هماهنگ سازی بازده و همبستگی روزانه پویا بین بازارهای بین المللی سهام

The use of close-to-close returns underestimates returns correlation because international stock markets have different trading hours. With the availability of 16:00 (London time) stock market series, we find dynamics of daily correlation and covariance, estimated using two non-synchroneity adjustment procedures, to be substantially different from their synchronous counterparts. Conditional correlation may have different signs depending on the model and data type used. Other findings include volatility spillover from the US to the UK (and France), and a reverse spillover which is not documented before. Also, unlike previous findings, we found the increase in daily correlation is prominent only under extremely adverse conditions when a large negative return has been registered.

The dynamics of daily correlation has a pivotal role in many important applications in finance. Riskmetrics™ uses it to produce value-at-risk (VaR) measures at short horizons. Erb et al. (1994) provide examples on how time varying correlation forecasts can affect optimal portfolio weights. Kroner and Ng (1998) show how time varying covariance matrices affect hedge ratios. Burns et al. (1998) show how a term structure of correlation can be built from a daily multivariate GARCH model. Such a correlation term structure can then be used to value derivative products whose payoff depends on the values of two or more assets. Under turbulent market conditions, real-time valuation of international portfolios can be critical. To produce accurate portfolio value, we need, among other things, accurate correlation estimates. Given the crucial role of correlation measures, it is not surprising that correlation dynamics and intertemporal relations between international stock markets are areas frequently explored by researchers. Volatility spillovers from the US to the rest of the world are reported in Eun and Shim (1989), Becker et al. (1990), Fischer and Palasvirta (1990), and Hamao et al. (1990). Other studies such as Koch and Koch (1991) and Von Furstenberg and Jeon (1989) find correlations have increased over time. King and Wadhwani (1990) and Bertero and Mayer (1990) find a substantial increase in correlation during stock market crises. More recent papers such as Theodossiou and Lee (1993), Longin and Solnik (1995) and Theodossiou et al. (1997) exploit a multivariate GARCH framework where all the conjectured relationships are tested jointly. It has been argued that a multivariate approach is the only right platform for studying the transmission mechanism and correlation dynamics. However, international stock markets have different trading hours. Hence the use of daily closing prices leads to an underestimation of the true correlations between stock markets. Some of the studies mentioned above by-pass the non-synchroneity problem by using weekly or monthly data.2 The use of low frequency data leads to small samples, which is inefficient for multivariate modelling especially when parameters are time varying. Moreover, monthly and weekly data cannot capture daily correlation dynamics. On the other hand, we have studies that use daily non-synchronous open-to-close and close-to-open returns. These studies cannot distinguish a spillover from a contemporaneous correlation.3 As a result, Riskmetrics™ (1996), and Burns et al. (1998)4 suggest various procedures for computing `synchronized'5 correlation from non-synchronous returns. To date, these non-synchroneity adjustment procedures have not yet been tested. The validation of these procedures is of paramount importance as they are potentially useful in cases where stock exchanges that do not share common trading hours. The objective of this study is twofold. First, we evaluate two returns synchronization procedures proposed in the literature; the Riskmetrics™ method and a GARCH-based method proposed by Burns et al. (1998). Second, we investigate the daily dynamics and spillover effects of the conditional variance, correlation, and covariance, for stock index returns in the US, the UK and France. The study of daily dynamics of second moments of stock returns is still incomplete because of the problem of non-synchronous data. The availability of Datastream 16:00 (London time 6) synchronous stock market series makes such a study possible now and allows us to make a clear distinction, as never before, between a spillover and a contemporaneous correlation. Another innovation in this paper is the use of the asymmetric dynamic covariance (ADC) ( Kroner and Ng, 1998) model, which, because of its all-encompassing nature, will indicate if multivariate GARCH models fitted previously are adequate for modelling correlation dynamics. 7 Results obtained here suggest that there is no spillover effect at the returns level. Previous studies have reported finding a volatility spillover effect from the US to the other countries. Here, we find also a reverse volatility spillover from Europe to the US. The asymmetric effect in conditional variance is already well documented, and there have been claims that correlation increases when markets are more volatile. Here, we find evidence that asymmetry permeates both conditional covariance and conditional correlation. But, unlike findings in previous studies, we find correlation responds to volatility only if a large negative return occurred on the previous day. Using both synchronous and non-synchronous daily returns, we find synchronized conditional measures to be substantially different from their synchronous counterparts. The conditional measures are also found to be sensitive to the model used to calculate them. The correlation among the conditional covariances, across models and data types, ranges from 0.396 to 0.795. But, the correlation of the corresponding conditional correlations can be as low as −0.012. This is alarming because different models are producing different hedge ratios and even conflicting hedging strategies. Our results show that the Riskmetrics™ model leads to more volatile hedge ratios and more conservative VaR estimates than the ADC model. The remainder of this study is organized as follows. Section 2 describes the data and computes sample correlations from synchronous and non-synchronous returns. Section 3 describes the Riskmetrics™ and the ADC models. The Riskmetrics™ model has built-in procedures for adjusting non-synchroneity. For the ADC model, we adopt the non-synchroneity adjustment procedures proposed in Burns et al. (1998). Section 4 reports and discusses the correlation dynamics and results from estimating the ADC model. In Section 5, we examine the in-sample sensitivity of the conditional measures with respect to model and data type. In Section 6, we compare the one-step-ahead forecasts for conditional correlation and covariance produced by the two models, and evaluate the economic significance of the differences using a VaR example. Finally, Section 7 concludes the paper.