The linkages,persistence, asymmetry in the volatility,the price discovery and efficiency,and the effect of the US subprime mortgage financial crisis on the spot and the futures market’s returns: the case of India

This article examines the effects of persistence, asymmetry and the US subprime mortgage crisis on the volatility of the returns and also the price discovery, efficiency and the linkages and causality between the spot and futures volatility by using various classes of the ARCH and GARCH models, and through the Granger’s causality. We have used two indices: one for spot and the other for futures, for the daily data from 12 June 2000 to 30 September 2013 from Nifty stock indices. We have then tested for ARCH effects, and subsequently employed various models of the ARCH and GARCH conditional volatility. The GARCH(1,1) model is found to be significant, and it implies that the returns are not autocorrelated and have ‘short memory’. It supports the hypothesis of the efficiency of the markets. The negative ‘news’ has more significant effect on volatility, corroborating the ‘leverage impact’ in finance on market volatility. We have also tested the volatility spillover effects. The two methods we employed support the spillover effects and the causality is bidirectional. We also have used the dummy variable for the US subprime mortgage financial crisis and found that they are statistically significant. Indian stock market is thus integrated to the world stock markets.     

The Chinese Stock Market Does not React to the Japanese Market: Using Intraday Data to Analyse Return and Volatility Spillover Effects

In this paper, we use high‐frequency data to explore the effects of return and volatility spillover during periods in which trading hours in China and Japan overlap. Specifically, we utilize 5‐min returns to estimate fractionally integrated asymmetric power autoregressive conditional heteroskedasticity and fractionally integrated exponential generalized autoregressive conditional heteroskedasticity models, then use the models' standardized residuals to employ a cross‐correlation function approach that tests for the degree to which the Chinese and Japanese markets affect each other. Results indicate a unidirectional influence of the Chinese stock market on Japanese markets in terms of return. This result is likely attributable to restrictions on foreign investment in the Chinese market and the lack of diversified international portfolios among individual Chinese investors.     

The effect of oil prices on stock prices: fresh evidence from asymmetric causality tests

This article investigates the causal impact of oil prices on stock prices in each G7 market as well as in the world market. An asymmetric causality test developed by Hatemi-J is used for this purpose. Since the underlying data appears to be non-normal with time-varying volatility, we use bootstrap simulations with leverage adjustments in order to produce more reliable critical values than the asymptotic ones. Based on symmetric causality tests, we find no causal effect of oil prices on the stock prices of the world market or any of the G7 countries. However, when we apply an asymmetric causality test, we find that increasing oil prices cause stock prices to rise in the world, the U.S. and Japan while decreasing oil prices cause stock prices to fall in Germany. This may imply that the world, the U.S. and Japanese stock markets consider increases in oil prices as an indicator of good news as this may mean that there is an increase in oil demand due to an expected growth in the economy while the German stock market treats decreasing oil prices as a signal of an expected contraction in the economy.     

Modeling and forecasting return jumps using realized variation measures

This paper proposes a simple HAR-RV-based model to predict return jumps through a conditional density of jump size with time-varying moments. We model jump occurrences based on a version of the autoregressive conditional hazard model that relies on past continuous realized volatilities. Applying our methodology to seven equity indices on the U.S. and Chinese stock markets, we reach the following key findings: (i) jump occurrence and size are dependent on past realized volatility, (ii) the proposed model yields superior in- and out-of-sample jump size density forecasts compared to an ARMA(1,1)-GARCH(1,1) model, (iii) and the occurrence and sign of return jumps are predictable to some extent.     

The relationship between the Vietnam stock market and its major trading partners – TECM with bivariate asymmetric GARCH model

This study employs threshold error-correction model with bivariate Glosten–Jagannathan–Runkle-generalized autoregressive conditional heteroscedasticity model to examine the relationship between the Vietnam stock market and its major trading partners, the United States, Japan, Singapore and China. The results indicate that the Vietnam stock market and return risks are influenced by Japan and Singapore stock markets. We also find that the volatility of stock market in Vietnam and its trading countries have an asymmetrical effect. These findings could be valuable to individual investors and financial institutions holding long-run investment portfolios in the Vietnam stock market.     

