he UK financial sock marke and he es ime inerval is considered o be from April 00 o April 006. The above resuls were based on PcGive oupu where he selecion crieria are obviously shown. Uni roo es for Hang Seng ime series before he FC ime horizon. D-lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob Noes: Hang Seng represens he ASIA financial sock marke and he es ime inerval is considered o be from April 00 o April 006. The above resuls were based on PcGive oupu where he selecion crieria are obviously shown.

5 I. M. NEOKOSMIDIS ET AL. 5 Table (b). Uni roo-10 h lag-es for S&P 500, FTSE 100 and Hang Seng ime series including he FC ime period. Uni roo es for FTSE 100 ime series during FC ime horizon. D-lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob Noes: FTSE 100 represens he UK financial sock marke and he es ime inerval is considered o be from April 00 o April 009. The above resuls were based on PcGive oupu where he selecion crieria are obviously shown. Uni roo es for S&P 500 ime series during FC ime horizon. D-lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob Noes: S&P 500 represens he US financial sock marke and he es ime inerval is considered o be from April 00 o April 009. The above resuls were based on PcGive oupu where he selecion crieria are obviously shown. Uni roo es for Hang Seng ime series during FC ime horizon. D-lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob Noes: Hang Seng represens he ASIA financial sock marke and he es ime inerval is considered o be from April 00 o April 009. The above resuls were based on PcGive oupu where he selecion crieria are obviously shown.

6 6 I. M. NEOKOSMIDIS ET AL. UK a a HS u (3) 0 1 3rd sep: We obain he fied errors uˆ from he above regressions and we es if hey include any sochasic rends or no. Then, if we find uni roos in he residuals we conclude ha here is no coinegraion beween he series. So, we apply an ADF es in he fied errors, k uˆ auˆ buˆ (4) 1 i i i1 where he null is: H : a 0 0 agains he alernaive, H : a 0 1 The resuls are shown in Tables 3 and 4, where we rejec he null (no coinegraion) a all lags for all marke pairs. The financial crisis does no affec he exisence of long-run relaionship among he hree markes Informaion Flow Even if he financial crisis did no affec he long-run relaionship of he markes, i may sill have affeced he flow of informaion (he direcion of ineracion) beween hem. As we discussed already, we find coinegraion beween (US/UK), (US/HS) and (UK/HS) markes. Ye, we don know he direcion of informaion flow (direcion of ineracion) beween he markes. As is well known Granger causaliy from X o Y does no indicae causaliy in he proper common use of he erm (i.e., i does no imply ha he Y series is he effec or he resul of X series). Insead, Granger causaliy ruly measures precedence and informaion flow, so ha in our conex here of he recen financial crisis Granger causaliy from a counry X o counry Y implies ha informaion during he crisis flows from X o Y. Alernaively, we may hink of developmens in X preceding developmens in Y. Our aim in his secion is o describe he dynamic ineracion beween he markes and o see he independen movemens before we proceed o volailiy modelling. I is a crucial aspec of a proper analysis of he crisis o analyze he cycle of informaion before we move o he nex level of volailiy analysis. We separae our markes in hree bi-variae VAR processes [13] like following: US 1c1,1US 1c1,UK1v1 (5) UK c,1us 1c,UK1v US 1 c 1,1 US 1 c 1, HS 1 1 HS c,1us 1c,HS1 (6) UK 1c1,1UK 1c1,HS 11 (7) HS c,1uk 1c,HS1 Alernaively, in he absence of Granger causaliy, our series are generaed by an AR(1) process as follows: US a p US u (8) HS a p HS u (9) 1 UK a p UK u (10) We say ha (R i, ) reurn series does no Granger cause (G-cause) he (R j, ) reurn series if and only if he bes linear predicion of R j, given he informaion se { R i,-1, R j,-1 ) does no depend on R i,-1. Following he above modelling, we can es he null hypohesis agains he alernaive: UK does no G-cause US: H0 : c1, 0 H : c 0 1 1, or alernaively, we may say ha under he null hypohesis he residual variances in (5) and (8) above are he same since ΔUK -1 does no have any significance in explaining ΔUS, i.e. H : Ev ( ) Eu ( ) H : Ev ( ) Eu ( ) US does no G-cause UK: H0 : c,1 0 H0 : Ev ( ) Eu ( 3) or H : c 0 H : Ev ( ) Eu ( ) 1,1 1 3 HS does no G-cause US: H0 : c1, 0 H0 : E( 1 ) E( u1 ) or H : c 0 H : E( ) E( u ) 1 1, US does no G-cause HS: H0 : c,1 0 H0 : E( ) E( u) or H : c 0 H : E( ) E( u ) 1,1 1 HS does no G-cause UK: H0 : c1, 0 H0 : E( 1 ) E( u3 ) or H1: c1, 0 H1: E( 1 ) E( u3) UK does no G-cause HS: H0 : c,1 0 H0 : E( ) E( u) or H : c 0 H : E( ) E( u ) 1, Esimaion and Tesing We have already described an assumed regression srucure for our series. We perform maximum likelihood

