No David Büttner and Bernd Hayo. Determinants of European Stock Market Integration

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1 MAGKS Aachen Segen Marburg Geßen Göngen Kassel Jon Dscusson Paper Seres n Economcs by he Unverses of Aachen Geßen Göngen Kassel Marburg Segen ISSN No Davd Büner and Bernd Hayo Deermnans of European Sock Marke Inegraon Ths paper can be downloaded from hp:// Coordnaon: Bernd Hayo Phlpps-Unversy Marburg Faculy of Busness Admnsraon and Economcs Unversässraße 24, D Marburg Tel: , Fax: , e-mal: hayo@ww.un-marburg.de

2 Deermnans of European Sock Marke Inegraon Davd Büner and Bernd Hayo Phlpps-Unversy Marburg Ths verson: 20 July 2009 Correspondng auhor: Davd Büner Faculy of Busness Admnsraon and Economcs Phlpps-Unversy Marburg Unversässr Marburg Germany Phone: (0) Fax: (0) Emal: buener@ww.un-marburg.de Web: hp:// We hank Gudo Germano for allowng us o use hs advanced compung facles, a prerequse for esmang he models for hs very large sample.

3 Deermnans of European Sock Marke Inegraon Absrac We analyse he deermnans of sock marke negraon among EU member saes for he perod Frs, we apply bvarae DCC-MGARCH models o exrac dynamc condonal correlaons beween European sock markes, whch are hen explaned by neres rae spreads, exchange rae rsk, marke capalsaon, and busness cycle synchronsaon n a pooled OLS model. By groupng he counres no euro area counres, old EU member saes ousde he euro area, and new EU member saes, we also evaluae he mpac of euro nroducon and he European unfcaon process on sock marke negraon. We fnd a sgnfcan rend oward more sock marke negraon, whch s enhanced by he sze of relave and absolue marke capalsaon and hndered by foregn exchange rsk beween old member saes and he euro area. Ineres rae spreads and busness cycle synchronsaon do no appear o play an mporan role n explanng equy marke negraon. Keywords: Sock Marke Inegraon, European Unfcaon, DCC-MGARCH model JEL Codes: E44, F3, F36, G15 1

4 I. Inroducon The negraon of fnancal markes s mporan o boh marke parcpans and polcymakers. In negraed markes, capal flows freely o where wll generae he hghes reurn. Inegraed fnancal markes have easer access o foregn capal, bu are also more vulnerable o fnancal crses occurrng n oher areas of he world. Moreover, any ncrease n he degree of global fnancal marke negraon decreases he opporuny for dversfcaon. I s hus essenal o acheve a beer undersandng of he facors drvng fnancal marke negraon. In hs sudy, we analyse deermnans of sock marke negraon usng daa from European Unon (EU) member saes. Our emprcal ndcaor of he degree of negraon of European equy markes s he (condonal) correlaon of reurns beween hese markes. Takng no accoun ha fnancal negraon s a dynamc process, we allow he esmaed correlaons o vary over me. We use bvarae DCC-MGARCH (dynamc condonal correlaon mulvarae generalzed auoregressve condonal heeroskedascy) 1 models (Engle, 2002) o exrac he condonal correlaons of European sock markes for he perod Followng Engle (2002), we characerse he condonal covarance marx of sock marke reurns r F N( 0, H ) 1 as follows: H D R D, 1 1 where D = dag }, and R dag{ Q } Q dag{ Q }, { h, = Q ( 1 β ) Q + α ( ε 1ε 1 ) + βq 1 = α, r = ε, h F 1 capures all nformaon up o -1, and Q s he marx of uncondonal correlaons. We are neresed n he elemens of he condonal correlaon marx R,.e., he dynamc condonal correlaons beween sock marke reurns: qj, ρ j, = (1) q q j, j, wh,j=1,2 and q j, as elemens of Q. 1 For he unvarae par of he model, we employ a FIGARCH (fraconally negraed GARCH) model (Balle e al., 1996). 2