Integration of world leaders and emerging powers into the Malaysian stock market: A DCC-MGARCH approach

This paper examines the financial integration of two world leaders (the U.S. and Japan) and two emerging powers (China and India) into the Malaysian stock market. A DCC-MGARCH approach is employed to examine the correlations among these countries in a time-variant manner to indicate the degree of financial integration among the countries. It is found that the financial integration between Malaysia and China started to evolve in April 2004. Strong financial integration between the stock markets in India and Malaysia was observed. In contrast, the volatility spillover effect from the U.S. to Malaysia disappeared, especially in the short term. Nevertheless, the study suggests that in the long run, investors in Malaysia could gain by diversifying their portfolios in China and Japan relative to India and the U.S.     

Return spillovers around the globe: A network approach

We study the connectedness of a sample of 40 stock markets across five continents using daily closing prices and return spillovers based on Granger causality. All possible 1560 return spillovers between 40 markets create a complex network of relationships between equity markets around the world. Apart from analyzing the topological and time-varying properties of the created networks, we also identify the determinants of the connectedness of equity markets over time. Adjusting for non-synchronous trading, our modelling approach leads to evidence that the probability of return spillover from a given stock market to other markets increases with market volatility and market size and decreases with higher foreign exchange volatility. We empirically show that the temporal proximity between closing hours is important for information propagation; therefore, choosing markets that trade during similar hours bears an additional risk to investors because the probability of return spillovers increases.     

Asymmetric spillover effects between the Shanghai and Hong Kong stock markets: evidence from quantile lagged regression

This article surveys the asymmetric spillover effects between the mainland China-based Shanghai Composite Index (SCI) and the Hong Kong based Hang Seng Index (HSI) using a quantile lagged regression model. Compared to previous studies, this article, based on data before and after the 2008 global financial crisis, presents a more detailed analysis, as we investigate the spillovers in terms of returns, volatilities and exchange rates between the renminbi (RMB) and the Hong Kong dollar (HKD) throughout the entire conditional return distribution, including the central quantiles, which are closely related to the normal circumstances, and the extreme quantiles, which correspond to the bear and bull markets. First, we find that the return spillovers from its lagged returns or from the other index not only vary across time but also depend on stock state. Second, while return volatility may boost the stock market in a bull market, it accelerates the decline in a bear market. Third, the depreciation of the RMB relative to the HKD does not significantly affect current returns for the HSI, while it negatively affects current returns for the SCI in a bad state after the crisis. The findings presented in this article will facilitate investors’ understanding of the two stock markets.     

Spillovers from the USA to stock markets in Asia: a quantile regression approach

This article analyses return spillovers from the USA to stock markets in Asia by means of quantile regressions. Traditional studies consider spillovers as effects of the conditional means of foreign returns onto the conditional means of chronologically succeeding domestic markets’ returns. We, by contrast, study the full range of quantiles of the conditional distribution of the domestic markets’ returns. This enables us to document the detailed structure of spillovers across return quantiles. Generally, we find spillovers from the USA to Asia to be negative. Specifically, however, we reveal an asymmetric structure of spillovers with an increasing negative magnitude from lower to upper return quantiles. Theoretically, this pattern is consistent with an asymmetric overreaction of traders in Asia to news from the US market. Extensions from the baseline model further suggest the presence of contagion throughout the financial crisis of 2007–2008 as well as of calm-down effects over weekends.     

股指期货限制交易对定价效率影响研究*——基于跨市场信息传递视角的实证

从跨国金融市场信息传递的视角对中国2015年股灾中股指期货限制交易政策实施前后的中美市场实证分析表明：股指期货的限制交易政策极大地增强了美国市场对中国市场的影响，尤其是在下跌行情中的影响更大。分位数回归显示美国市场的交易活动对中国市场开盘价的影响呈“V”型特征，美国市场的微小波动都会引起中国市场的巨大波动，限制交易措施实施后，在下跌行情中来自美国市场的负冲击对中国市场的影响变得更大。这一实证研究的政策含义在于：为了增强股指期货市场的定价效率，金融监管层在市场稳定后应放开股指期货的限制交易，并通过降低准入门槛和合约大小等措施提高市场的开放程度。此外，监管层和国内投资者不能忽视美国股指期货对国内市场的影响。     

