پاسخهای نامتقارن از بازارهای بین المللی سهام به حجم معاملات سهام

The major goal of this paper is to examine the hypothesis that stock returns and return volatility are asymmetric, threshold nonlinear, functions of change in trading volume. A minor goal is to examine whether return spillover effects also display such asymmetry. Employing a double-threshold GARCH model with trading volume as a threshold variable, we find strong evidence supporting this hypothesis in five international market return series. Asymmetric causality tests lend further support to our trading volume threshold model and conclusions. Specifically, an increase in volume is positively associated, while decreasing volume is negatively associated, with the major price index in four of the five markets. The volatility of each series also displays an asymmetric reaction, four of the markets display higher volatility following increases in trading volume. Using posterior odds ratio, the proposed threshold model is strongly favored in three of the five markets, compared to a US news double threshold GARCH model and a symmetric GARCH model. We also find significant nonlinear asymmetric return spillover effects from the US market.

As a result of globalization, de-regulation and advances in information technology, the modern theory of international markets has switched from the traditional view of market segmentation to the concept of market integration. A substantial amount of research emphasizes the co-movements of international stock markets and explores the dynamics of return co-variances and spillover effects between markets. For example, Jaffe and Westerfield [1] provide empirical evidence of a significant direct spillover effect among some national stock markets, while Eun and Shim [2] find return spillover effects among national markets and an influential role of the US market on the cross-country market index series. Ross [3] argues further that information from one stock market can be incorporated into the volatility process of other stock markets. Hamao et al. [4], Theodossiou and Lee [5], Chiang and Chiang [6] and Martens and Poon [7] subsequently find supporting evidence for volatility spillover among major stock markets. There is also substantial evidence in the literature that stock markets react asymmetrically to market news results. This phenomenon was first discovered by Black [8] and Christie [9] who discuss the leverage effect as the cause of higher volatility following negative stock returns; similar to the market over-reaction hypothesis discussed in Ref. [10]. There is also the volatility feedback hypothesis which says that higher volatility causes stock prices to fall. Many models have been developed to capture types of asymmetric behavior, most fit into the threshold GARCH framework. Glosten et al. (1993) employ a threshold GJR-GARCH model and find evidence that local negative market news causes increased market volatility [47]. This finding is confirmed in studies by Koutmos [11], Nam et al. [10] and Brooks [12], using double threshold models. These papers also find evidence of faster mean reversion dynamics following local market bad news. More recently, Chen et al. [13] employ a double-threshold GARCH model with a US market threshold variable, to explore the dynamics of daily stock-index returns for six international markets from 1985 to 2001. Their results provide strong evidence supporting an asymmetric nonlinear spillover effect from the US market to other markets in Europe and Asia. The US market news transmits asymmetrically, around a threshold value, to each of the national stock markets considered with average volatility in each national market much higher following bad US news. Further, Chen and So [14] explore a range of international markets to use as exogenous threshold values in a double threshold GARCH model. They find that the Japanese market has little spillover or threshold nonlinear effect on mean returns in Asian markets, in comparison with the US market. These results are supported by Wang and Firth [15], who find the emerging market of China does not exhibit significant spillover effects to other markets worldwide, including those in Asia. The US return has thus evolved as the preferred threshold variable in the examination of return spillover and nonlinear asymmetric effects. However, this previous work ignores the possible correlation between the stock price or return and trading volume. Numerous financial studies have documented this important relationship. Clark [16] and Epps and Epps [17] suggested that trading volume is a good proxy for information arrival from the capital market. The hypothesis has been further supported by empirical evidence; Lamoureux and Lastrapes [18], Kim and Kon [19], Andersen [20], Gallo and Pacini [21] found the same effect for the US stock market; Omran and McKenzie [22] observed this effect for the UK stock market; Bohl and Henke [23] reported similar evidence for the Polish stock market. Ying [24] was the first to provide strong empirical evidence supporting an asymmetric relation between trading volume and price-change. By investigating six series of daily data from NYSE, Ying made the following conclusions: a small trading volume is usually accompanied by a fall in price; a large volume is usually accompanied by a rise in price; and a large increase in volume is usually accompanied by either a large rise in price or a large fall in price. These propositions lay an important foundation for our nonlinear asymmetric hypothesis and illustrate that a linear relationship between price return and volume, and/or volatility and trading volume, may not be sufficient to capture the true relationship. This hypothesis is also documented by Karpoff [25] in an extensive survey of research into the relationship between stock–price change and trading volume. Karpoff suggests several reasons why the volume–price change relationship is important and provides evidence to support the asymmetric volume–price change hypothesis. His asymmetric hypothesis implies that the correlation between volume and price change is positive when the market trend is going up, but that this correlation is negative when the market trend is downwards. This is again important and highlights that we should not simply add a linear exogenous volume term to the mean equation in a GARCH model for stock returns. To capture the possible nonlinearity we will consider an asymmetric piecewise linear relationship between price (return) and volume, as can be captured by threshold models [26]. Departing from traditional work that focused on the contemporaneous relation between return and trading volume, Chordia and Swaminathan [27] examine the causal relationship and the predictive power of trading volume on the short-term stock return. Their empirical evidence suggests that volume plays a substantial role in the dissemination of national market-wide information. In a dynamic context, Lee and Rui [28] utilize the GARCH(1,1) model to investigate the relationship between stock returns and trading volume using the New York, Tokyo and London stock markets. Their empirical results suggest that US financial market variables, in particular US trading volume, have extensive predictive power in both the domestic and cross-country markets, after the 1987 market crash. Moosa et al. [29] employ a bivariate VAR model and find significant mean level asymmetry in the price–volume relationship for the future market in crude oil prices; they did not consider a heteroscedastic model and they enforced the threshold variable to be zero. The above findings further enforce our belief that a consideration of trading volume as a threshold variable might add to the understanding of capital market behavior in general. The major objective of this study is thus to investigate whether stock returns, volatility and international return spillover effects react in a threshold nonlinear fashion to changes in trading volume, in five international markets. As far as the authors are aware, this is the first time that trading volume has been employed as a threshold variable in a dynamic heteroscedastic model of stock returns. To address the above issue, we follow Chen et al. [13] and employ a double-threshold autoregressive GARCH model, using a Bayesian estimation approach through Markov chain Monte Carlo (MCMC) methods. This new model allows threshold nonlinearity in both the mean and volatility processes to be driven by lagged changes in trading volume. We compare this model to the US news threshold model in Ref. [13] and a symmetric GARCH model. We utilize the posterior odds ratio, the standard Bayesian model comparison technique, as in Ref. [30], to determine which is the most favored model in each market. We also employ the causality tests of Ref. [29] to motivate and validate our models. Building on the work of Ying [24], Karpoff [25] and Moosa et al. [29], our empirical results provide strong evidence supporting the return-volume nonlinear threshold relation. The stock return, volatility and international return spillover do react asymmetrically, around a threshold value of change in trading volume, in the five markets considered. Our findings shed new light on the application of trading volume in the market integration literature and develop a new avenue for asset pricing in a multi-market framework. The remainder of this study proceeds as follows. Section 2 describes the data used in this study and presents some statistical properties of the stock returns in a standard GARCH(1,1) specification. Section 3 discusses the Bayesian methods for estimation and present the double threshold models considered. Section 4 discusses the estimated results for each model, and compares the findings with the existing literature. Section 5 discusses the Bayesian model comparison methods and discusses the findings for each market. Section 6 contains concluding remarks.

