Time Varying Correlations between Stock and Bond Returns: Empirical Evidence from Russia

Transcription

1 Asian Journal of Finance & Accouning Time Varying Correlaions beween Sock and Bond Reurns: Empirical Evidence from Russia Kashif Saleem Lappeenrana School of Business, Lappeenrana Univerisy of Technology, Finland Received: Sepember 3, 011 Acceped: November 1, 011 Published: December 1, 011 doi:10.596/ajfa.v3i1.989 URL: hp://dx.doi.org/10.596/ajfa.v3i1.989 Absrac The purpose of his sudy is o look a he relaionship beween he sock and he bond marke of Russia. By using mulivariae condiional volailiy models, such as, Bollerslev (1990) CCC model, Engle (00) he DCC model, we firs examine wheher he correlaions beween wo classes of asses are consan or ime varying. Secondly, o invesigae he asymmeries in condiional variances, covariances, and correlaions, an asymmeric version of he DCC model proposed by Cappiello e al. (006) is adoped. The empirical resuls do no suppor he assumpion of consan condiional correlaion and here was clear evidence of ime varying correlaions beween he Russian socks and bond marke. Boh asse markes exhibi posiive asymmeries. Keywords: DCC-GARCH, Time varying correlaions, Russia, Asymmeric, Emerging marke JEL classificaion: C3, G15 7

2 Asian Journal of Finance & Accouning 1. Inroducion Since he seminal work by Markowiz (195, 1959) clearly addresses he imporance of sock bond correlaion in consrucing he opimal porfolio, examinaion of he co-movemens beween he sock and bond markes has been one of he mos fundamenal quesions o porfolio managers, risk analyss and financial researchers, among ohers, in recen pas. However, he quesion is sill open and here is no general consensus among financial researchers on he dynamics of he sock bond correlaion and how i migh perform in he fuure. For insance, Keim and Sambaugh (1986), Campbell and Ammer (1993), and Kwan (1996) empirically suppor he heoreical argumen of posiive correlaion among socks and bonds. On he oher hand Gulko (00), Connolly e al. (005) and Baur and Lucey (009) suppor he phenomenon of fligh o qualiy and fligh from qualiy which reflecs a negaive correlaion beween he wo asses, and addiionally, Alexander e al. (000) found mixed sign correlaions. Moreover, prior lieraure is divided ino wo disinc opinions regarding he co-movemen of wo asses; for example, Shiller and Belrai (199) and Campbell and Ammer (1993) are among hose who implicily assume ha sock bond correlaion is ime invarian. In conras, Scruggs and Glabadanidis (003) srongly rejec models ha impose a consan correlaion resricion on he covariance marix beween sock and bond reurns. Furhermore, Siegel (1998), Gulko (00), Cappiello, Engle and Sheppard (006), Ilmanen (003), Connolly e al. (005), Jones and Wilson (004) and Li (00) are among hose who have shown ha he correlaion beween sock and bond reurns exhibis considerable ime variaion, whereas Barsky (1989) is of he view ha sock and bond co-movemens are sae dependen. Furhermore, despie is imporance, his phenomenon has been severely ignored in he conex of emerging markes, regardless of heir high reurns and favourable diversificaion opporuniies. There is no doub ha fuures, opions and differen kinds of derivaive producs have acquired an ever increasing imporance in oday s modern finance. However, socks and bonds are sill he primary securiies raded on sock exchanges and he major componen of any opimal porfolio, especially in emerging markes. Since he risk reurn characerisics of socks and bonds are very differen, sock bond correlaion plays an imporan role in asse allocaion, porfolio managemen and risk managemen. I is herefore a naural quesion o ask wheher here is a relaionship beween he reurns on socks and bonds, considering he main objecive of a porfolio manger, i.e., o consruc a porfolio ha has he larges expeced reurn wih a minimum risk. Moreover, he above-menioned conradicory empirics moivae us o explore his issue furher and especially in he conex of emerging markes. There are several echniques o model he correlaions beween he reurns on he sock and bond markes, bu he convenional mehod relies on a simple regression analysis or akes an uncondiional correlaion based on a specific sample period, such as rolling window correlaion esimaion. However, he pas wo and a half decades have winessed a high developmen in ime series analysis, especially afer he seminal works of Engle (198) and Bollerslev (1986). Mulivariae GARCH models have been exensively applied o invesigae 73

