The bivariate GARCH approach to investigating the relation between stock returns,trading volume,and return volatility

We use a bivariate GJR-GARCH model to investigate simultaneously the contemporaneous and causal relations between trading volume and stock returns and the causal relation between trading volume and return volatility in a one-step estimation procedure, which leads to the more efficient estimates and is more consistent with finance theory. We apply our approach to ten Asian stock markets: Hong Kong, Japan, Korea, Singapore, Taiwan, China, Indonesia, Malaysia, the Philippines, and Thailand. Our major findings are as follows. First, the contemporaneous relation between stock returns and trading volume and the causal relation from stock returns and trading volume are significant and robust across all sample stock markets. Second, there is a positive bi-directional causality between stock returns and trading volume in Taiwan and China and that between trading volume and return volatility in Japan, Korea, Singapore, and Taiwan. Third, there exists a positive contemporaneous relation between trading volume and return volatility in Hong Kong, Korea, Singapore, China, Indonesia, and Thailand, but a negative one in Japan and Taiwan. Fourth, we find a significant asymmetric effect on return and volume volatilities in all sample countries and in Korea and Thailand, respectively.     

Can happiness predict future volatility in stock markets?

In this paper, we use the Twitter based happiness index as a proxy for investor sentiment in order to examine whether happiness influences future market volatility of country VIX indexes. Our sample includes the major stock markets of the USA, Canada, UK, Germany, France, Netherlands, Switzerland, Japan, China, Hong Kong, India, Brazil, South Korea, and South Africa. Using linear and nonlinear causality tests, we find that Twitter happiness significantly causes the future volatility of the sample countries. The robustness checks show no divergence from our primary findings and provide strong evidence of a nonlinear relationship between investor sentiment and future stock market volatility.     

Long memory and level shifts in REITs returns and volatility

This paper provides an empirical investigation of the long memory in the returns and volatility of REITs markets of the USA, the UK, Hong Kong, Australia, and Japan. Initially, we subject the series to unit root tests proposed by Saikkonen and Lütkepohl (2002) and Lanne et al. (2002), which allow for a level shift in the data generating process. We confirm the stationarity of the REITs returns in the presence of structural breaks, with the breaks happening during the 2008 and 2009 periods. Second, by employing long memory tests and estimators, a weak long memory is demonstrated in the return series, but a strong evidence is provided in the volatility measures. Then using Smith (2005)'s modified GPH estimator, we find that a short-memory model with a level shift is a viable alternative to a long memory model for the USA, Hong Kong and Japan and not for the UK nor for Australia. Finally, we confirm that the long memory in volatility is real and not caused by shifts in variance for all markets. Our results should be useful to market participants in the REITs markets, whose success depends on the ability to forecast and model REITs price movements.     

The predictive power of the implied volatility of options traded OTC and on exchanges

This paper investigates the efficiency of stock index options traded over-the-counter (OTC) and on the exchanges in Hong Kong and Japan. Our findings suggest that implied volatility is superior to either historical volatility or a GARCH-type volatility forecast in predicting future volatility in both the OTC and exchange markets. This paper is also one of the first to compare the predictive power of the implied volatility of stock index options traded OTC to that of exchange-traded stock index options. Our evidence suggests that the OTC market is more efficient than the exchanges in Japan, but that the opposite is true in Hong Kong.     

Determinants of returns and volatility of Chinese ADRs at NYSE

This paper examines the determinants of returns and of volatility of the Chinese ADRs as listed at NYSE. Using an autoregressive conditional heteroskedasticity (ARCH) model and data from 16 April 1998 through 30 September 2004, we find that Hong Kong stock market (underlying market), US stock market (host market), and local (Shanghai A and B) markets all are important determinants of returns of the Chinese ADRs. However, the underlying Hong Kong market has the most significant impact on mean returns of the ADRs. In terms of the determinants of the conditional volatility of the ADRs returns, only shocks to the underlying markets are significant. These results are consistent with [Kim, M., Szakmary, A.C., Mathur, I., 2000. Price transmission dynamics between ADRs and their underlying foreign securities. Journal of Banking and Finance 24, 1359–1382] who find that the most influential factor in pricing the ADRs in Japan, UK, Sweden, The Netherlands and Australia is their underlying shares. Implications of the results for investors are discussed.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

Stock return volatility,operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly

This study highlights the link between stock return volatility, operating performance, and stock returns. Prior studies suggest that there is a ‘low volatility’ anomaly, where firms with a low stock return volatility out-perform firms with a high stock return volatility. This paper confirms that low volatility stocks earn higher returns than high volatility stocks in emerging markets and developed markets outside of North America. We also show that low volatility stocks have higher operating returns and this might explain why low volatility stocks earn higher stock returns. These results provide a partial explanation for the ‘low volatility effect’ that is independent from the existence of market anomalies or per se inefficiencies that might otherwise drive a low volatility effect. We emphasize the importance of controlling for stock return volatility when analyzing operating performance and stock performance.     