5 To assess he mpac of polcal facors on fnancal marke negraon, we group he counres based on her European negraon saus no euro area member (EMU), old EU member saes whou he euro (OMS), and new EU member saes (NMS). 2 The esmaed dynamc correlaons of European sock markes are explaned by varables proxyng for he maury of fnancal markes, exchange rae rsk, he degree of busness cycle synchronsaon, and seasonal as well as radng day effecs. A number of auhors sugges (e.g., Frazscher, 2002; Baele e al., 2004; Km e al., 2005) ha he nroducon of he euro promoes fnancal marke negraon by elmnang exchange rae rsks and shor-erm neres rae spreads. We model exchange rae rsk by exracng he condonal volales of exchange rae reurns usng a GARCH model. Shorerm neres rae spreads are compued usng he absolue hree-monh neres rae dfferenals of he respecve counres. 3 To accoun for he curren sae of fnancal marke developmen, we nclude wo measures of he deph and sze of hese markes n our model: he sums of absolue and relave (n erms of GDP) marke capalsaon of he wo markes under consderaon (see Km e al., 2005). 4 Erb e al. (1994) fnd ha he degree of busness cycle synchronsaon has a sgnfcanly posve effec on sock marke negraon. We evaluae he mpac of goods marke negraon on fnancal marke negraon by addng wo (monhly) lags of an ndcaor varable for busness cycle synchronsaon, whch ake he value 1 f he oupu gaps (exraced from monhly ndusral producon) of wo counres show he same sgn and 1 f hey have opposng sgns. 5 The cumulaed effec of busness cycle synchronsaon over (almos) one year s capured by a varable ha consss of he sum of he busness cycle synchronsaon ndcaor from lag 3 unl lag Allowng for possble lags n he processng of nformaon on oupu developmens, we nclude he sum of hs ndcaor varable for up o 12 monhs. We also examne wheher he phase of he busness cycles maers by ncludng dummy varables for booms and recessons. Conrollng for omed rendng facors, we nclude a deermnsc me rend. Fnally, dummy varables capure he fnancal crss 2 Members of he groups are: (1) Euro area: Ausra, Belgum, Fnland, France, Germany, Ireland, Ialy, he Neherlands, Porugal, and Span. Greece s no ncluded, as nroduced he euro only n 2001; nor s Luxemburg, as s markes are small. (2) Old EU member saes ousde he euro area: Denmark, Sweden, and he Uned Kngdom. (3) New EU member saes: he Czech Republc, Hungary, and Poland. These counres were chosen because hey have no ye nroduced he euro, bu have suffcenly large fnancal markes. 3 The daa were rereved from Eurosa and naonal sascal offces/cenral banks. 4 Daa sources: ECB and Eurosa. 5 The daa are from he OECD webpage (MEI orgnal release daa and revsons daabase). To exrac he oupu gaps, we derve he rend usng a Hodrck-Presco fler wh λ =14400 (Hodrck and Presco, 1997). 3