7 I. M. NEOKOSMIDIS ET AL. 7 Table 3. Regression resuls for he (US/UK), (US/HS) and ( UK/HS) markes before and during FC horizon. Regression resuls beween US and UK markes before FC ime duraion. Regression resuls beween US and ASIA markes ino FC ime duraion. Coefficien Sd.Error -value -prob Par.R a a RSS: ( ), (R ): F( 1, 1007) = 430 [0.000]** Log-likelihood (1478.3); DW: (0.066) Νoes: Τhe regression equaion is US a 0 auk 1 u. US is represened by S&P 500 index daa, and UK is represened by FTSE 100 index daa. Boh samples of daa are referred o ime inerval from April 00 o April 006. Regression resuls beween UK and ASIA markes before FC ime duraion. Coefficien Sd.Error -value -prob Par.R a a RSS: ( ), (R ): F( 1, 993) = 40 [0.000]** Log-likelihood: ( ); DW: (0.0579) Νoes: Τhe regression equaion is UK a a HS u. UK is represened by FTSE 100 index daa, and HS is represened by Hang Seng 0 1 index daa. Boh samples of daa are referred o ime inerval from April 00 o April 006. Regression resuls beween US and ASIA markes before FC ime duraion. Coefficien Sd.Error -value -prob Par.R a a RSS: ( ), ( R ): F( 1, 993) = 7865 [0.000]** Log-likelihood ( ); DW: (0.084) Νoes: Τhe regression equaion is US a is represened by S&P 500 index daa, and HS is represened by Hang Seng 0 a 1 HS u. US index daa. Boh samples of daa are referred o ime inerval from April 00 o April 006. esimaion (MLE), wih MLE esimaors acually idenical o OLS esimaors. The log-likelihood funcion for our models, assuming he iidn(o,σ ) for he residuals, is of he following form j i log L T /log T /log T 1/ 1 ji yi, i aiyi, 1 biyj, 1 (11) i j 1,, 3 where ( y, US, y, HS, y3, UK ) and 1 j i Coefficien Sd.Error -value -prob Par.R a a RSS: ( ), (R ): F( 1,1746) = 4018 [0.000]** Log-likelihood: ( ); DW: (0.035) Νoes: Τhe regression equaion is US a a HS u 0 1. US is represened by S&P 500 index daa, and HS is represened by Hang Seng index daa. Boh samples of daa are referr ed o ime inerval from April 00 o April 009. Regression resuls beween US and UK markes ino FC ime duraion. Coefficien Sd.Error -value -prob Par.R a a RSS: ( ), (R ): F( 1, 176) = 1.496e [0.000]** Log-likelihood (500.08); DW: (0.0881) Νoes: Τhe regression equaion is US a auk u US. is rep- 0 1 resened by S&P 500 index daa, and UK is represened by FTSE 100 index daa. Boh samples of daa are referred o ime inerval from April 00 o April 009. Regression resuls beween UK and ASIA markes ino FC ime duraion. Coefficien Sd.Error -value -prob Par.R a a RSS: ( ), (R ): F( 1, 1746) = 5810 [0.000]** Log-likelihood: (188.19); DW: (0.035) Νoes: Τhe regression equaion is UK a a HS u. UK is 0 1 represened by FTSE 100 index daa, and HS is represened by Hang Seng index daa. Boh samples of da a are referred o ime inerval from April 00 o April 009. is he residual variance in he sysem measuring Granger flow from he j h o he i h counry defined above. We need o esimae he following vecor of parameers: (,,, i i a i b i ji )', i j 1,,3. We can compue he MLE of he above parameer by direcly uilizing he OLS esimaors, which saisfy he following variance equaion ˆ T ˆ ˆ ˆ 1 ji 1/ T yi, iaiyi, 1 b iyj, (1) 1