Common stochastic trends and volatility in Asian-Pacific equity markets

This paper uses Johansen's cointegration test and a modified cointegration test with generalized autoregressive conditional heteroskedasticity (GARCH) effects to examine linkages between the U.S. and five Asian-Pacific stock markets (Australia, Hong Kong, Japan, Malaysia, and Singapore) during the period from 1988 to 1994. The modified cointegration test with GARCH effects is used to assess whether these stock price series share common time-varying volatility. The results indicate that the six stock markets are highly integrated through the second moments of stock returns but not the first moments.     

Stock market integration and volatility spillover: India and its major Asian counterparts

Stock market integration and volatility spillover between India and its major Asian counterparties is studied. Apart from different degrees of correlations, contemporaneous intraday return spillovers between India and its Asian counterparts are found to be positively significant and bi-directional. Hong Kong, Korea, Singapore and Thailand are found to be four Asian markets from where there is significant flow of information in India. Though most of the information gets transmitted between the markets without much delay, some amount of information still remains unsent and is found to be successfully transmitted as soon as the domestic market opens in the next day.     

Simultaneous volatility transmissions and spillover effects: U.S. and Hong Kong stock and futures markets

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.     

Why are stock returns and volatility negatively correlated?

The literature documents that low stock returns are associated with increased volatility, but two competing explanations have proved difficult to disentangle. A negative return increases leverage, making equity value more volatile. However, an increase in volatility that persists causes stock prices to drop. We follow Bekaert and Wu [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42.] in controlling for leverage, but distinguish between volatility regimes that persist from less persistent changes using GARCH. For post-World War II returns on the value-weighted portfolio of all NYSE stocks, we find that changes in the volatility regime are reflected in stock returns but not in GARCH.     

Idiosyncratic volatility and cross‐sectional stock returns in Southeast Asian stock markets

We examine the role of idiosyncratic risk in five ASEAN markets of Malaysia, Singapore, Thailand, Indonesia, and the Philippines. Our research was motivated by the findings of Ang et al. (2006, 2009) of a ‘puzzling’ negative relation between idiosyncratic volatility and 1‐month ahead stock returns in developed markets and the suggestion of the ubiquity of these results in other markets. In contrast, we find no evidence of an idiosyncratic volatility puzzle in these Asian stock markets; instead, we document a positive relationship between idiosyncratic volatility and returns in Malaysia, Singapore, Thailand, and Indonesia and no relationship in the Philippines. The idiosyncratic volatility trading strategy could result in significant trading profits in Malaysia, Singapore, Thailand, and to some extent in Indonesia. Our study underscores the fact that generalizing empirical results obtained in developed stock markets to new and emerging markets could potentially be misleading.     

Option-implied volatility factors and the cross-section of market risk premia

The main goal of this paper is to study the cross-sectional pricing of market volatility. The paper proposes that the market return, diffusion volatility, and jump volatility are fundamental factors that change the investors’ investment opportunity set. Based on estimates of diffusion and jump volatility factors using an enriched dataset including S&P 500 index returns, index options, and VIX, the paper finds negative market prices for volatility factors in the cross-section of stock returns. The findings are consistent with risk-based interpretations of value and size premia and indicate that the value effect is mainly related to the persistent diffusion volatility factor, whereas the size effect is associated with both the diffusion volatility factor and the jump volatility factor. The paper also finds that the use of market index data alone may yield counter-intuitive results.     

Causality in volatility and volatility spillover effects between US, Japan and four equity markets in the South China Growth Triangular

In examining co-movement across international stock markets, previous researchers usually pre-determine the direction of causation and neglect the Chinese equity markets. In this study, we examine the spillover effects of volatility among the two developed markets and four emerging markets in the South China Growth Triangular using Chueng and Ng's causality-in-variance test. Several findings deserve mention: (1) the Japanese stock market affects the US stock market and there is a feedback relationship between the Hong Kong and US stock market. (2) Markets of the SCGT are contemporaneously correlated with the return volatility of the US market. (3) Econometric models constructed according to the results of variance-in-causality tests have greater explanatory power than the conventional GARCH(1,1) model. (4) Using the return volatility of foreign exchange as a proxy for informational arrival can explain excess kurtosis of a stock return series, especially for the less open emerging market. (5) Geographic proximity and economic ties do not necessarily lead to a strong relationship in volatility across markets.     