6 followng he errors aacks on 11 Sepember 2001, as well as deermnsc seasonal and radng day effecs. As shown by Orphandes and van Norden (2002), usng revsed oupu daa o esmae reacons of economc agens can lead o severely based esmaes. Thus, he auhors advse employng real-me daa ha reflec he acual sae of nformaon a he me of decson makng. To nvesgae he mporance of hs dsncon n he consrucon of he daa se, we esmae he model usng boh knds of daa for ndusral producon. 7 Usng hese facors, we explan he condonal dynamc correlaons beween equy markes n he varous groups of counres. Esmaon akes place whn one large sacked OLS model wh group-ndvdual regressors and fxed effecs (see Equaon (2)). The advanages of hs model se-up nclude an ncrease n esmaon effcency due o he large number of observaons and he possbly of esng for asymmeres across counry groups usng powerful sandard ess; he dsadvanages nclude he assumpons of equal coeffcens whn he groups and a common error srucure. We esmae Equaon (2): Corr, = Cons 2 r= 0 Recesson, 1 + Ineres Rae Spread,m r, -1 + Relave Marke Capalzaon + + Corr α + Conrol ϖ,m r 2 r= 0,, m-1 2 r= 0 + Cumulaed Busness CycleSyncronsaon ν + γ + Exchange Rae Rsk ξ Boom, κ,m r + u, -1 + Absolue Marke Capalsaon +,m r Busness CycleSyncronsaon μ β, m-1,m-r τ η,m r (2) = EMU/EMU, EMU/OMS, EMU/NMS, OMS/OMS, OMS/NMS, NMS/NMS, where Corr, s he bvarae dynamc sock marke correlaon beween wo counres belongng o he groups EMU, OMS, or NMS a me (see Equaon (1)), Conrol ncludes a deermnsc rend as well as dummes for January, Frday, 11 Sepember 2001, and 12 Sepember m-1, m-2, refers o he prevous 1, 2, monh. All oher varables are self-explanaory. 6 A a prelmnary sage we also vary he specfcaon wh regard o he underlyng szes of he respecve oupu gaps. However, no subsanal changes n esmaon resuls occurred. 7 The correlaon coeffcens beween real-me and revsed oupu gap esmaes are 0.68, 0.62, and 0.75 for EMU, OMS, and NMS, respecvely. 4

7 II. Esmaon Resuls Table 1 provdes an overvew of our resuls for oupu gap esmaes based on real-me daa for ndusral producon. Tess ndcae ha here s no sascal dfference from esmaors obaned wh revsed oupu daa. Thus, conrary o Orphandes and van Norden (2002), we do no fnd he daa dsncon o be mporan. Noe ha he esmaon akes place whn he framework of one large model and he columns n Table 1 refer o he coeffcen assocaed wh he observaons for each parcular counry group whn hs specfcaon. Correlaons are normalsed o 100 for ease of nerpreaon. Table 1: Explanng dynamc condonal correlaons of european sock marke reurns = EMU/EMU EMU/OMS EMU/NMS OMS/OMS OMS/NMS NMS/NMS Cons * * * * * * Corr, * * * * * * Trend * * * * * * January Frday * * * * * * * Exchange rae rsk, * Ineres rae spread, Marke Cap, m-1 (% of GDP) * * * * Marke Cap, m-1 (Bllon ) * * * Boom Boom,m Boom,m Recesson * Recesson,m Recesson,m Busness Cycle Synchronsaon Busness Cycle Synchronsaon,m Busness Cycle Synchronsaon,m Cumulaed Busness Cycle Synchronsaon,m-3 o m No. of observaons Log-Lkelhood Normaly es Heero es AR 1-2 es: 230, ,428 Ch²(2) = 1,099,400* F(165,230230) = 14.0* F(2,230394) = 44.4* Noe: * ndcaes sgnfcance a a 0.5% level. Sock marke correlaons are normalsed o 100. m-1, m-2 refer o values of he prevous 1,2 monh. As he sample s very large (230,520 observaons), makng he sascal ess hghly sensve o volaons of he null hypoheses, we es a a 0.5% level followng Leamer (1983), who recommends adjusng he sgnfcance level nversely wh he sample sze. 5