8 8 I. M. NEOKOSMIDIS ET AL. Table 4. Uni roo-10 h lag-es for obained residuals by (US/UK), (US/HS) and (UK/HS) regression equaions. Uni roo es for obained residuals from US-UK regression series before FC. D-lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob * * * * * ** ** ** ** ** ** Noes: The esi maion period is considered o be he inerval (April 00-Apr il 006). The abov e resuls were based on PcGive oupu where he rejecion crieria are obviously shown. Uni roo es for obained residuals from US-ASIA regression series before FC. D -lag -adf bea Y_1 sigma - DY_lag -prob AIC F-prob ** ** ** ** * * ** ** ** ** ** ** Noes: The esi maion period is considered o be he inerval (April 00-April 006). The abov e resuls were based on PcGive oupu where he rejecion crieria are obviously shown. Uni roo es for obained residuals from UK-ASIA regression series before FC. D -lag -adf bea Y_1 sigma -DY_lag -prob AIC F-prob * ** ** * * * * * ** ** ** Noes: The esimaion period is considered o be he inerval (April 00-Apri l 006). The abov e resuls were based on PcGive oupu where he rejecion crieria are obviously shown.

9 I. M. NEOKOSMIDIS ET AL. 9 Uni roo es for obained residuals from US-UK regression series including FC. D -lag -adf bea Y_1 sigma - DY_lag -prob AIC F-prob ** ** ** ** * * ** ** ** ** ** ** Noes: The esi maion period is considered o be he inerval (April 00-April 009). The abov e resuls were based on PcGive oupu where he rejecion crieria are obviously shown. Uni roo es for obained residuals from US-ASIA regression series including FC. D -lag -adf bea Y_1 sigma - DY_lag -prob AIC F-prob ** ** ** ** * * ** ** ** ** ** ** Noes: The esimaion period is considered o be he inerval (April 00-Apr il 009). The abov e resuls were based on PcGive oupu where he rejecion crieria are obviously shown. Uni r oo es for obained residuals from UK-ASIA regression series including FC. D -lag -adf bea Y_1 sigma - DY_lag -prob AIC F-prob ** ** ** ** * * ** ** ** ** ** ** Noes: The esimaion period is considered o be he inerval (April 00-Apri l 009). The abov e resuls were based on PcGive oupu where he rejecion crieria are obviously shown.

