Volatility Spillover in Regional Emerging Stock Markets: A Structural Time-Series Approach

Volatility spillovers among the stock markets of Bahrain, Kuwait, and Saudi Arabia are investigated using the concept of stochastic volatility and structural time-series modeling. The results reveal volatility spillovers, in which the Kuwait market plays the major role. It is also found that volatility in one market cannot be explained fully in terms of volatility in the other two markets, but that, out of the three markets, the Kuwait market seems to be the most influential. Some explanations are put forward for why this is the case.     

Empirical Analysis of Stock Returns and Volatility: Evidence from Seven Asian Stock Markets Based on TAR-GARCH Model

This paper investigates the time-series behavior of stock returns for seven Asian stock markets. In most cases, higher average returns appear to be associated with a higher level of volatility. Testing the relationship between stock returns and unexpected volatility, the evidence shows that four out of seven Asian stock markets have significant results. Further analyzing the relationship between stock returns and time-varying volatility by using Threshold Autoregressive GARCH(1,1)-in-mean specification indicates that the null hypothesis of no asymmetric effect on the conditional volatility is rejected for the daily data. However, the null cannot be rejected for the monthly data.     

Time Inhomogeneous Multiple Volatility Modeling

Price variations at speculative markets exhibit positive autocorrelationand cross correlation. Due to large parameter spaces necessaryfor joint modeling of variances and covariances, multivariateparametric volatility models become easily intractable in practice.We propose an adaptive procedure that identifies periods ofsecond-order homogeneity for each moment in time. To overcomethe high dimensionality of the problem we transform the multivariateseries into a set of univariate processes. We discuss thoroughlythe implementation of the adaptive technique. Theoretical andMonte Carlo results are given. We provide two applications ofthe new method. For a bivariate exchange rate series we comparethe multivariate GARCH approach with our method and find thelatter to be more in line with the underlying assumption ofindependently distributed innovations. Analyzing a 23-dimensionalvector of asset returns we underscore the case for adaptivemodeling in high-dimensional systems.     

The Value Premium and Time-Varying Volatility

Abstract:  Numerous studies have documented the failure of the static and conditional capital asset pricing models to explain the difference in returns between value and growth stocks. This paper examines the post-1963 value premium by employing a model that captures the time-varying total risk of the value-minus-growth portfolios. Our results show that the time-series of value premia is strongly and positively correlated with its volatility. This conclusion is robust to the criterion used to sort stocks into value and growth portfolios and to the country under review (the US and the UK). Our paper is consistent with evidence on the possible role of idiosyncratic risk in explaining equity returns, and also with a separate strand of literature concerning the relative lack of reversibility of value firms' investment decisions.     

期货市场的交易量与价格波动

本文检验了美国期货市场WTI原油、S＆P500指数和10年期国债品种的日内、日间价格波动与日内交易量、隔日交易量之间的关系,发现预期的日内和隔日交易量都有平抑期货市场价格波动的作用,非预期的隔日交易量与期货价格波动之间有正相关关系,非预期的目内交易量对价格波动的影响不显著。从信息对称性的角度分析,预期的交易量中含有更多信息,能抑制期货价格的偏离;非预期的交易量主要由信息反馈者提供,他们往往对期货价格的变动做出过度反应,从而加剧价格波动。     

Modeling Time-Varying Volatility and Expected Returns: Evidence from the GCC and MENA Regions

The aim of this study is to investigate empirically the underlying nexus of stock market returns and volatility in the Gulf Cooperation Council (GCC) countries and Middle East and North Africa (MENA) region by using the GARCH-M model. We find that volatility is time-varying in all countries, which indicates substantial variation in the degree of risk across time. However, we do not find empirical support that this time-varying volatility significantly explains expected returns, except in the case of Kuwait, United Arab Emirates, and the MENA region portfolio. Our findings show that stock return volatility is negatively correlated with stock returns in these three markets under the assumption of investor risk aversion. This lends some support to the hypothesis of a volatility-driven negative relationship in the literature. The policy implications of our results are discussed.     

Shock and Volatility Transmission in the Futures and Spot Markets: Evidence from Turkish Markets

This paper examines the volatility transmission mechanism between the futures and corresponding underlying asset spot markets, focusing on Turkish currency and stock index futures traded on the lately established Turkish Derivatives Exchange (TURKDEX). Employing multivariate generalized autoregressive conditional heteroskedasticity modeling, which allows for potential spillovers and asymmetries in the variance-covariance structure for the market returns, the paper investigates the volatility interactions among each of the three futures-spot market systems. For all market systems under study, the volatility spillovers are found to be important and bidirectional. For the stock index market system, in line with the previous literature, volatility shows asymmetric behavior and strong asymmetric shock transmission. The main implication is that investors need to account for volatility spillovers and asymmetries among the futures and the spot markets to correctly build hedging strategies.     

