US AND LATIN AMERICAN STOCK MARKET LINKAGES

US AND LATIN AMERICAN STOCK MARKET LINKAGES Abdelmounaim Lahrech * School of Business and Adminisraion Al Akhawayn Universiy Kevin Sylweser Deparmen of Economics So. Illinois Universiy-Carbondale Absrac: This paper examines wheher he Lain American equiy markes of Argenina, Brazil, Chile and Mexico have become more inegraed wih he US equiy marke. We empirically measure inegraion by finding he dynamic condiional correlaion (DCC) beween each marke and ha in he U.S. using a DCC mulivariae GARCH model. We hen rack how hese correlaions evolve over ime using a smooh ransiion model which can no only show when greaer inegraion firs occurs bu also how long i akes hese correlaions o ransiion o heir new levels. Our sample period sreches from December 30 h, 1988 o March 26 h, 2004. Resuls show an increase in he degree of marke inegraion beween hese counries and he U.S. Moreover, we find ha he beginning of rapid inegraion coincides wih he beginning of economic liberalizaion for Argenina and Brazil. For Mexico and Chile we find ha he period of rapid inegraion is wihin he period of increasing bilaeral rade. * Corresponding Auhor: Abdelmounaim Lahrech (P.O.Box 104, Avenue Hassan II, Ifrane 53 000, Morocco, A.Lahrech@aui.ma, and elephone # 21235862320) 1

1. Inroducion: Has any srucural change happened o he degree of comovemen among Norh and Lain American equiy markes? If so, when did he change occur and how long was he ransiion period? Answers o hese quesions are of a grea imporance for invesors and policy makers. For invesors he design of a well-diversified porfolio requires a clear undersanding of how inernaional sock reurns are correlaed and how hese correlaions change over ime. Policy makers are concerned abou correlaions among equiy reurns and how hese correlaions evolve over ime because of heir role in he sabiliy of he financial sysem in he region. I is now well documened ha he poenial gain from inernaional diversificaion has been reduced due o he increase in he degree of comovemen among equiy markes (see for example Taylor and Tonks (1989), Eun and Shim (1989) and Campbell and Hamao (1992)). However, many sudies have shown ha emerging equiy markes appear o provide beer diversificaion opporuniies due o heir low correlaions wih developed equiy markes (see for example Bekaer and Harvey (1995), Harvey (1995) and Korajczyk (1996)). Emerging Lain American equiy markes have became of grea imporance o inernaional invesors, especially o US invesors, since he lae 1980s and during he 1990s as hese counries sared o liberalize heir equiy markes during hese periods. Moreover, he subsanial increase in bilaeral rade beween hese counries and he US during he period from 1992 o 2003 have araced aenion of no only invesors and policy makers bu also of academic researchers due o he impac of inernaional rade on equiy marke correlaions. For example, Johnson and Soenen (2003) find a high percenage of conemporaneous associaion beween he Lain American equiy marke CRS Repor for Congress May 11, 2004 2

and he US marke. Moreover, hey find ha a high share of rade wih he US has a srong posiive impac on equiy marke comovemens. Forbes and Chinn (2004) show ha direc rade flows are he mos imporan deerminans of cross-counry linkages. Chen and Zhang (1997) sudy he relaionship beween bilaeral rade and cross-counry reurn correlaions and find ha counries wih more rade o a region end o have higher reurn correlaions wih ha region. Since Lain America is he fases growing regional rade area wih he US, especially during 1992 o 2003, we would expec a higher degree of comovemen beween he US and Lain American equiy marke reurns during his period. In his sudy we are rying o find ou wheher here has been a srucural change in he bivariae correlaions beween he US and Lain American equiy reurns during he period spanning from 1988 o 2004. Specifically, we will answer he quesions: Has any srucural change happened o he degree of comovemen among Norh and Lain American equiy markes? If so, when did he change occur and how long was he ransiion period? In addiion, having idenified he ransiions in he condiional correlaion series we are invesigaing, our sudy will es wheher hese ransiions coincide wih liberalizaion episodes. Resuls from his es will add o previous sudies ha have quesioned he success of liberalizaion. For example, Bekaer and Harvey (1995) find ha some counries like Mexico and Chile became less inegraed afer he firs wo o hree years of liberalizaion. For his purpose we follow a wo-sep approach. The firs sep applies he dynamic condiional correlaion model (DCC) proposed by Engle (2002) o model he flucuaions of correlaion and volailiy beween each Lain American sock marke wih 3

