انتقال نوسانات همزمان و اثرات لبریز :بازارهای سهام و بازارهای آتی ایالات متحده و هنگ کنگ

Contemporaneous transmission effects across volatilities of the Hong Kong Stock and Index futures markets and futures volume of trade are tested by employing a structural systems approach. Competing measures of volatility spillover, constructed from the overnight U.S. S&P500 index futures, are tested and found to impact on the Hong Kong asset return volatility and volume of trade patterns. The examples utilize intra-day 15-min sampled data from this medium-sized Asia Pacific equity and derivative exchange. Both the intra- and inter-day patterns in the Hong Kong market are allowed for in the estimation process.

The determinants of short-horizon (including intra-day) volatility are generally assumed to be unknown and controversial. Existing studies do not jointly consider contemporaneous and lead/lag volatility and volume of trade effects between equity and derivative markets (volatility transmissions) and volatility effects from other derivative markets (volatility spillovers). The former issue is important in option-pricing models where better estimates of volatility are required. The latter issue can also be important in pricing options and in position setting in spot and derivative markets. International volatility spillovers between developed stock markets, employing the vector autoregressive (VAR) time series estimator, is first reported in Eun and Shim (1989). A multi-country factor model is employed in King and Wadhwani (1990) to measure these effects between developed international stock markets. Confounding effects from employing close-to-close or open-to-close return data mean that it is difficult to separate the spillover effect from market specific volatility effects. Leachman and Frances (1996) use monthly recorded G7 stock market closing prices but do not separate market specific from volatility spillover effects. The conditional variance from fitted univariate GARCH models is employed in a VAR framework. Booth, Martikainen, and Tse (1997) measure asymmetric volatility spillover effects between Scandinavian stock markets using the multivariate exponential GARCH (EGARCH) estimator. Daily sampled price data is employed but some confounding is present as the Danish market closes 30 min prior to the Norwegian, Swedish and Finnish markets. Karolyi (1995) avoided the confounding effect and quantified daily volatility spillover effects between the synchronously sampled Canadian and U.S. equity markets. The study employs variants of multivariate generalized autoregressive heteroscedastic (MGARCH) time series volatility structures. In this paper, we avoid the confounding effects from sampling data from international markets where opening and closing times overlap by focusing on markets (U.S. and Hong Kong) that trade in different time zones. In addition, we employ intra-day data sampled from non-overlapping time zones for these markets in the analysis. Volatility spillovers are reported from the U.S. and U.K. markets onto Asia Pacific markets trading in different time zones. Hamao, Masulis, and Ng (1990) find asymmetric pre-crash daily volatility spillovers from New York to London and London to Tokyo but no spillovers in other directions. Post-crash volatilities are reported to have increased and effects from Japan more pronounced in Hamao, Masulis, and Ng (1991). A univariate GARCH estimator is employed in those studies. Koutmos and Booth (1995) use open-to-close records from these markets and a multivariate EGARCH structure to measure these spillover effects. Other studies of volatility spillovers between these three markets employing MGARCH estimators are reported for daily price records in Dae and Karolyi (1994) and using weekly price records in Theodossiou, Kahya, Koutmos, and Andreas (1997). Volatility spillovers from the U.S. and Japan onto the Asia Pacific stock markets of Hong Kong, Singapore, Taiwan and Thailand are reported in Liu and Pan (1997). International index futures meteor shower spillovers and heat wave persistence volatility effects are tested by Booth, Chowdhury, Martikainen, and Tse (1997). MGARCH volatility estimators as well as extreme value volatility estimators with a VAR structure are employed. The extreme value estimates are constructed from open-to-close and high/low records from the U.S., U.K. and Japanese stock index futures markets so that confounding effects from differential market opening and closing are not accounted for. We take an alternative approach in this paper by employing a systems of equations estimator. The U.S. data is carefully sampled so that shocks from this market enter as exogenous variables but are actually observed overnight prior to the open of the Hong Kong market. There are few studies of intra-day volatility transmission between derivative markets and the underlying asset markets that employ formal econometric estimators. Chan, Chan, and Karolyi (1991) find volatility transmissions and two-way predictability between the S&P500 cash and futures returns volatility within a bi-variate GARCH structure for intra-day data sampled at 5-min intervals. However, this time series estimator and those employed in the above volatility spillover studies impose exogenous restrictions or causality restrictions (lagged returns and lagged volatility) in the estimation process. Given that information transmission is virtually instantaneous, it is more logical to employ structural systems incorporating simultaneous endogenous effects in the formalization. When contemporaneous volatility and volume effects are synchronously observed at high frequency then these effects dominate lagged time series effects in estimated structural systems. Koch and Koch (1991) had earlier employed a simultaneous set of equations in measuring daily return linkages across the U.S. and Japanese stock markets. Modeling the contemporaneous volume effects within a tri-variate structural simultaneous systems framework was first reported for testing 15-min volatility transmissions from the Australian index futures to stock market in Gannon (1994). This structure accounts for similar U-shaped patterns in intra-day index futures volatility and volume of trade. Volatility spillovers from the U.S. index futures to Hong Kong index futures and stock markets are tested with 15-min sampled data within a bi-variate structural simultaneous systems framework in Gannon and Choi (1998). These latter two structural models separately focus on market specific volatility transmission and international volatility spillover effects. In this paper, both of these latter applications are extended to jointly incorporate all effects employing data sampled at similar time intervals. In order to test effects of changes in market structure and effects of market specific information announcements, it is fundamental that existing market features such as volatility transmission and spillover effects are controlled for. Then contemporaneous correlations across variables of interest are identifiable. This systematic approach is employed in this paper. The examples utilize intra-day 15-min cash index and index futures data sampled from the medium-sized Asia Pacific equity and derivative exchanges in Hong Kong. Active derivative trade is well established in this economy. Potential overnight volatility spillovers from the U.S. Standard and Poors 500 index futures onto these observed series are tested. The sampling is undertaken during normal market trading activity (non-volatile) between 1993 and 1994 well away from the crash periods of 1987 and 1997. This sampling period also coincides with the initial provision of real-time live feed public availability of transaction data. The linking of the Hong Kong dollar to the U.S. currency unit means that this index futures market should be responsive to movements in international asset prices. In this paper, the simultaneous volatility structure is described in Section 2. The data is described and empirical estimates are presented and discussed in Section 3. Some concluding comments and issues for further research are provided in Section 4.

This Hong Kong example is very interesting because U.S. market spillover effects are important. In this market it is well documented that speculative activity is relatively strong with position setting and re-balancing of futures positions. The inter-day volatility effect is very important. There is strong evidence of unidirectional transmission effects from 334 G. Gannon / International Review of Financial Analysis 14 (2005) 326–336 volatility of the HSIF to volatility of the HSI for this high frequency observed data. The spillover effect from the overnight U.S. index futures market is very significant and dominates intra-day patterns obtained from exclusion of this effect. This effect is most pronounced in the Hong Kong index futures volatility equation where many parameters are no longer significant when the spillover effect is included. Given that the reduced forms and final forms of these estimated systems can be obtained, additional insight into the behaviour of these volatility processes and equations can be gained through multistep estimation and simulation analysis. Identification of the structural form system is important because then unique reduced form forecasts can be generated. Then significant systematic effects can be employed volatility estimates entering optionpricing models and in position setting and re-balancing positions in the futures market.