8 Snce he esmaes exhb evdence of auocorrelaon and heeroscedascy, we apply robus sandard errors based on Andrews (1991). We dscover a sgnfcan deermnsc rend oward greaer sock marke negraon Ths rend n negraon beween old and new member saes, as well as beween euro area parcpans and new members, s sronger han he correlaon beween he new EU member saes hemselves, whch s no sgnfcan. Foregn exchange rsk depresses sock marke negraon among old EU member saes and parcpans n he euro area. The absence of foregn exchange rae rsk n he euro area leads o hgher equy marke negraon. These resuls sugges ha adopon of he euro by hose EU member counres ha are sll ousde he euro area wll foser fnancal marke negraon. In conras, neres rae dfferenals do no play a role. We approxmae he maury and deph of equy markes by ncludng relave and absolue marke capalsaon. As expeced, hese ndcaors exer a posve mpac on marke negraon, mplyng ha he deepenng of fnancal markes (especally n he new member saes) wll resul n enhanced marke correlaons n he fuure. The pon esmaes show ha correlaons are hgher on Frdays and lower durng January bu, n conras o he fndngs of Km e al. (2005), hese seasonal effecs are no sgnfcan. The downurn of fnancal markes followng he errors aacks on 11 Sepember 2001 ghens he correlaon of all sock marke reurns excep whn he euro area. Fnally, busness cycle synchronsaon does no play a major role n explanng sock marke negraon. We fnd ha when euro area and new EU member saes are n a recesson, her muual fnancal marke correlaon decreases. Moreover, he sum of he boom ndcaors decreases he nra-euro area correlaons (Ch² = 10.22*). III. Concluson We analyse he deermnans of sock marke negraon beween EU member saes usng dynamc condonal correlaons esmaed by DCC-MGARCH models. Our ndcaor of fnancal marke negraon s hen analysed by means of a pooled OLS model ha groups EU member counres no hree caegores: euro area members, old EU member counres no parcpang n EMU, and new member saes. We conduc our analyss for boh real-me and revsed oupu daa and dscover no noeworhy dfferences. Regardng he general developmen of European sock marke negraon, we fnd ha for almos all groups of counres here s a sgnfcan rend oward more negraon. However, foregn exchange rsk 6

9 depresses negraon among old EU member saes and for parcpans of he euro area. Therefore, f non-euro area counres adop he euro, an ncrease n sock marke correlaons vs-à-vs he euro area can be expeced. The sze of relave and absolue marke capalsaon also promoes equy marke negraon. If markes deepen, hgher correlaons can be expeced n he fuure, especally for he new member saes, whch are n he process of cachng-up. Busness cycle synchronsaon does no appear o play a major role n deermnng fnancal marke negraon and neher do neres rae spreads. We do no fnd sgnfcan evdence of seasonal or radng day effecs. Thus, nomnal deermnans of sock marke negraon seem o be more mporan o hs process han real deermnans. References Andrews, D. W. K., 1991, Heeroskedascy and Auocorrelaon Conssen Covarance Marx Esmaon, Economerca 59, Baele, L., A. Ferrando, P. Hördahl, E. Krylova, and C. Monne, 2004, Measurng Fnancal Inegraon n he Euro Area, ECB Occasonal Paper Seres No. 14. Balle, R. T., T. Bollerslev, and H. O. Mkkelsen, 1996, Fraconally Inegraed Generalzed Auoregressve Condonal Heeroskedascy, Journal of Economercs 74, Engle, R. F., 2002, Dynamc Condonal Correlaon A Smple Class of Mulvarae GARCH Models, Journal of Busness and Economc Sascs 20, Erb, C., C. R. Harvey, and T. E. Vskana, 1994, Forecasng Inernaonal Equy Correlaons, Fnancal Analyss Journal, November December, Frazscher, M., 2002, Fnancal Marke Inegraon n Europe: On he Effecs of EMU on Sock Markes, Inernaonal Journal of Fnance and Economcs 7, Hodrck, R. J. and E. C. Presco, 1997, Poswar U.S. Busness Cycles: An Emprcal Invesgaon, Journal of Money, Cred, and Bankng 29, Km, S. J., F. Moshran, and E. Wu, 2005, Dynamc Sock Marke Inegraon Drven by he European Moneary Unon: An Emprcal Analyss, Journal of Bankng & Fnance 29, Leamer, E. E., 1983, Model Choce and Specfcaon Analyss, n: Z. Grlches, M. D. Inrlgaor, and P. Schmd, eds., Handbook of Economercs, Vol. 1 (Norh-Holland, Amserdam)

10 Orphandes, A. and S. van Norden, 2002, The Unrelably of Oupu-Gap Esmaes n Real Tme, Revew of Economcs and Sascs 84,