10 10 I. M. NEOKOSMIDIS ET AL. Based on he a bove, we arrive a he follow ing log- expression for our sysem likelihood log ˆ log Lˆ T log T j i T (13) We can suppose he same for he univariae syse m, where our daa are generaed by an AR(1) model. In his case he esimaed variance is given by,, 1 ˆ ˆ ˆ i T yi ai pi yi 1 (1 ) T i 1,, 3 (14) And he corresponding log-likelihood is given by: log L ˆ T log T log ˆ T (15) i Based on Engle [14] he Wald and LR saisics are asympoically equivalen and we ma y hus use any of wo in order o es for causaliy. The likelihood raio and Wald saisics are given respecively as follows: ˆi LR T log ˆ j i (16) (cˆ ) ij W var(ˆ c ) ij (17) Empirical Resuls Table 5 presens he resuls from he Granger causaliy es for all our series before he financial crisis (FC period) and including he financial crisis (FC) reurns. The major finding is ha, as we will explain, informaion flows and precedence of informaion have changed when examining daa before and including he financial crisis. Specifically, using only financial daa from he hree markes ha exclude he financial crisis period, we observe ha he only wo hypoheses ha are accepable (i.e., we canno safely rejec) are ha he US and UK markes canno ransmi informaion o he Hong Kong marke. This finding is very reasonable: wih 8 hours of difference in local ime 4 beween he UK and HS (and 13 hours of difference in local ime 5 beween he US and HS) while evens ha occur when he Hong Kong marke is open will be capured by boh HS reurns as well as UK (US) reurns his is no necessarily rue for informaion released during he UK marke hours. The hypohesis ha: HS marke does no precede he UK has a p-value of and is rejeced. I will acually be impossible for informaion released during he US hours 4 7 hours in he summer monhs due o Dayligh saving ime in he UK bu no in Hong Kong. 5 1 hours in he summer monhs due o Dayligh saving ime in NY (US) bu no in Hong Kong. Table 5. Res uls for G-cause es; Evidence of changing informaion flow s during he Financial Crisis. Granger causaliy es bewe en US and UK mar ke before FC period. Ho: US-UK T W-Saisic Prob. US does no Granger Cause UK UK does no Granger Cause US Granger causaliy es beween US and ASIA marke before FC period. Ho: US-ASIA T W-Saisic Prob. HS does no Granger Cause US E-07 US does no Granger Cause HS Granger causaliy es beween UK and ASIA marke before FC period. Ho: UK-ASIA T W-Saisic Prob. HS does no Granger Cause UK UK does no Granger Cause HS Granger causaliy es beween US and UK marke including FC period. Ho: US-UK T W-Saisic Prob. UK does no Granger Cause US US does no Granger Cause UK E-17 Granger causaliy es beween US and ASIA marke including FC period. Ho: US-ASIA T W-Saisic Prob. HS does no Granger Cause US E- US does no Granger Cause HS Granger causaliy es beween UK and ASIA marke including FC period. Ho: UK-ASIA T W-Saisic Prob. HS does no Granger Cause UK E- UK does no Granger Cause HS of operaion o ge incorporaed in same day reurns for he HS and his is why he hypohesis ha HS marke does no precede he US h as a p-value of only and is hu s srongly r ejeced. While i was very difficul for inf ormaion o flow from he US o Asia before he crisis (p-value of ) i becomes clear ha informaion does flow from he US o HS when we include he FC period (p-value of 0.000). This evidence of a newly produced channel of infor- maion from he US o Asia is quie an amazing finding and, o our knowledge, he firs saisical verificaion