3 Asian Journal of Finance & Accouning he co-movemens beween differen asse markes. However, sudies dealing wih he inerdependence across he sock and bond markes are scarce, paricularly wihin he conex of emerging markes. In he early days, Bollerslev e al. (1988), among ohers, proposed a VECH model o inspec he correlaion beween he bond and sock marke in he USA. Laer, he BEKK model of Engle and Kroner (1995) became popular o es he linkage beween differen markes and i has also been applied o examine he relaionship beween he sock and bond markes (see, e.g., Scruggs and Glabadanidis, 003). Similarly, he Consan Correlaion Coefficien model of Bollerslev (1990) has been applied o invesigae he linkage beween he sock and bond markes (see, e.g., Abid e al., 003) The mos recen addiion in he class of Mulivariae GARCH models is he Dynamic Condiional Correlaion (DCC) model of Engle (00). This model has a clear advanage over previous models as i avoids compuaional complexiies, esimaes large condiional variance covariance marices and also circumvens he convergence problems. Moreover, he DCC model perfecly overcomes he heeroskedasiciy problem since he residuals of he reurns are sandardized by he condiional sandard deviaion based on he GARCH (1, 1) process. However, i does no accoun for he asymmeries in condiional variances, covariances, and correlaions, bu hanks o Cappiello e al. (006) his oversigh was correced because hey recenly proposed an Asymmeric version of he Dynamic Condiional Correlaion (ADCC) model o deal wih he asymmeries in he condiional variances, covariances, and correlaions of wo asses. Over he pas few years he Russian equiy and fixed income markes have shown a remendous aracion o boh domesic and inernaional invesors due o heir rapid growh and correcive measures aken by he Russian policy makers. Alhough he Russian marke is considered risky (see, e.g., Saleem and Vaihekoski, 008), i is also a fac ha oday Russia is a mainsream marke for inernaional invesors who are ineresed o diversify heir porfolios geographically. Therefore, he Russian equiy marke is worh invesigaion. In his sudy he aemp is o model he correlaions beween he reurns on he sock and bond markes of Russia. Firsly, we quesion wheher he co-movemen beween he wo asse classes is consan over ime by uilizing Bollerslev s (1990) Consan Condiional Correlaion model. Furher, o analyze he dynamics of he ime varying condiional correlaions beween he wo asses we use he DCC-GARCH (1, 1) model proposed by Engle (00). Finally, o invesigae he asymmeries in condiional variances, covariances, and correlaions, we adop an asymmeric version of he Dynamic Condiional Correlaion (ADCC) model proposed by Cappiello e al. (006). The empirical resuls do no suppor he assumpion of consan condiional correlaion and here was clear evidence of ime varying correlaions beween he Russian socks and bond marke. Moreover, boh asse markes exhibi posiive asymmeries. In general, he resuls offer a beer undersanding of he dynamics of he correlaions beween socks and bonds in an emerging marke seing which is obviously very valuable for porfolio managers, inernaional invesors, risk analyss and financial researchers as well as for is policy implicaions. The srucure of he paper is as follows. The nex secion describes he model specificaions 74

4 Asian Journal of Finance & Accouning used o sudy he correlaions among Russian sock and bond markes. Secion 3 presens he daa in his sudy. Secion 4 shows he empirical resuls, and Secion 5 provides conclusions.. Model Specificaion Various approaches have been suggesed for he modeling of he correlaions beween wo asses. The simples one is he rolling window correlaion. However, due o is fixed window and equal weighs given o all he sample poins in a daa se, i may ignore he srucural changes wih differen degrees of volailiy in a ime series. Bollerslev e al. (1988), among ohers, proposed a model in he early days o check he condiional covariances beween bills, bonds, and socks; however, he model was no able o assure he posiive definieness of he condiional variance marix. Moreover, his approach does no allow he cross equaion condiional variances and covariances o affec each oher due o oversimplifying resricions. Many of hese problems are circumvened by he Consan Condiional Correlaion (CCC) model proposed by Bollerslev (1990). However, he assumpion of consan correlaion is perhaps relaively uncerain and may no hold always. For example, prior research has documened high correlaions among financial markes during crisis periods (see, e.g., Chesnay and Jondeau, 001). Following Bollerslev (1990), Engle and Sheppard (001) and Engle (00), his empirical specificaion sars wih he assumpion ha sock marke reurns from he k series are mulivariae and normally disribued wih zero mean and condiional variance covariance marix H. Hence, his mulivariae DCC-GARCH model can be presened as follows: r (1) wih N(0, H ) where, r is he (k 1) vecor of he reurns; ε is a (k 1) vecor of 1 zero mean reurn innovaions condiional on he informaion, 1, available a ime -1and for he bi-variae case, he condiional variance covariance marix (H ) in he DCC model can be expressed as: H D R D, () Here D represens a (k k) diagonal marix of he condiional volailiy of he reurns on each asse in he sample and R is he (k k) condiional correlaion marix. Basically, he DCC-GARCH model esimaes condiional volailiies and correlaions in wo seps. In he firs sep he mean equaion of each asse in he sample, nesed in a univariae GARCH model of is condiional variance is esimaed. Hence, we can define D as follows: 1/ 1/ D diag( h... h ), (3) where h ii, condiional variance of each asse is assumed o follow a univariae GARCH(p, q) process given by he following expression: ii kk 75