ha of US over ime. In he second sep a smooh ransiion analysis is applied o he bivariae condiional correlaions esimaed in he firs sep. Smooh ransiion analysis is an approach o modeling deerminisic srucural change in a ime series regression. So our seup allows us no only o endogenously deermine he dae of change, bu also wheher he ransiion o he new regime was abrup or gradual. The remaining paper is divided ino four secions. Secion 2 presens mehodology. Secion 3 describes he daa and presens summary saisics. Secion 4 analyzes he resuls. Secion 5 concludes. 2. Economeric Mehodology: In his par of he paper we follow a wo-sep approach. The firs sep applies he dynamic condiional correlaion model (DCC) proposed by Engle (2002) o model he flucuaions of correlaion and volailiy beween each Lain American sock marke wih ha of US over ime. In he second sep we examine wheher here has been any srucural break. This is achieved by esing for saionariy in correlaions. If a bivariae condiional correlaion is saionary hen a smooh ransiion process is no suggesed, because no ransiion of any sor is apparen. On he oher hand if a bivariae correlaion series is nonsaionary, a smooh ransiion model will be applied. This model will allow us o measure exacly when srucural change occurs and how quickly i occurs. 2-1. Dynamic Condiional Correlaion model: I sar his secion by discussing a number of properies of asse reurn volailiy and correlaion ha are observed empirically. These properies can indicae which echniques are appropriae o model volailiy (which will be done in he firs sep of he 4

mehodology). They can also indicae why a DCC-GARCH model is appropriae o model equiy marke comovemens. For asse reurn volailiy, i is observed ha large (small) changes in reurns in one period end o be followed by large (small) changes in subsequen periods. This is called volailiy clusering which becomes more apparen as he frequency of he daa increases. The GARCH class models have proven o be successful in capuring volailiy clusering. I is also observed ha volailiy of asse reurns ofen reacs differenly o posiive news han o negaive news, and many sudies documen ha negaive shocks on asse prices end o have a larger impac on volailiy han do posiive shocks of he same magniude (see for example, Black (1976), Chrisie (1982) and Campbell and Henschell, (1992)). A number of sudies have concluded ha inernaional correlaions are no consan over ime (see for example, Longin and Solnik (1995), Tse (2000), Engle and Sheppard (2001), Goezmann e al. (2003) and Berben and Jansen (2005)). For example, Goezmann e al. (2003) examine he correlaion srucure of world equiy markes for a period of 150 years and find ha inernaional equiy correlaions change significanly over ime, wih peaks in he lae 19 h Cenury, he Grea Depression, and lae 20 h Cenury. The above properies observed in asse reurn volailiy and correlaions sugges ha a ime varying condiional correlaion model ha allows for asymmeric dynamics in volailiy is needed. For his reason he DCC-GARCH model of Engle (2002) ha was recenly exended by Sheppard (2002) o allow for asymmeric dynamics in correlaion and variance is used. To represen Engle s (2002) DCC model for he purpose of his sudy, le r r, r ] be a 2x1 vecor conaining he equiy marke reurns series where: [ = 1 2 5

r 1 Ω ~ N (0, H ). H { } for i = 1, 2 is he condiional variance-covariance marix h i of he equiy reurns vecor r = [ r, r2 ] and Ω is he informaion se ha includes all 1 informaion up o and including ime. The mulivariae DCC-GARCH srucure can be easily undersood by firs rewriing he condiional variance-covariance marix as: where H = D R D (2) D = diag h, h ) is he 2x2 diagonal marix of ime-varying sandard ( 1 2 deviaions from univariae GARCH models wih h i on he diagonal and R is he ime-varying condiional correlaion marix. The DCC model is designed o allow for wo-sage esimaion of he condiional variance-covariance marix H. In he firs sage he univariae volailiy models for each marke will be esimaed and he bes one will be seleced using he Akaikie Informaion Crierion (AIC) from a class of models ha are capable of capuring he common properies of equiy reurns variance. The models include GARCH of Bollerslev (1986), EGARCH of Nelson (1991) and GJR-GARCH of Glosen e al. (1993). In he second sage marke reurns, ransformed by heir esimaed sandard deviaions resuling from he firs sage, are used o esimae he parameers of he condiional correlaions. So, once he univariae volailiy models for markes are esimaed, he sandardized residuals for each marke ε ri i = are used o esimae he h i dynamics of correlaion. The dynamic condiional correlaion marix R is assumed o vary according o a GARCH-ype process. R (3) * 1 * 1 = Q QQ Q ε (4) = ( 1 a b) Q + a 1ε 1 + bq 1 6