11 I. M. NEOKOSMIDIS ET AL. 11 ha he 008 crisis was ruly made in he US. The dramaic opening of his new channel of informaion flow from he US o Asia is regisered in he precipious drop of he p-value (775 imes lower han he p-value ha excludes he crisis). This drop is a clear indicaion ha during he financial crisis period he abiliy of he US markes o produce, capure and disseminae crisis spe- informaion was unmached by he financial mar- cific kes in oher regions of he world. A he same ime, due o he operaing hours overlap beween he US and he UK, we rejec he null ha US does no insananeously G-cause UK in boh examining periods and we conclude ha here is a simulaneous ineracion beween he wo markes. Moreover, he Asian marke affecs UK marke bu he opposie is no rue. We are going o accep (canno rejec) he null of no G-causaliy from UK o Asia. This means ha while i seems Asia affecs UK, a he same ime i is no affeced by UK. This resul remains significan during he FC horizon. Before FC, he Asian marke did G-cause he US marke bu he opposie flow did no exis in he sense ha he US marke did no G-cause Asian markes. When we include he FC period in he analysis, we find an insananeous ineracion beween he wo markes, which means ha we srongly rejec he null of no Granger cause effec. 4. Volailiy Link beween he Markes In his secion, we finally proceed in analyzing he 008 financial crisis and how i is manifesed by changes in he volailiy dynamics of he hree markes. We have already observed ha he markes are co-inegraed, which means ha price movemens of one marke index are srongly relaed o movemens of he oher marke indices. This inerrelaed naure of financial markes is a key facor in conemporary financial analysis, and i is ofen saisically modelled as a mulivariae GARCH ime series model. Such models conain muliple reurn series of he co-inegraed markes, and heir main purpose is o faciliae he analysis of he variance and co- We use coninuously compounded reurns of he hree variance dynamics among he muliple reurn series. main marke indices under sudy: he US index S&P 500, he UK index FTSE 100 and he Hang Seng index for ASIA. We apply he leading mulivariae GARCH specificaion, he Diagonal VECH 6 model [15] in order o capure mulivariae volailiy dynamics. We model he reurns as a summaion of a consan and an innovaion of he series: r u (18) where r = (r FTSE,, r HS,, r SP, ), μ = (μ FTSE, μ HS, μ SP ), u = (u FTSE,, u HS,, u SP, ). The condiional covariance marix of he innovaion vecor u, given he informaion se 1, is defined as H Covu 1. The (p, q)-lag DVECH for volailiy modeling assumes a ime varying H ha follows dynamics defined by, p i i i j - j i1 j1 H C A u u B H (19) We employ an DVECH (1, 1) model in order o analyze our series. Then we ake he following form of (19): H C A u u BH (0) where q h11,.. H h1, h,., h31, h3, h 33, is he covariance marix and is diagonal elemens consiue he variance componens of (FTSE, HS, SP) while he cross producs are he covariance elemens beween he series. The elemen (h 1, ) expresses he ime varying correlaion beween (HS, FTSE), (h 31, ) expresses he ime varying correlaion beween (SP, FTSE) and he elemen (h 3, ) expresses he ime varying correlaion beween (SP, HS). The marix (C) conains he consan erms and marices (A, B) conain he ARCH and GARCH coefficiens respecively 7. We analyze up o seven years of daily daa in order o capure he dynamic volailiy process of he muliple reurn series before and during he FC period. The resuls are as follow: Figures 1 and show he ime plo of reurns for each series before and including he FC period while Figures 3 and 4 show he esimaed volailiies for coninuously compounded reurns for each index marke. Moreover, Figures 3 and 4 presen he ime-varying covariance of DVEC (1, 1) model for coninuously compounded reurns of he hree index markes. Furhermore, we saw only he condiional variance and covariance wih he above modelling procedure, bu we have no ye a clear view abou he correlaion beween he markes and if hey are affeced by he changing direcion of informaion flows as we described in he previous secion. I is necessary o es he condiional covariance for significance, wih a formal srucure of he correlaion coefficien. This es can be done by using he Consan Correlaion Coefficien (CCC) model [17] ha is based on he following specificaion srucure for condiional covariance: 6 The Diagonal VECH model essenially wries he covariance marix as a se of univariae GARCH models. 7 For more deails see [16].

12 1 I. M. NEOKOSMIDIS ET AL. Table 6. Esimaed coefficiens for mean reurn equaion and DVEC (1, 1) before and during FC. Mean reurn coefficien vecor: (μ) Coefficien Before FC Sd. Error z-saisic Prob. μ fse μ hs μ SP Variance Equaion: H C A u u BH C E E C E E C E E C 4.6E-07.87E C 3.04E E C E E-07 a 11 a a a a a b b b b b b During FC Mean reurn coefficien vecor: ( μ) Coefficien Sd. Error z-saisic Prob. μ fse μ hs μ SP Variance Equaion: H C A u u 1 1 BH 1 C E-06 3.E C E E C E E C 1.6E E C 3.8E E C E E a a a a a a b b b b b b

13 I. M. NEOKOSMIDIS ET AL RETURNS_FTSE RETURNS_FTSE RETURNS_HS.15 RETURNS_HS RETURNS_S&P RETURNS_S&P Figure 1. Coninuously compounded reurns of FTSE 100, S&P 500 and Hang Seng ime series before FC period. Figure. Coninuously compounded reurns of FTSE 100, S&P 500 and Hang Seng ime series during FC period.

