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.     

Evaluating the Forecast Accuracy of Emerging Market Stock Returns

This paper analyzes the forecast performance of emerging market stock returns using standard autoregressive moving average (ARMA) and more elaborated autoregressive conditional heteroskedasticity (ARCH) models. Our results indicate that the ARMA and ARCH specifications generally outperform random walk models. Models that allow for asymmetric shocks to volatility are better for in-sample estimation (threshold autoregressive conditional heteroskedasticity for daily returns and exponential generalized autoregressive conditional heteroskedasticity for longer periods), and ARMA models are better for out-of-sample forecasts. The results are valid using both U. S. dollar and domestic currencies. Overall, the forecast errors of each Latin American market can be explained by the forecasts of other Latin American markets and Asian markets. The forecast errors of each Asian market can be explained by the forecasts of other Asian markets, but not by Latin American markets. Our predictability results are economically significant and may be useful for portfolio managers to enter or leave the market.     

Asymmetries of the intraday return-volatility relation

This study investigates the asymmetry of the intraday return-volatility relation at different return horizons ranging from 1, 5, 10, 15, up to 60 min and compares the empirical results with results for the daily return horizon. Using data on the S&P 500 (SPX) and the VIX from September 25, 2003 to December 30, 2011 and a Quantile-Regression approach, we observe strong negative return-volatility relation over all return horizons. However, this negative relation is asymmetric in three different aspects. First, the effects of positive and negative returns on volatility are different and more pronounced for negative returns. Second, for both positive and negative returns, the effect is conditional on the distribution of volatility changes. The absolute effect is up to five times larger in the extreme tails of the distribution. Third, at the intraday level, there is evidence of both autocorrelation in volatility changes and cross-autocorrelation with returns. This lead-lag relation with returns is also very asymmetric and more pronounced in the tails of the distribution. These effects are, however, not observed at the daily return horizon.     

Using implied volatility on options to measure the relation between asset returns and variability

Prior research has documented that volatility in financial asset markets is most directly related to trading rather than calendar days, and that there is an inverse asymmetric relation between volatility and returns in both stocks and long-term bonds. We examine these relations in 37 futures options markets representing a wide variety of asset types. Using futures prices and implied volatilities from this extensive array of markets, we confirm that in all of them, save one, market volatility is more directly related to trading days. However, the nature of the association between implied volatility and underlying asset returns varies greatly across asset categories and across exchanges. Thus, we show that findings from equity markets apparently are not generalizable to other asset classes.     

Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis

This paper investigates the relationship between investor attention and the major cryptocurrency markets by wavelet-based quantile Granger causality. The wavelet analysis illustrates the interdependence between investor attention and the cryptocurrency returns. Multi-scale quantile Granger causality based on wavelet decomposition further demonstrates bidirectional Granger causality between investor attention and the returns of Bitcoin, Ethereum, Ripple and Litecoin for all quantiles, except for the medium. Among them, the Granger causality from investor attention to the returns is relatively very weak for Ethereum. In the short term, the Granger causality from these cryptocurrency returns to investor attention seems symmetric, but in the medium- and long- term, the causality shows some asymmetry. The Granger causality from investor attention to these cryptocurrency returns is asymmetric and varies across cryptocurrencies and time scales. Specifically, investor attention has a relatively stronger impact on the cryptocurrency returns in bearish markets than that in bullish markets in the short term.     

An inverted U‐shaped crude oil price return‐implied volatility relationship

We examined the return–volatility relationship for USO ETF oil price return and CBOE Crude Oil ETF Volatility Index, OVX. The data for the USO and OVX covers the period covering May 11, 2007 to February 28, 2013. Our OLS regression results suggest evidence of regular feedback and leverage effects. When we employ linear quantile regression techniques, we find evidence of regular and inverse feedback effects. The inverse feedback effects being noticeable in the upper quantile region of the oil return distribution. There is also support for a regular leverage effect in USO prices. We also examined the return–volatility relationship using quantile regression copula methods for measuring the degree of asymmetry in the relationships between the oil price return and implied volatility. The results of the analysis indicate, first, that there exists a negative relationship between contemporaneous oil VIX and USO ETF oil returns. Second, that the relationship between oil returns and implied volatilities depends on the quartile at which the relationship is being investigated. Third, there exists an inverted U-shaped dependency relationship between returns and implied volatilities across quantiles. Fourth, though an inverted U-shape exists, the shape is different from those observed in stock markets.     

Volatility clustering,leverage effects,and jump dynamics in the US and emerging Asian equity markets

This paper proposes asymmetric GARCH-Jump models that synthesize autoregressive jump intensities and volatility feedback in the jump component. Our results indicate that these models provide a better fit for the dynamics of the equity returns in the US and emerging Asian markets, irrespective whether the volatility feedback is generated through a common GARCH multiplier or a separate measure of volatility in the jump intensity function. We also find that they can capture several distinguishing features of the return dynamics in emerging markets, such as, more volatility persistence, less leverage effects, fatter tails, and greater contribution and variability of the jump component.     

Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices' daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.     

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.     

Daily volatility behavior in Chinese futures markets

This study examines the daily volatility of four futures contracts on Chinese futures exchanges (copper, mungbeans, soybeans and wheat). We find that returns have asymmetric effects on volatility, meaning that negative returns have a greater effect on volatility than positive returns do. Volume is positively related to volatility, open interest is negatively related to volatility, and the extent of large-volume traders’ participation is also positively related to volatility. We conjecture that the global patterns of volatility relationship, which have become more pronounced in Chinese markets in more recent years, are attributable to the results of ongoing government attempts to achieve transparency and better disclosure.     