where Q is he uncondiional correlaion marix of he ε s. diag{ } Q q ii, * = is a diagonal marix conaining he square roo of he diagonal elemens of Q = { q ij } and Q is a posiive marix which guaranees ha R is a correlaion marix wih ones * 1 * 1 = Q QQ on he diagonal and every oher elemen less han one in absolue value. The ypical elemen ρ ij of R will be of he form ρ ij = q / ij q ii q jj. a and b are scalar parameers ha capure he effec of previous shocks and previous dynamic correlaions. These parameers are he same for all asses, which means ha all asses reac in he same way o news. As Engle s (2002) model does no allow for asymmeries, Sheppard (2002) modified he evoluion equaion o be: Q = Q A QA B QB G NG) + A ε ε A + B Q B + G n n G (5) ( 1 1 1 1 1 where A11 0 A = 0, A B11 0 B = 22 0, B G11 0 G = 22 0 G22 are 2x2 diagonal marices, I[.] is an indicaor funcion and n = I[ ε < 0] o ε (where o denoes he Hadamard produc, i.e. elemen-by-elemen muliplicaion). The marix N equals E n n ] for = 1,,T. In he [ esimaion procedure Q and N are replaced wih sample analogues T T 1 ε ε = 1 and T T 1 = 1 n n respecively. Four models can be rerieved from model (5) by imposing resricions on he parameer marices A, B and G in equaion (5). (See also Engle (2002) and Cappiello e al. (2006)). 7

Model I: The sandard DCC model. This model is given in equaion (4) by he resricions A 11 = A 22 = a, B 11 = B 22 = b, G = G 0 where a and b are he 11 22 = corresponding parameers in equaion (4). This model assumes ha each asse has he same parameer which means ha all asses reac in he same way o news. Moreover, each asse reacs in he same way o posiive and negaive news. Model II: The generalized symmeric DCC model. This model is given by he resricions A11 A22, B11 B22, G 11 = G22 = 0 and simplifies o: Q = ( Q A QA B QB) + A ε 1ε 1 A + B Q B 1 This equaion assumes ha asses reac differenly o news ( A11 A22, B11 B22 ). However, each asse reacs in he same way o posiive and negaive news ( G = G 0). 11 22 = 2-2. Smooh Transiion modeling: We use smooh ransiion model suggesed by Granger and Terasvira (1993) and Lin and Terasvira (1994) o deermine any srucural change in he condiional correlaion series. This model was applied by Leybourne e al. (1997), Leybourne and Mizen (1999) and more recenly by Chelley-Seeley (2005) and Berben and Jansen (2005). Since equiy marke inegraion is likely o be a gradual process smooh ransiion models are good in measuring marke inegraion since hey allow for a smooh ransiion beween wo correlaion regimes. The smooh ransiion model is applied o bivariae equiy marke dynamic condiional correlaions, which have been derived using he DCC-GARCH 8