5 Asian Journal of Finance & Accouning h pi Qi i, 1 i i, p i, 1 p p1 q1 h, (4) i, q i, p However, o insure non-negaiviy and saionariy, some resricions, such as, i, p > 0, i, q > pi 0 and i, p1 p qi + i, q1 q 1 should be imposed. These univariae variance esimaes are hen used o sandardize he zero mean reurn innovaions for each asse. In he second sep, he sandardized zero mean reurn innovaions are assumed o follow a mulivariae GARCH (m, n) process o illusrae he developmen of he ime varying correlaion marix, R, which can be described as follows: 1/ 1/ R ( diagq ) Q ( diagq ), (5) where Q ( 1 ) Q 1 ' 1 Q 1 refers o a (k k) symmeric posiive definie marix and i i / hii, Q is he (k k) uncondiional variance marix of i, and α and β are non-negaive scalar parameers saisfying α + β < 1. Finally, he condiional correlaion coefficien equaion: ij beween wo asses i and j is hen expressed by he following (1 ) qij i, 1 j, 1 qij, 1 ij, (6) 1/ 1/ (1 ) q q (1 ) q q ii i, 1 ii, 1 As per Engle and Sheppard (001) and Engle (00) his model can be esimaed wih he quasi-maximum likelihood mehod (QMLE) given below: 1 T 1 jj j, 1 jj, 1 L ( nlog( ) log D log R ' R ), (7) 1 Since / h D 1 he log-likelihood funcion can be rewrien as follows: 1 T L ( nlog( ) log D R D ' D R D ), (8) 1 As he DCC model does no allow for asymmeries and asse specific news impac parameer, he modified model of Cappiello e al. (006) for incorporaing he asymmerical effec and asse specific news impac can be wrien as: 76

6 Asian Journal of Finance & Accouning Q Q AQA BQB GNG) A A BQ B Gn n, (9) ( G where A, B and G are diagonal parameer marixes, n = I[ε < 0]o ε (wih o indicaing Hadamard produc), N E n n. For Q and N expecaions are infeasible and are replaced wih sample analogues, T 1 T 1 T and T 1 n n 1, respecively. 3. Daa and Descripive Saisics Daily oal reurn indices for he Russian marke calculaed by DaaSream are used as proxy for Russian socks 1. The JP Morgan EMBI Russia index (proxy for Russian bond marke) is used o model he key facors influencing movemens in he Russian bond marke. The daase sars from July 1994 and ends a December 007, yielding 35 observaions for each series. The beginning of his daa se is due o he availabiliy of he oal reurn index for Russia, and he use of daily daa (over a five-day period) in his sudy is o ge meaningful saisical generalizaions and o obain a beer picure of he movemens of sock bond reurns. Daily reurns are consruced as he firs difference of logarihmic prices muliplied by 100. Table 1 presens a wide range of descripive saisics for boh of he series under invesigaion. As a firs sep, saionariy in he ime series is checked by applying he Augmened Dickey Fuller (ADF) es. The resuls (see Table 1) allow us o rejec he null hypohesis ha reurns have a uni roo in favor of he alernae hypohesis of saionariy (even a 1% MacKinnon criical value). The developmen of boh asse indices is shown in Figure 1. This clearly exhibis non-saionariy in boh reurn indices RUSSIA-DS JPM EMBI Jul-94 Jul-96 Jul-98 Jul-00 Jul-0 Jul-04 Jul Figure 1. Developmen of Russian equiy marke indices (lef axis) and he JPM EMBI Russia index (righ axis) from 1994 o Noe ha he oal reurn index series is an index series and no a reurn series. 77