FINANCIAL CRISES AND INTERNATIONAL STOCK MARKET VOLATILITY TRANSMISSION

This paper examines the interplay between stock market returns and their volatility, focusing on the Asian and global financial crises of 1997–98 and 2008–09 for Australia, Singapore, the UK, and the US. We use a multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model and weekly data (January 1992–June 2009). Based on the results obtained from the mean return equations, we could not find any significant impact on returns arising from the Asian crisis and more recent global financial crises across these four markets. However, both crises significantly increased the stock return volatilities across all of the four markets. Not surprisingly, it is also found that the US stock market is the most crucial market impacting on the volatilities of smaller economies such as Australia. Our results provide evidence of own and cross ARCH and GARCH effects among all four markets, suggesting the existence of significant volatility and cross volatility spillovers across all four markets. A high degree of time‐varying co‐volatility among these markets indicates that investors will be highly unlikely to benefit from diversifying their financial portfolio by acquiring stocks within these four countries only.     

The Effect of Annual Earnings Announcements on the Chinese Stock Markets

This paper examines the annual earnings announcement effect of the stock markets in China. The investigation is based on events analysis and carried out by modeling the daily changes of stock returns using the M-EGARCH approach, by testing the news effects of annual earnings announcement on the conditional mean of abnormal return and the variance of the returns. It is found that a higher than expected earnings announcement leads to a rise in the conditional mean of stock returns on days before the news announcement and a fall afterwards. The conditional volatility of the changes are significantly reduced by bigger absolute values of reported earnings before the news announcement and increased afterwards, supporting the rejection of semi-strong-form efficiency.     

Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model

The predictability of stock return dynamics is a topic discussed most frequently in empirical studies; however, no unanimous conclusion has yet been reached due to the ignorance of structural changes in stock price dynamics. This study applies various regime switching GJR-GARCH models to analyze the effects of macroeconomic variables (interest rate, dividend yield, and default premium) on stock return movements (including conditional mean, conditional variance, and transition probabilities) in the U.S. stock market, so as to clearly compare the predictive validity of stable and volatile states, as well as compare the in-sample and out-of-sample portfolio performance of regime switching models. The empirical results show that macro factors can affect the stock return dynamics through two different channels, and that the magnitude of their influences on returns and volatility is not constant. The effects of the three economic variables on returns are not time-invariant, but are closely related to stock market fluctuations, and the strength of predictability in a volatile regime is far greater than that in a stable regime. It is found that interest rate and dividend yield seem to play an important role in predicting conditional variance, and out-of-sample performance is largely eroded when the effects of these two factors on volatility are ignored. In addition, the three macro factors do not play any role in predicting transition probabilities.     

Dynamic linkages between Asian stock prices and exchange rates: new evidence from causality in quantiles

This article applies the Granger causality test in quantiles to investigate causal relations between stock returns and exchange rate changes for nine Asian markets over the period 1 January 1997 to 16 August 2010. Our empirical results indicate that the quantile causal relations vary across different quantiles and different periods. Although the causal effects of exchange rate changes on stock returns (or stock returns on exchange rate changes) are heterogeneous across quantiles, the overall evidence suggests that most stock and foreign exchange markets are negatively correlated. The result shows that there are more bidirectional causal relations in accordance with this method than the conventional least square (LS) estimation. The symmetry of these quantile causal effects (the ‘averaging effect’) helps to explain why conventional LS method usually obtains an insignificant result of causality.     

Where does return and volatility come from? The case of Asian ETFs

We analyze return and volatility of Asian iShares traded in the U.S. The difference in trading schedules between the U.S. and Asia offers a unique market setting that allows us to distinguish various return and volatility sources. We find Asian ETFs have higher overnight volatility than daytime volatility, explained by public information released during each local market's trading session. Local Asian markets also play an important role in determining each Asian ETF return. Nonetheless, returns for these funds are highly correlated with U.S. markets, indicative of the effects of investor sentiment and location of trade. Finally, returns in the U.S. market Granger-cause returns in all six Asian markets are analyzed.     