model from above. We consider he following logisic smooh ransiion regression model for he condiional correlaion ime series ρ ˆ = α + β ij, ˆρ ij, calculaed above. S ( γ, τ) + ε where ε is a zero mean saionary I(0) process. The smooh ransiion beween he wo correlaion regimes is conrolled by he logisic funcion S (?, ) defined as: 1 S ( γ, τ ) = (1 + exp( γ ( τt ))), γ > 0 where T is he sample size. The parameerτ deermines he iming of he ransiion midpoin which is half of he move from regime one o regime wo. The parameer γ deermines he speed of he ransiion beween he wo correlaion regimes. The change beween he wo correlaion regimes is gradual for small values of? indicaing a gradual movemen oward marke inegraion. However, he change beween he wo correlaion regimes is abrup for large values of?. The model assumes ha condiional correlaions change from one saionary regime wih mean a prior o inegraion o anoher saionary regime wih mean a+ß. If ß>0 he condiional correlaions move upward, whereas if ß<0 he condiional correlaions move downward. Before applying he smooh ransiion we need o es for saionariy of he condiional correlaion series. If he series are nonsaionary a smooh ransiion model may be applied as his indicaes ha he series evolves over ime. However, if he condiional correlaion series are saionary he smooh ransiion canno be applied because no srucural change is apparen. Since he model assumes ha he residuals are saionary, i is imporan o es for saionariy of he residuals afer esimaing he smooh ransiion model. ˆ 2 ρ = α + β + α S ( γ, τ ) + β S ( γ, τ ) + ε We also used smooh ransiion wih rend ij, 1 1 2 bu he one wihou rend gives a beer fi o our condiional correlaion series. 9

3. Daa descripion: Our daa on sock prices consis of he S&P500 Composie index for he U.S. and four Lain American Composie local indices for Argenina, Brazil, Chile and Mexico. We use weekly daa spanning from December 30 h, 1988 hrough March 26 h, 2004. Daa are provided by Emerging Marke Daabase (EMBD). 3-1. Descripive Saisics: Table 3.1 Summary saisics of weekly reurns (defined as he log difference of he price) Argenina Brazil Chile Mexico USA Mean 0.0118 0.0226 0.0034 0.0049 0.0017 Median 0.0068 0.0181 0.0018 0.0065 0.0033 Maximum 0.7056 0.3662 0.1043 0.1750 0.0749 Minimum -0.3618-0.6808-0.0708-0.1771-0.1241 Sd. Dev. 0.0761 0.0813 0.0237 0.0377 0.0217 The summary saisics of he daa are given in Table 3.1. From Table 3.2 we find ha he series for Argenina and Chile are posiively skewed which indicaes a long righ fa ail. Also, we find ha he series for Brazil, Mexico and US are negaively skewed. For all five counries hese series have asymmeric disribuions. The kurosis of each of he series is higher compared o he normal disribuion, which has a kurosis of 3. This means ha he empirical disribuion has more weigh in he ails and is hus lepokuric. Table 3.2: Tes for normaliy Argenina Brazil Chile Mexico USA Skewness 2.4290-0.5407 0.4495-0.2692-0.4967 Kurosis 19.5357 11.6412 4.6194 4.9079 5.9324 Jarque-Bera 9839.24 2512.22 113.64 130.18 317.54 Probabiliy 0.0000 0.0000 0.0000 0.0000 0.0000 10

We can es for normaliy of sock reurns by using he Jarque-Bera (1987) es. Resuls from Table 3.2 show he Jarque-Bera es rejecs he null hypohesis of normaliy for all series a he 5% level. If he normaliy assumpion does no hold also for he sandardized residuals hen we need o esimae he parameers of he GARCH model using Quasi- Maximum Likelihood (QML) insead of Maximum Likelihood (ML) (see Bollerslev and Wooldridge (1992))..8 Sock reurns of Argenina.4.0 -.4 90 92 94 96 98 00 02 Figure 3.1: Weekly sock reurns of Argenina by dae 11

.4 Sock reurns of Brazil.0 -.4 -.8 90 92 94 96 98 00 02 Figure 3.2: Weekly sock reurns of Brazil by dae.12 Sock reurns of Chile.08.04.00 -.04 -.08 90 92 94 96 98 00 02 Figure 3.3: Weekly sock reurns of Chile by dae 12

Sock reurns of Mexico.2.1.0 -.1 -.2 90 92 94 96 98 00 02 Figure 3.4: Weekly sock reurns of Mexico by dae Sock reurns of US.10.05.00 -.05 -.10 -.15 90 92 94 96 98 00 02 Figure 3.5: Weekly sock reurns of US by dae In he figures above he weekly reurns of he sock indices are ploed. We can see ha here is volailiy clusering. 13