In this paper, we have thoroughly examined the empirical dynamic relationship between stock return, return volatility, international return spillover and change in trading volume for five international stock markets. Conforming to well-established empirical results, stock returns and return volatility display a certain degree of persistence and consistent with a meteor-shower hypothesis, our empirical results suggest that the stock-return news developed from US is transmitted significantly to each of the five national stock markets. In particular, the US return-news is positively correlated with the stock price of each national market but this correlation is asymmetric around a threshold US return value and/or a value of trading volume change. This confirms the minor goal of this paper. The major issue of this study has been whether stock returns and return volatility react asymmetrically, in a threshold nonlinear fashion, to trading volume change. By employing a double-threshold GARCH model to capture the nature of market reaction to trading volume change, we find that a large increase in volume is accompanied by a large rise in average stock price, while a decrease in volume is associated with lower average returns, with the exception of Thailand. Moreover, the return volatility of all the stock-markets also displayed an asymmetric reaction to volume change, around a threshold level. With the exception of Taiwan, the average magnitude of return volatility following an increase in trading volume was much larger than that following decreases in volume. Our model comparison results revealed that the asymmetric nonlinear model, with change in volume as a threshold, was decisively favored in the three Asian markets: Taiwan, Thailand and South Korea. However, the DT-GARCH model of Chen et al. [13], with US return news as threshold variable, was favored in UK and France. In summary, our findings are consistent with, and add to, the arguments of Ying [24], Karpoff [25] and Gallant et al. [43]: that the joint study of stock price and trading volume, as an asymmetric nonlinear relationship, leads to a better understanding of capital market behavior. These findings shed new light on the application of trading volume in the market integration literature and develop a new avenue for asset pricing in a multi-market framework.