7 Asian Journal of Finance & Accouning The firs wo momens of he daa, i.e., mean and sandard deviaion, are muliplied by 40 and he square roo of 40 o show hem in annual erms. As can be anicipaed, sock marke indices have higher reurns as compared o he bond marke, however, he high reurns are clearly associaed wih high risks (sandard deviaions). Boh he reurn series are, wihou excepion, highly lepokuric and exhibi srong skewness. This suggess he presence of asymmery in he reurn series of boh socks and bonds. To check he null hypohesis of normal disribuion he Jarque-Bera es saisic was calculaed which rejeced he null of he normaliy in boh cases. Table 1. Summary of descripive saisics for he Russian sock and bond marke. Mean Sd. dev. Skewness Kurosis JB ADF LB (4) LB (4) ARCH- LM Sock <0.001* * 5.831* * * Bond * * 8.490* * 6.439* * indicaes significance a 5% level. Descripive saisics are provided for Russian sock and bond marke reurns. Socks marke reurns are proxied by logarihmic reurns on Daasream daily oal reurn index for Russia. Bond marke reurns are proxied by logarihmic reurns on JP. Morgan EMBI Russia daily index. Sample period is from July 1994 o December 007. Mean and sandard deviaions are annualized by muliplying hem by 40 and he square roo of 40. JB sands for Jarque-Bera es on normaliy of he reurns. P-value is repored. ADF sands for Augmened Dickey Fuller es o check he saionariy in he ime series. LB sands for Ljung-Box es saisic. Since he GARCH process o model he variance in he asse reurns was used, he presence of he ARCH effec was also esed for. Table 1 repors values for he Ljung-Box es saisic on he squared reurns (4 lags) ogeher wih he ARCH LM saisic (five lags) on each reurns series. The resuls show evidence of an auocorrelaion paern in boh residuals and heir squares, which suggess ha GARCH parameerizaion migh be appropriae for he condiional variance processes. 4. Empirical Resuls 4.1 Consan Condiional Correlaion esimaes Following Bollerslev (1990), his invesigaion sared wih he assumpion of consan condiional correlaion in a mulivariae GARCH seing where variance covariance erms are ime varying. The CCC model 3 seemed o be he bes saring poin as i avoids compuaional complexiies and assures he posiive definieness of he condiional variance covariance marix as well as he condiional correlaion marix. Table presens he We chose 5 working days lengh in a week, so 0 days in one monh and 40 working days per year. 3 The esimaion is conduced using a modified RATS rouines originally available a

8 Asian Journal of Finance & Accouning resuls. Parameer µ corresponds o he mean equaion, whereas, and represen he condiional variance of boh he sock and bond reurns which are modeled by a separae univariae GARCH (1, 1) model wih no drif parameers. Finally characerizes he correlaion beween he wo asses. All parameers are found highly significan and posiive; he significance of mean equaion parameer µ shows he dependence of boh sock and bond reurns on heir lag reurns, and variance equaion parameers and suppor his modeling echnique, i.e. he mulivariae GARCH analysis, by revealing he presence of condiional heroskedasiciy in he ime series. The esimaed consan condiional correlaion beween he wo asses is Since i is posiive i can be argued ha boh sock and bond markes are exposed o common macroeconomic condiions. The esimaed coefficien of CCC reflecs he lower co-movemens beween he wo asses which is consisen wih prior lieraure (see, e.g., Keim and Sambaugh, 1986; Campbell and Ammer, 1993) and also provides a beer opporuniy for opimal porfolio selecion. Since he CCC model assumes ha he condiional correlaions are consan over ime, rolling condiional correlaions were esimaed wih a window size se o six monhs o check he validiy of his assumpion. Table. Bond-sock bivariae CCC-GARCH (1, 1) model Sock 0.156* 0.080* 0.01* 0.978* (0.031) (0.000) (0.001) (0.001) 0.13* Bond 0.061* 0.006* 0.080* 0.919* (0.007) (0.000) (0.005) (0.005) (0.013) Resuls are repored from a bivariae consan condiional correlaion GARCH (1, 1) process; conduced by using daily reurns on bond and sock marke indices for Russia from July 1994 o December 007. In he Table, represens he consan from he mean equaion whereas, and are he parameers of bivariae GARCH processes. The parameer sands for he correlaion beween wo asses. Sandard errors are in ( ). * indicaes he significance a 5% level. The developmens of he sock bond reurn correlaions in he Russian equiy marke indices and he JPM EMBI Russia index are ploed in Figure. The gray line represens a six-monh rolling window correlaion, whereas he brown line sands for condiional correlaion produced by he DCC model. Several ineresing feaures emerge from his figure. Excep for he period of he correlaions in boh indices are posiive. From he firs quarer of 1996 he correlaions were consanly increasing unil he collapse of he Russian financial marke in Augus Moreover, Figure 3 indicaes ha he sock bond correlaion flucuaed subsanially during he period of 1999 and 003 and hen i became raher sable. This may pose challenges for asse allocaion and risk managemen procedures. I is apparen 79