Table 3.3: Tes for auocorrelaion of squared reurns Argenina Brazil Chile Mexico USA LjungBox(6) 277.50 103.62 115.66 31.98 62.88 Probabiliy 0.0000 0.0000 0.0000 0.0000 0.0000 Noes: Table 3.3 shows Ljung-Box for up o 6 auocorrelaion lags The Ljung-Box auocorrelaion es on he squared reurns shows ha series exhibi significan auocorrelaion a he 1% level. This second order dependence of squared reurns can be capured by a GARCH process. Table 3.4: Uncondiional correlaions Argenina Brazil Chile Mexico USA Argenina 1 Brazil 0.2162 1 Chile 0.2223 0.3128 1 Mexico 0.2945 0.2758 0.2516 1 USA 0.1792 0.2223 0.2273 0.4682 1 Table 3.4 gives he uncondiional correlaions beween he five sock reurns. We see ha Mexico has he highes correlaion wih he US. This is probably due o he high rade share beween he wo counries. All hese Lain American sock reurns have posiive correlaion wih he US sock reurn. 4. Empirical resuls: 4-1. Correlaion Dynamics: This secion presens he empirical resuls of DCC models. In he firs sep he univariae GARCH model for each marke is fied and he bes one seleced using Akaikie Informaion Crieria. Table 3.5 conains he specificaion of he GARCH process seleced by he AIC and he esimaed parameers from hese models. AIC informaion crieria shows ha he equiy marke reurns of Argenina, Brazil and Chile follow a GARCH(1,1) model which means here is no asymmeric effec in hese markes. The 14

equiy marke reurn of Mexico follows a GJR-GARCH (1,1) and he equiy marke reurn of U.S. follows EGARCH (1,1). We can see ha he US and Mexican marke reurns conain significan asymmery erms. For he US marke reurn he asymmery erm is highly significan (1% level of significance). The Mexican marke reurn is significan a he 5 % level. Table 3.5: Univariae GARCH (1,1) models Model Seleced? a? ß Argenina GARCH 0.000152*** 0.2870*** 0.7181*** Brazil CARCH 6.35 e-05 0.1166*** 0.8813*** Chile GARCH 1.60 e-05* 0.1105*** 0.8616*** Mexico GJR-GARCH 6.45 e-05*** 0.0515** 0.0874** 0.8594*** USA EGARCH -0.5597*** 0.2096*** -0.1006*** 0.9492*** Noes: *, ** and *** indicae a significan a he 10, 5 and 1% levels, respecively. ε 1 ε 1 EGARCH model: log( h ) = ω + α + γ + β log( h 1 ) h h 1 2 GARCH model: h = + + ω αε 1 βh 1 2 2 GJR-GARCH model: h = ω + αε 1 + γ[ ε 1 < 0] ε 1 + βh 1 The ess of significance are compued wih he robus sandard errors of Bollerslev and Wooldridge (1992). 1 Table 3.6: Normaliy es for sandardized residuals Argenina Brazil Chile Mexico USA Skewness 0.3898-0.7227 0.3527-0.2375-0.4635 Kurosis 6.8054 6.01634 3.9148 3.7341 4.3699 Jarque-Bera 499.8191 370.5929 44.2023 25.3263 90.6334 Probabiliy <0.00001 <0.00001 <0.00001 <0.00001 <0.00001 The sandardized residuals are sill no normally disribued. Therefore, we mus use Quasi-maximum likelihood and he corresponding sandard errors are calculaed. Using he sandardized residuals from he firs sep, we coninue wih he second sep of he 15