Volatility spillovers across daytime and overnight information between China and world equity markets

This article explores the transmission of daytime and overnight information in terms of returns and volatility between Chinese and Asian, European and North American main stock markets. We propose a bivariate analysis with China as benchmark. By testing the constancy of the conditional correlations, we use an extended constant or dynamic conditional correlation GARCH model. The empirical findings show that across the daytime information transmissions, the relationships between China and Asian markets are closer than China and non-Asian markets, whereas through the overnight information transmissions these relationships are inverse. The analysis provides, before the crisis, that the overnight volatility spillover effects are from China to the United States and the United Kingdom. During the crisis, China affects the United Kingdom in terms of daytime volatility spillovers, whereas in terms of overnight volatility spillovers China affects the United States and is influenced by Japan. After the crisis, daytime volatility spillovers are from Taiwan to China, whereas the overnight volatility spillover effects are from China to the United States and the United Kingdom.     

Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression

This paper investigates the volatility transmission effect between Brent oil futures and stock markets in the major global oil producing and consuming countries – the U.S., Russia, China and Saudi Arabia. In that process, we employ a mixture of novel and elaborate methodologies – wavelet signal decomposing procedure, GARCH model with complex distribution and recently developed robust quantile regression. Our results indicate that the effect is stronger in short-term horizon than in midterm and long-term in most cases. The magnitude is much stronger in turbulent times, whereas in tranquil times, this effect is very weak. We find that Russian RTS index endures the strongest volatility transmission effect from oil market. Surprisingly, Saudi stock market does not suffer heavy spillover effect even in the periods of increased market unrest. In the U.S. and China, the effect is much stronger from stocks to oil than vice-versa, and this particularly applies for the U.S. case.

     

Equity market information and credit risk signaling: A quantile cointegrating regression approach

Investigating linkages between credit and equity markets, we consider daily aggregate U.S. CDS spreads as well as well-chosen equity market and implied volatility indexes over ten years. We describe such robust (to spurious correlation) relationship with the quantile cointegrating regression approach. Such approach handles extreme quantiles/CDS values and their behavior with respect to the equity market's influence. Heteroskedastic patterns such as time-varying variance, but also autocorrelation, skewness and leptokurtosis are captured. Thus, the sensitivity of aggregate CDS spreads to equity market price and volatility channels is accurately measured across quantiles and spreads. Such quantile-dependent sensitivity exhibits asymmetric responses to equity market shocks. A sub-period analysis investigates potential regime shifts in estimated quantile cointegrating regressions. Quantile cointegrating coefficients vary over time and quantiles, and exhibit different magnitudes across sub-periods and spreads. Therefore, the relationship is unstable over time. We also propose a scenario analysis and risk signaling application for credit risk management prospects. Under specific risk levels, credit risky situations are described conditional on the equity market's information over time, and related expected aggregate CDS spreads are computed. Estimated conditional quantiles/CDS spreads act as credit alert triggers.     

Extracting portfolio management strategies from volatility transmission models in regime-changing environments: Evidence from GCC and global markets

Unlike previous studies, this paper uses the Multi-Chain Markov Switching model (MCMS) to examine portfolio management strategies based on volatility transmission between six domestic stock markets of Gulf Arab states (GCC) and global markets (i.e., the U.S. S&P 500 index and oil prices) and compares the results with those of the VAR model. Our volatility approach is range-based and not return-based which is traditionally used in estimating the optimal hedge ratios and portfolio weights. The results demonstrate the relative hedging effectiveness of the MCMS model compared to the VAR. We also highlight the time and regime dependency of the optimal hedge ratios and the portfolio weights for each selected pair of the considered markets conditional on the regime of the same market and the regimes of the other market. Policy implications on portfolio strategies under different states are also discussed.     

后危机时代亚洲地区股票市场风险研究——基于非对称GARCH模型

运用非对称GARCH模型对后危机时代的日本、中国、印度和韩国的股票指数收益率波动性及亚洲各国股票市场的风险进行比较可发现:亚洲地区股票指数收益率的波动呈现出聚集性和持续性,股票市场存在着冲击的非对称性;后危机时代,日本和韩国股市收益与风险不相匹配,反映出发达国家股票市场的波动性显著大于发展中国家,同时,中国股票市场的抗风险能力正在逐步加强,股票市场的信息冲击也趋于平缓。