14 14 I. M. NEOKOSMIDIS ET AL Var(RETURNS_FTSE 100).0004 Var(RETURNS_HS).0006 Var(RETURNS_S&P 500) Cov(RETURNS_FTSE 100, RETURNS_HS) Cov(RETURNS_FTSE 100, RETURNS_S&P 500) Cov(RETURNS_HS, RETURNS_S&P 500) Figure 3. Condiional variance-covariance represenaion of esimaed reurn series before FC ime period. Var(RETURNS_FTSE 100) Var(RETURNS_HS) Var(RETURNS_S&P 500) Cov(RETURNS_FTSE 100, RETURNS_HS) Cov(RETURNS_FTSE 100, RETURNS_S&P 500) Cov(RETURNS_HS, RETURNS_S&P 500) Figure 4. Condiional variance-covariance represenaion of esimaed reurn series during FC ime period.

15 I. M. NEOKOSMIDIS ET AL. 15 hij, Rij hii, hjj,, i, j FTSE, HS, SP (1) If we combine he above finding wih he G-cause findings, we conclude ha he UK and Asian markes The coefficien (R ij ) is he consan correlaion beween he i h and j h markes while he individual marke remained so during he Financial Crisis. On he oher were insignificanly posiive correlaed (R 1 ) before and specific condiional variance following a one-dimensional hand, we canno observe any insignifican difference in GARCH he correlaion (significan correlaion coefficien) beween he US and UK (R 13 ) markes while he condi- hkk, ck aku 1 bk hkk, 1, k () ional correlaion beween he US and he Asian marke (R 3 ) remains srongly insignifican before and during he Table 7. CCC esimaion before and during he FC. FC period. Thus i seems ha informaion is ransmied in par hrough marke reurns and parly also hrough Before FC volailiy spillovers in he case of US/UK. CCC equaion: h c a u b h, 1, 1 kk k k k kk h R h h ij, ij ii, jj, 5. Conclusions Coefficien Sd. Error z-saisic Prob. C E E A B C 3.97E-07.58E A B C E-07.84E A B R R R Coefficien During FC CCC equaion: h c a u b h, 1 1, kk k k k kk h R h h ij, ij ii, jj, Sd. Error z-saisic Prob. C E E A B C 1.9E E A B C E E A B R R R An empirical objecive of his paper was o examine he exisence and source of he srong iner-marke co-movemens ha are suggesed by financial analyss during he 008 financial crisis. We analyzed levels and sock reurns for hree indices (FTSE100, Hang Seng and S&P500) ha represen hree major financial markes ha consiue a major fracion of he world capializaion. We believe ha hese hree sock markes are represenaive of he European, Asian and US markes respecively. Afer finding ha all hree indices have uni roos and hey are coinegraed, we performed a hos of Granger causaliy ess in order o see he direcion of informaion flows beween he markes before and during he finan- cial crisis. The mos surprising finding is ha while i was very difficul for info rmaion o flow from he US o Asia before he crisis, in formaion did flow f rom he US o Asia when he crisis peri od is included in he sample. This provides he firs evidence of a newly produced channel of informaion and, o our knowledge, is he firs saisical verificaion ha he 008 crisis was ruly made in he US. Moreover, we did no find any significan correlaion coefficien, as i was mo delled by a CCC model, beween US/ASIA and UK/AS IA markes excep am ong he US and UK. 6. References [1] K. J. For bes and R. Rigobon, Co nagion in Lain America: Definiions, Meas uremen, and Policy Implicaions, Economi a, Vol. 1, No., 001, pp [] A. Lo and A. C. Mackinlay, An Econome ric Analysis of Nonsynchronous Trading, Journal of Economerics, Vol. 45, No. 1-, 1990, pp [3] R. Engle, Auoregressive Condiional Heeroskedasici- y wih Esimaes of hevariance of U.K. Inflaion, Eco- nomeric a, Vol. 50, N o. 4, 198, pp [4] R. Engle, Arch Seleced Readings, Oxford Universiy

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