esimaion procedures for DCC models. Models I and II are esimaed for he dynamics of condiional correlaion among he US and he Lain American local indices reurns. The esimaion resuls of all he models are given in Table 3.7: Table 3.7: DCC-GARCH Models Model I a b LLF 0.0125*** 0.9543*** 7944.1 Model II a b LLF Argenina 0.0082*** 0.9708*** 7944.8 Brazil 0.0464*** 0.9450*** Chile 0.0114*** 0.9875*** Mexico 0.0024*** 0.9780*** USA 0.0236*** 0.9637*** Noes: *, ** and *** indicae a significan a he 10, 5 and 1% levels, respecively. Two differen models were esimaed for he dynamics of he correlaions. Model I was esimaed allowing for no asymmeries in he correlaion dynamics. In addiion, each of he marices, A and B, are diagonal wih he same value on each diagonal. Model II was esimaed allowing for no asymmeries in he correlaion dynamics. In addiion, each of he marices, A and B, are diagonal wih differen values for each diagonal elemen. Resuls in Table 3.7 show ha Model II slighly ouperforms Model I since i has a higher log likelihood value. 4-2. Has any change happened o he correlaions? In order o answer his quesion we firs need o plo all he condiional correlaions ha were esimaed using he DCC model. An eyeball view of he graphs below clearly shows an increase in he average level of he condiional correlaions, which is an indicaion ha he level of inegraion beween he US equiy marke and ha of Argenina, Brazil, Chile and Mexico has increased. 16

.5 Condiional correlaion.4.3.2.1.0 90 92 94 96 98 00 02 Figure 3.6: Condiional correlaion beween US and Argeninean equiy reurns.5 Condiional correlaion.4.3.2.1 90 92 94 96 98 00 02 Figure 3.7: Condiional correlaion beween US and Brazilian equiy reurns 17

.32 Condiional correlaion.28.24.20.16 90 92 94 96 98 00 02 Figure 3.8: Condiional correlaion beween US and Chilean equiy reurns.6 Condiional correlaion.5.4.3.2 90 92 94 96 98 00 02 Figure 3.9: Condiional correlaion beween US and Mexican equiy reurns 18

Table 3.8 conains he compued ADF ess for condiional correlaions beween US and each of he Lain American markes. All he bivariae condiional correlaions are found o be non-saionary a he 10% level. These ADF ess provide some informaion abou bilaeral inegraion. The non-saionariy of hese condiional correlaions means ha he degree of bilaeral co-movemen beween he US equiy marke and each of he Lain American equiy markes may have changed. Table 3.8: Compued augmened Dickey-Fuller saisics: prior o he fiing of he smooh ransiion model. Correlaions in levels Correlaions in firs differences Argenina-USA -3.132-29.18** Brazil-USA -2.805-14.01** Chile-USA -2.560-31.11** Mexico-USA -3.231-21.39** Noes: The ADF saisics have been compued wih a consan and a rend. The opimal lag lengh is seleced by Akaike informaion crierion. Significance a a 1% and 5% level is denoed by ** and * respecively. From Table 3.8 we conclude ha all he condiional correlaions are nonsaionary in levels and saionary in he firs differences, which means ha he series are inegraed of order one. Table 3.9: Summary saisics of he bivariae condiional correlaions Mean Min Max Sd Argenina 0.2812 0.1022 0.4869 0.093 Brazil 0.3051 0.1435 0.4927 0.083 Chile 0.2231 0.1638 0.2821 0.031 Mexico 0.4280 0.2223 0.5813 0.078 In Table 3.9 we have compued he mean of he bivariae condiional correlaions beween he US and each of he respecive Lain American markes as his will give us which marke is highly inegraed wih he US one. On average Mexico has he highes condiional correlaion wih he US, approximaely 43%, followed by Brazil a 30%, Argenina a 28% and Chile a 22%. This indicaes ha Mexico is highly inegraed wih 19

he US compared o he oher Lain American equiy markes. This is no surprising since Mexico has engaged in a free rade agreemen wih he US since 1994. 4-3. When did he change occur? Since we find ha all he bivariae condiional correlaions are non-saionary, we esimae he smooh ransiion model for all hese series. Table 3.10 gives he resuls of he esimaed smooh ransiion model. a and a+ß are he correlaions in he old and new regime, respecively. If ß is greaer han zero, here will be an upward movemen in he correlaions. However, if ß is less han zero here will be a downward movemen in he correlaions.? deermines he shape of he ransiion curve, while T deermines he middle of he ransiion period. The change beween correlaion regimes is abrup for large values of?. Table 3.10: The esimaed smooh ransiion model a ß? Adjused R 2 Argenina 0.18331 0.16402 7.03185 0.399 (46.03) (32.60) (8.76) (53.50) 0.6177 Brazil 0.22519 0.16276 6.21574 0.509 (96.18) (48.16) (12.92) (93.81) 0.7818 Chile 0.20088 0.05114 35.29345 0.566 (217.91) (36.20) (3.52) (185.55) 0.6320 Mexico 0.36864 0.14938 5.93267 0.6027 (160.96) (36.85) (11.18) (81.16) 0.6921 Noe: -saisics are given in brackes The resuls from he esimaion of he smooh ransiion model sugges an increase in marke inegraion beween he US and Lain American counries (Argenina, Brazil, Chile, Mexico) as ß > 0 for all hese counries. Since? is larges for Chile, he ransiion owards inegraion wih he US is faser han ha for Argenina, Brazil and Mexico. There is lile difference beween he ransiion midpoins of hese counries. In he case 20