Firm size and volatility analysis in the Spanish stock market

Using Spanish stock market data, this paper examines volatility spillovers between large and small firms and their impact on expected returns. By using a conditional capital asset pricing model (CAPM) with an asymmetric multivariate GARCH-M covariance structure, it is shown that there exist bidirectional volatility spillovers between both types of companies, especially after bad news. After estimating the model, a positive and significant price of risk is obtained. This result is consistent with the volatility feedback effect, one of the most popular explanations of the asymmetric volatility phenomenon, and explains why risk premiums are much more sensitive to negative return shocks coming from the whole market or other related markets.     

Impact of idiosyncratic volatility on stock returns: A cross-sectional study

This paper proposes a new approach to estimate the idiosyncratic volatility premium. In contrast to the popular two-pass regression method, this approach relies on a novel GMM-type estimation procedure that uses only a single cross-section of return observations to obtain consistent estimates. Also, it enables a comparison of idiosyncratic volatility premia estimated using stock returns with different holding periods. The approach is empirically illustrated by applying it to daily, weekly, monthly, quarterly, and annual US stock return data over the course of 2000–2011. The results suggest that the idiosyncratic volatility premium tends to be positive on daily return data, but negative on monthly, quarterly, and annual data. They also indicate the presence of a January effect.     

Disruption and stock markets: Evidence from Hong Kong

We examine the impact of disruption on stock markets using the 2019 Hong Kong protests for identification. We find that greater protest intensity corresponds to higher bid–ask spreads, lower trading volume, and greater return volatility for dual-listed Chinese firms’ Hong Kong (H) shares but not their home (A) shares. We also document negative abnormal returns only for these firms’ H-shares around major protest events, which shortly after exhibit reversal. Next, we validate our main findings by documenting similar results using Hong Kong-listed firms only. Overall, we provide new evidence highlighting the impact of protest-induced disruption on financial markets.     

Asymmetry in spillover effects: Evidence for international stock index futures markets

The paper investigates the asymmetry in return and volatility spillovers across futures markets with non-overlapping stock exchange trading hours. The transmission of positive and negative return and volatility shocks is analysed for 104 channels of information conveyance identified by combining 9 developed and 11 emerging markets in markets pairs with non-overlapping trading hours. The asymmetric causality test is employed to daily stock index futures returns and volatilities for the period from 03 October 2010 to 03 October 2014. The paper sheds light on the relatively little explored concept of asymmetry in return and volatility spillovers across markets, providing novel evidence on stabilizing and destabilizing spillover effects.     

Asymmetry in return and volatility spillover between equity and bond markets in Australia

We document asymmetry in return and volatility spillover between equity and bond markets in Australia for daily returns during the period 1992–2006 using a bivariate GARCH modelling approach. Negative bond market returns spillover into lower stock market returns whereas good news originating in the equity market leads to lower bond returns. Bond market volatility spills over into the equity market but the reverse is not true. Transmission of bond volatility into equity volatility depends in a complex way upon the respective signs of the return shocks in each market.     

A High‐Frequency Investigation of the Interaction between Volatility and DAX Returns

One of the most noticeable stylised facts in finance is that stock index returns are negatively correlated with changes in volatility. The economic rationale for the effect is still controversial. The competing explanations have different implications for the origin of the relationship: Are volatility changes induced by index movements, or inversely, does volatility drive index returns? To differentiate between the alternative hypotheses, we analyse the lead‐lag relationship of option implied volatility and index return in Germany based on Granger causality tests and impulse‐response functions. Our dataset consists of all transactions in DAX options and futures over the time period from 1995 to 2005. Analyzing returns over 5‐minute intervals, we find that the relationship is return‐driven in the sense that index returns Granger cause volatility changes. This causal relationship is statistically and economically significant and can be clearly separated from the contemporaneous correlation. The largest part of the implied volatility response occurs immediately, but we also observe a smaller retarded reaction for up to one hour. A volatility feedback effect is not discernible. If it exists, the stock market appears to correctly anticipate its importance for index returns.