The Causal Relationship Between Real Estate and Stock Markets

This paper examines the dynamic relationship that exists between the US real estate and S&P 500 stock markets between the years of 1972 to 1998. This is achieved by conducting both linear and nonlinear causality tests. The results from these tests provide a number of interesting observations which primarily show linear relationships to be spuriously affected by structural shifts which are inherent within the data. Linear test results generally show a uni-directional relationship to exist from the real estate market to the stock market. However, these results are not consistent with financial theory and for all sub-samples of the data. In contrast, the nonlinear causality test shows a strong unidirectional relationship running from the stock market to the real estate market, and is consistent in the presence of any structural breaks.     

Integration Between the Baltic and International Stock Markets

Using cointegration tests, this paper analyzes the existence of long-run relationships among Baltic stock markets and major international stock markets, including the United States, Japan, Germany, the United Kingdom, and France. Bivariate and multivariate cointegration tests indicate a common trend linking Latvia to European markets. Evidence indicates that the German market dominates this long-run relationship. In general, short-term Granger causality indicates causality running from the European markets to the Baltic markets, as well as among the Baltic states, excepting Latvian and Lithuanian short-term effects on the Estonian market. Overall, the results suggest that international investors can obtain diversification benefits given a long-term investment horizon because of the low degree of integration between the Baltic and international capital markets.     

Volatility dependences of stock markets with structural breaks

We develop a Vector Heterogeneous Autoregression model with Continuous Volatility and Jumps (VHARCJ) where residuals follow a flexible dynamic heterogeneous covariance structure. We employ the Bayesian data augmentation approach to match the realised volatility series based on high-frequency data from six stock markets. The structural breaks in the covariance are captured by an exogenous stochastic component that follows a three-state Markov regime-switching process. We find that the stock markets have higher volatility dependence during turmoil periods and that breakdowns in volatility dependence can be attributed to the increase in market volatilities. We also find positive correlations between the Asian stock markets, the European stock market, and the UK stock market. The US stock market has positive correlations with all other markets for most of the sample periods, indicating the leading position of US stock market in the global stock markets. In addition, the proposed three-state VHARCJ model with Dynamic Conditional Correlation (DCC) and break structure under student-t distribution has a superior density forecast performance as compared to the competing models. The forecast models with structural breaks outperform those without structural breaks based on the log predicted likelihood, the log Bayesian factor, and the root mean square loss function.     

Volatility Spillover Effects in Interregional Equity Markets: Empirical Evidence from Brazil and Turkey

We investigate volatility spillovers between two stock markets: Turkey and Brazil. Using a misspecification-robust causality-in-variance test, we find evidence supporting volatility spillovers from the São Paulo Stock Exchange to the Istanbul Stock Exchange. Moreover, the results imply that financial crises may change the nature of volatility spillovers between the two markets by adding an additional channel of volatility transmission from Turkey to Brazil.     

Regulation Change and Volatility Spillovers: Evidence from China's Stock Markets

This paper investigates the structural changes of volatility spillovers between Chinese A-share and B-share markets induced by a regulation change on February 19, 2001, that allowed Chinese domestic investors to trade in the B-share market. The empirical results of the study, using high-frequency intraday data collected from a sample of seventy-eight firms issuing both A-shares and B-shares and employing a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model, show that after the regulation change, the volatility in A-shares increases the volatility in B-shares, thus increasing the risk of the whole market, whereas the latter reduces the former, thus reducing the risk of the whole market. A further investigation of the determinants influencing these structural changes shows that the following factors can encourage structural changes that reduce overall market risk: government ownership, institutional ownership, firm size, B-share proportion, and market-to-book ratio. Conversely, the following factors can encourage structural changes that increase overall market risk: dual roles of chief executive officer and chairman and the joint effect of firm size and B-share proportion.     

Return and Volatility Spillovers Between Large and Small Stocks in the UK

Abstract:  This paper investigates return and volatility spillover effects between the FTSE 100, FTSE 250 and FTSE Small Cap equity indices using the multivariate GARCH framework. We find that return and volatility transmission mechanisms between large and small stocks in the UK are asymmetric. In particular, there are significant spillover effects in both returns and volatility from the portfolios of larger stocks to the portfolios of smaller stocks. For volatility, there is also evidence of limited feedback from the portfolios of smaller stocks to the portfolios of larger stocks, although sub-period analysis suggests that this is to some extent period-specific. Simulation evidence shows that non-synchronous trading potentially explains some, but not all, of the spillover effects in returns, and that it explains none of the spillover effects in volatility. These results are consistent with a market in which information is first incorporated into the prices of large stocks before being impounded into the prices of small stocks.     

Correlation and Volatility Dynamics in International Real Estate Securities Markets

We study international correlation and volatility dynamics of publicly traded real estate securities using monthly returns from 1984 and 2006. We also examine, for comparison, the correlations among the corresponding stock markets. A multivariate dynamic conditional correlation model captures the time-varying correlation within the full period. We confirm lower correlations between all real estate securities market returns than those between the stock market returns themselves. Some significant variations and structural changes in the correlation structure happened within the sample period. We detect a strong and positive connection between real estate securities market correlations and their conditional volatilities. We also find the international correlation structure of real estate securities and the broader stock market are linked to each other. Our results have economic motivations regarding the potential integration of international real estate securities markets and the possibility of including information on changing correlations and volatilities to design more optimal portfolios for international real estate securities.