Risk perception in financial markets: On the flip side

We propose an alternative approach to capture the asymmetric risk-return relationship in financial markets using affective cognitive analysis. Implied volatility is employed as a robust gauge of risk perception. Markets exhibit a dramatic increase in fear sentiment when extreme upper-quantile losses hit investors while conditional positive returns fuel exuberance. However, an inverse response is observed in Asian markets due to normative societal phenomena, such as herding. A cognitive paradigm provides with a better interpretation of contagion than classical leverage-feedback theories as risk perception evolves dynamically over time. Overall, the fear of losses is not the flip side of gains' exuberance.     

A behavioural explanation to the asymmetric volatility phenomenon: Evidence from market volatility index

This study examines how the behavioural explanations, in particular loss aversion, can be used to explain the asymmetric volatility phenomenon by investigating the relationship between stock market returns and changes in investor perceptions of risk measured by the volatility index. We study the behaviour of India volatility index vis‐à‐vis Hong Kong, Australia and UK volatility index, and provide a comprehensive comparative analysis. Using Bai‐Perron test, we identify structural breaks and volatility regimes in the time series of volatility index, and investigate the volatility index‐return relation during high, medium and low volatility periods. Regardless of volatility regimes, we find that volatility index moves in opposite direction in response to stock index returns, and contemporaneous return is the most dominating across the four markets. The negative relation is strongest for UK followed by Australia, Hong Kong and India. Second, volatility index reacts significantly different to positive and negative returns; negative return has higher impact on changes in volatility index than positive return across the markets over full‐sample and sub‐sample periods. The asymmetric effect is stronger in low volatility regime than in high and medium volatility periods for all the markets except UK. The strength of asymmetric effect is strongest for Hong Kong and weakest for India. Finally, negative returns have exponentially increasing effect and positive returns have exponentially decreasing effect on the changes in volatility index.     

Financial market interdependencies: A quantile regression analysis of volatility spillover

This paper investigates the degree and structure of interdependence between emerging (Asian and Latin American) and developed (USA and Japan) stock markets through the study of volatility spillovers for the period spanning from January 1, 1993 to October 13, 2010. Using both standard GARCH model and quantile regression approach, we find the evidence of significant interdependence between financial markets which may give evidence of volatility transmission. The volatility transmission is closely associated with geographical proximity as well as with crisis periods which confirm the presence of contagion. The analysis of upper and lower quantiles allows observing that the interdependence increases during bullish markets while decreases during bearish markets. Accordingly, the structure of interdependence is asymmetric for both Asian and Latin American emerging markets. These findings open up new insights for government policy makers as well as for managerial purposes.     

Modelling the asymmetric linkages between spot gold prices and African stocks

We model the asymmetric linkages between returns of spot gold prices and African stock markets using wavelets and quantile regression techniques. The maximal overlap discrete wavelet transform technique was employed to decompose the returns into short-, medium-, and long-term series and the quantile regression was employed to explore the nexus by matching their conditional distributions along 0.05 quantile intervals. We find that the relationship between gold and African stocks is frequency-dependent and asymmetric in nature across the various timescales and quantiles. We find a mixture of negative and positive connections across the various quantiles in the short- and medium-terms. In the long-term, whereas the effect of gold is positive for Ghana, Mauritius, and Nigeria; it is negative for Egypt, Morocco, South Africa, and Tunisia. The results possess important implications for risk management as dependencies are not only studied over the entire conditional distribution at once but based on quantiles and further at different frequencies. Investors can make well-informed decisions to mitigate trade risks as they closely match the time heterogeneity in the markets.     

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.     

Forecasting the return distribution using high-frequency volatility measures

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1, 1) model when the 30-year Treasury bond futures return is considered.     

Weather Effects on Stock Returns and Volatility in South Asian Markets

We study the effect of mood-proxy variables on index returns and volatility in six South Asian markets. Our mood-proxy variables include six weather (temperature, humidity, cloud cover, air pressure, visibility, and wind speed), three weather indicator variables (fog, thunder storm and rain or drizzle) and two biorhythmic variables (SAD and lunar phases). We adopt a robust approach and attempt to select the best parsimonious econometric model for each market. Our findings suggest that mood-proxy variables have some convincing influences in South Asian capital markets. In some instances, these variables are influencing returns while in other instances they are influencing volatility.     

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.     

Firm fundamentals and stock prices in emerging Asian stock markets: some panel data evidence

The purpose of this paper is to investigate the direct link between firm fundamentals and stock prices in a set of emerging Asian stock markets using firm-level panel data. In doing so, we explore the relationship between firm-specific variations in stock returns and firm fundamentals in the context of a simple present value framework. We find that alternative proxies of variation in firm fundamentals—albeit at differing degrees—explain a significant part of firm-specific return variation in a majority of emerging markets in Asia. Findings are robust to the influence of other factors known to affect stock return volatility (e.g. firm size, stock turnover, and leverage). Overall results suggest that stock prices in a majority of the Asian emerging markets contain a significant amount of firm-specific fundamental information and are, therefore, not as murky as commonly thought.     

Market States and the Effect on Equity REIT Returns due to Changes in Monetary Policy Stance

This study investigates the effect of changes in monetary policy on US equity real estate investment trust (EREIT) returns in lower and higher return ranges during bull, bear, and volatile stock market states using quantile regression. Results show that EREIT returns are sensitive to changes in monetary policy at different EREIT return ranges in different market states. During bull markets, changes in monetary policy have a significant negative impact on EREIT when investors have lower expectations of real estate price increases, but are not effective when investors have higher expectations of real estate price increases. During volatile and bear markets, EREIT returns are not sensitive to changes in monetary policy stance. Results also show that EREIT returns respond positively to stock returns in various states and conditions.