9 Asian Journal of Finance & Accouning ha he relaion beween sock and bond reurns has been raher unsable over ime, which is clear evidence agains he consan correlaion hypohesis rolling corr. cond. corr Jan-94 Jan-96 Jan-98 Jan-00 Jan-0 Jan-04 Jan-06 Figure. Six-monh rolling window correlaion and condiional correlaion beween Russian equiy marke indices and he JPM EMBI Russia index from 1994 o 007. Condiional correlaion is calculaed on he basis of he esimaes in Table 3. I may also be noed from Figure ha he condiional and rolling window sock bond reurn correlaions exhibi a very similar paern over ime. However, as expeced, he rolling window correlaion esimaes appear o be considerably more unpredicable han he condiional correlaions produced by he DCC model. Moreover, he DCC esimaes should accoun for he changes in volailiy, and hus be free from he possible rising bias during he periods of economic meldown. 4. Dynamic Condiional Correlaion esimaes Acknowledging he realiy ha he consan correlaion coefficien fails o reveal he dynamic marke condiions in response o innovaion, nex he DCC GRCH (1, 1) model proposed by Engle (00) was applied. Basically, he DCC-GARCH model esimaes condiional volailiies and correlaions in wo seps. In he firs sep he mean equaion of each asse in he sample, nesed in a univariae GARCH model of is condiional variance, is esimaed (see Figure 3), whereas he second sep illusraes he developmen of he ime varying correlaion marix (see Figure ). The resuls in Table confirm ha he condiional correlaions of bond and sock reurns are highly dynamic and ime varying. This is eviden from Figure 3 as well, which presens he plos of condiional variances based on he esimaion done in he firs sep of he DCC esimaion procedure. 80

10 Asian Journal of Finance & Accouning HBOND HSTOCK Jul-94 Jul-96 Jul-98 Jul-00 Jul-0 Jul-04 Jul-06 Figure 3. Condiional volailiies in Russian equiy marke indices and he JPM EMBI Russia index from 1994 o 007. The figure above shows ha he condiional variances are no consan over ime and especially volaile during he periods before and during he Russian financial crisis of Moreover i seems ha he volailiies of Russian socks and bonds move ogeher, which is consisen wih prior research. For insance, Schwer (1989) found ha volaily in he US sock and bond marke end o move ogeher. From Table 3, i is eviden ha he esimaes of he mean equaion and variance equaion are saisically significan which is consisen wih ime varying volailiy and jusifies a clusering phenomenon in he evoluion of volailiy. Moreover, he sum of esimaed coefficiens ( DCCs + DCCb ) in he variance equaion is close o uniy, implying ha volailiy exhibis a highly persisen behavior. Table 3. Bond-sock bivariae DCC-GARCH (1, 1) model DCCs DCCb Sock 0.148* 0.016* 0.0* 0.977* (0.03) (0.000) (0.001) (0.001) 0.005* 0.993* Bond 0.061* 0.06* 0.079* 0.90* (0.006) (0.000) (0.005) (0.005) (0.001) (0.001) Resuls are repored from a bivariae dynamic condiional correlaion GARCH (1, 1) process; conduced by using daily reurns on bond and sock marke indices for Russia from July