An Empirical Study of Volatility in Seven Southeast Asian Stock Markets Using ARV Models

This paper investigates the volatility persistence, volatility variability from day to day and transmission of volatility in seven Southeast Asian stock markets from 1980 to 1991 using the ARV approach. We found strong evidence that shocks to volatility are persistent in Taiwan. Moreover, the Stock Exchange of Thailand Daily Index has the strongest interday volatility fluctuation. Instantaneous causality of volatility among six of the seven markets (except Seoul) was discovered. Besides, there is significant volatility spillover effect from Hong Kong to Taiwan, Malaysia to Singapore and Singapore to Malaysia in the period 1980 to 1991.     

Volume and Volatility in Foreign Currency Futures Markets

In this paper we propose and test several hypotheses concerning time series properties of trading volume, price, short and long-term relationships between price and volume and the determinants of trading volume in forcign currency futures. The nearby contracts for British Pound, Canadian Dollar, Japanese Yen, German Mark and Swiss Franc are analyzed in three frequencies i.e. daily, weekly and monthly.We find supportive evidence for all the five currencies that the price volatility is a determinant of the trading volume changes. Furthermore, the volatility of the price process is a determinant of the unexpected component of the changes in trading volume. Also, there is a significant relationship between the volatility of price and the volatility of trading volume changes for three of the five currencies in the daily frequency and for one currency in the monthly frequency.     

Returns,Volatilities, and Correlations Across Mature,Regional, and Frontier Markets: Evidence from South Asia

We investigate returns, volatilities, and correlations across mature, dominant regional, and frontier equity markets. Standard & Poor's 500 is chosen as a mature equity market; India is chosen as a dominant regional market; and Bangladesh, Pakistan, and Sri Lanka are chosen as frontier markets. Our empirical tests show that the frontier markets remain fundamentally decoupled from the mature markets during normal market periods. During turbulent times, the contagion effects from the mature to the frontier markets become more pronounced. The results suggest that the dominant regional market plays a key role in disseminating shocks across the frontier markets during normal periods; during the turbulent recent financial crisis period, a similar contagion is not observed.     

Volatility Spillovers and Contagion During the Asian Crisis: Evidence from Six Southeast Asian Stock Markets

Using a multivariate generalized autoregressive conditional heteroskedasticity (GARCH-M) model, we investigate volatility spillovers in six Southeast Asian stock markets around the time of the 1997 Asian crisis. We focus on interactions with the U.S. market as a world financial market, and with the Japanese market as a regional financial market. We also use bivariate GARCH-M models to examine the behavior of individual markets and their interactions with other markets in the region. All models lend support to the idea of the "Asian contagion," which started in Thailand and rapidly spread to other markets.     

Optimal hedge ratio and hedging effectiveness of stock index futures: evidence from India

In a free capital mobile world with increased volatility, the need for an optimal hedge ratio and its effectiveness is warranted to design a better hedging strategy with future contracts. This study analyses four competing time series econometric models with daily data on NSE Stock Index Futures and S&P CNX Nifty Index. The effectiveness of the optimal hedge ratios is examined through the mean returns and the average variance reduction between the hedged and the unhedged positions for 1-, 5-, 10- and 20-day horizons. The results clearly show that the time-varying hedge ratio derived from the multivariate GARCH model has higher mean return and higher average variance reduction across hedged and unhedged positions. Even though not outperforming the GARCH model, the simple OLS-based strategy performs well at shorter time horizons. The potential use of this multivariate GARCH model cannot be sublined because of its estimation complexities. However, from a cost of computation point of view, one can equally consider the simple OLS strategy that performs well at the shorter time horizons.     

Modeling the Conditional Covariance Between Stock and Bond Returns: A Multivariate GARCH Approach

To analyze the intertemporal interaction between the stock andbond market returns, we assume that the conditional covariancematrix follows a multivariate GARCH process. We allow for asymmetriceffects in conditional variances and covariances. Using dailydata, we find strong evidence of conditional heteroskedasticityin the covariance between stock and bond market returns. Theresults indicate that not only variances, but also covariancesrespond asymmetrically to return shocks. Bad news in the stockand bond market is typically followed by a higher conditionalcovariance than good news. Cross asymmetries, that is, asymmetriesfollowed from shocks of opposite signs, appear to be importantas well. Covariances between stock and bond returns tend tobe relatively low after bad news in the stock market and goodnews in the bond market. A financial application of our modelshows that optimal portfolio shares can be substantially affectedby asymmetries in covariances. Moreover, our results show sizablegains due to asymmetric volatility timing.