of Argenina i is approximaely in 01/1995, for Brazil i is approximaely in 09/1996, for Chile i is approximaely 07/1997, and for Mexico i is approximaely 02/1998. The highes R 2 is for Brazil (78.18 %) suggesing ha for his counry he smooh ransiion model explains a greaer proporion of he variaion in condiional correlaions han for any oher counry. The R 2 is approximaely 62% for Argenina, 63% for Chile and 69% for Mexico. The correlaion beween Argenina and he US increased from 0.1833 o 0.3473. The ransiion phase covers he period from 10/1989 o 11/1999. The beginning of he ransiion phase coincides wih he beginning of he liberalizaion dae 1989. The correlaion beween Brazil and he US increased from 0.2252 o 0.3879. The ransiion phase covers he period from 3/1991 o 1/2002. The beginning of he ransiion phase coincides wih he liberalizaion dae for Brazil which is 1991. The correlaion beween Chile and he US increased slighly from 0.2009 o 0.2520. The ransiion phase covers he period from 11/1996 o 6/1998. The beginning of ransiion phase does no coincide wih he beginning of liberalizaion dae 1992, bu he ransiion period is wihin he high bilaeral period 1992-2003. Finally, he correlaion beween Mexico and he US increased from 0.3686 o 0.5180. The ransiion phase covers he period from 1/1991 o 11/2003. The beginning of he ransiion period does no coincide wih he beginning of he liberalizaion dae 1989, bu mos of he ransiion period falls wihin he high bilaeral rade period 1992-2003. The dae of he beginning of each liberalizaion episodes is obained from BeKaer, Harvey and Lundblad (2001, Table 1). 21

Figure 3.10: Plos he fied series and DCC correlaion beween US and Argenina. Figure 3.11: Plos he fied series and DCC correlaion beween US and Brazil. 22

Figure 3.12: Plos he fied series and DCC correlaion beween US and Chile. Figure 3.13: Plos he fied series and DCC correlaion beween US and Mexico. 23

5. Conclusion: The main objecive of his paper is o examine wheher he Lain American equiy markes of Argenina, Brazil, Chile and Mexico have become more inegraed wih he US equiy marke. We have used several mehods including DCC mulivariae GARCH and a smooh ransiion model. Resuls show an increase in he degree of marke inegraion beween hese counries and he Unied Saes. Moreover, we find ha he beginning of rapid inegraion coincides wih he beginning of liberalizaion for Argenina and Brazil. For Mexico and Chile we find ha he period of rapid inegraion is wihin he period of increasing bilaeral rade. The implicaion of our sudy for invesors is ha opimal porfolios have changed as a resul of he correlaion shifs. Excep for Chile he condiional correlaions beween Unied Saes and oher Lain American equiy reurns have significanly increased which may lessen he advanages of porfolio diversificaion beween he US and hese counries. Alhough Chile has he lowes correlaion wih he Unied Saes, i has he highes? which means he degree of inegraion is moving faser han ha of any oher Lain American equiy marke. For policy makers, an increase in he level of correlaions beween US and hese Lain American equiy markes means ha equiy marke disurbances in US are more likely o be ransmied o hese counries, which may have adverse consequences for he sabiliy of he financial sysem. One exension of his paper is o invesigae he economic facors behind he shif in he correlaions and see wheher here are some differences beween hese Lain American counries. 24

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