11 Asian Journal of Finance & Accouning o December007. In he Table, represens he consan from he mean equaion whereas, and are he parameers of bivariae GARCH processes. The parameers, DCCs and DCCb, are DCC-GARCH esimaes of sock and bond, respecively. Sandard errors are in ( ). * indicaes he significance a 5% level. Asymmeric Dynamic Condiional Correlaion esimaes The DCC model perfecly overcomes he heeroskedasiciy problem since he residuals of he reurns are sandardized by he condiional sandard deviaion based on he GARCH (1,1) process. However, i does no accoun for he asymmeries in condiional variances, covariances, and correlaions. Hence, an asymmeric version of he Dynamic Condiional Correlaion (ADCC) model proposed by Cappiello e al. (006) was adoped o deal wih he asymmeries in condiional variances, covariances, and correlaions of he wo asses. Table 4 presens he empirics. Again he GARCH (1, 1) parameers are highly significan confirming he ime varying variance covariance process as well as srenghening he use of mulivariae GARCH modeling for he Russian sock and bond marke daa. Parameer measures he asymmeries in condiional variances, covariances, and correlaions, and in his regard he resuls proved o be very ineresing. Empirics show ha he Russian bond marke and sock marke boh exhibis posiive asymmeries in condiional variances, covariances, and correlaions. Table 4. Bond-sock bivariae ADCC-GARCH(1, 1) model DCCs DCCb Sock 0.080* -0.05* 0.0* 0.977* 0.001* (0.009) (0.000) (0.001) (0.001) (0.000) 0.007* 0.98* Bond 0.06* 0.008* 0.098* 0.901* 0.044** (0.007) (0.000) (0.007) (0.007) (0.007) (0.00) (0.00) Resuls are repored from a bivariae asymmeric dynamic condiional correlaion GARCH (1, 1) model, conduced using daily reurns on bond and sock marke indices for Russia from July 1994 o December 007. In he Table, represens he consan from he mean equaion whereas, and are he parameers of bivariae GARCH processes. accoun for asymmeric behaviour. The las parameers, DCCs and DCCb, are DCC-GARCH esimaes of sock and bond, respecively. Sandard errors are in ( ). * (**) indicae he significance a 5% (10%) level. Finally, he significance of DCC-GARCH esimaes DCCs and DCCb once again allow us o conclude ha condiional correlaions of bond and sock reurns are highly dynamic and ime varying. 8

12 Asian Journal of Finance & Accouning 5. Summary and Conclusions In his sudy we address one of he mos fundamenal issues of radiional and modern porfolio managemen, i.e., he dynamics of sock bond correlaion and how i migh perform in he fuure. Sock bond correlaion plays an imporan role in asse allocaion, porfolio managemen and risk managemen. Despie is imporance, his phenomenon has been severely ignored in he conex of emerging markes, regardless of heir high reurns and favorable diversificaion opporuniies. We chose he Russian sock and bond marke as a es laboraory due o is rapid growh and aracion o boh domesic and inernaional invesors. The co-movemens beween he reurns on he sock and bond markes of Russia were modeled by using mulivariae condiional volailiy models. The invesigaion sared by applying Bollerslev s (1990) Consan Condiional Correlaion model o es wheher varying correlaions are saisically significan. Then he DCC-GARCH (1, 1) model proposed by Engle (00) was used o analyze he dynamics of condiional correlaions beween he wo asses. Finally, o invesigae he asymmeries in condiional variances, covariances, and correlaions, an asymmeric version of he Dynamic Condiional Correlaion (ADCC) model proposed by Cappiello e al. (006) was adoped. The empirical resuls do no suppor he assumpion of consan condiional correlaion and here was clear evidence of ime varying correlaions beween he Russian socks and bond marke. Moreover, boh asse markes exhibi posiive asymmeries. We believe ha our resuls offer a beer undersanding of he dynamics of he correlaions beween he socks and bonds in an emerging marke seing which is obviously very valuable for porfolio managers, inernaional invesors, risk analyss and financial researchers as well as for is policy implicaions. References Abid, F., Aoua, N., Mikhail, A.D. (003). Linkages beween, and conagion in, Asian sock and foreign exchange markes. Finance India 17, Alexander, G., Edwards, A., Ferri, M. (000). Wha does NASDAQ s high-yield bond marke reveal abou bondholder-sockholder conflics? Financial Managemen 9, Barsky, R. B. (1989). Why don he prices of socks and bonds move ogeher? American Economic Review 79, Baur, D., Lucey, B.M. (009). Fligh and conagion - An empirical analysis of sock-bond correlaions. Journal of Financial sabiliy 5, hp://dx.doi.org/ /j.jfs Bollerslev, T. (1990). Modeling he coherence in shor-run nominal exchange raes: A mulivariae generalized ARCH model. Review of Economics and Saisics 7, Bollerslev T., Engle, R.F., Wooldridge, J.M. (1988). A capial asse pricing model wih imevarying covariances. Journal of Poliical Economy 96, Bollerslev, T. (1986). Generalized Auoregressive Condiional Heeroskedasiciy. Journal of 83

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