Dependence between the global gold market and emerging stock markets (E7+1): Evidence from Granger causality using quantile and quantile‐on‐quantile regression methods

This study examined the dependence between gold and stocks during 2002–18 in seven emerging countries. The study combined the bivariate cross‐quantilogram introduced recently with quantile‐on‐quantile regression (QQR) approaches to conduct comprehensive and complementary analyses. The QQR results for the full sample revealed a weak positive dependence in all the quantiles of gold and stock returns across all the countries selected during mild market conditions. The results for pre and post‐crisis periods largely were consistent with those obtained for the full sample, except for Turkey (pre‐crisis), and China and Indonesia (post‐crisis). The results of the causality test‐in‐mean (return) and that of the causality test‐in‐variance revealed no causal relation between stock and gold in the pre‐crisis period, while causality ran only from gold to some stocks in the post‐crisis period. Further, while there was volatility causality running only from gold to stocks during the pre‐crisis period, the volatility causality between the two markets was very high during the post‐crisis period. Therefore, we suggest that gold may have been a hedge for stocks during the pre‐crisis compared to the post‐crisis period. Further, international risk factors should be considered in optimal investment decisions between domestic and global markets' assets (stocks and gold).     

Asymmetric Relationship between Investors' Sentiment and Stock Returns: Evidence from a Quantile Non‐causality Test

This study investigates the causal relationship between investor sentiment and stock returns in the USA by conducting a quantile Granger non‐causality test. Employing two proxies for investor sentiment – the sentiment index developed by Baker and Wurgler in 2007 and the University of Michigan Consumer Survey, a consumer confidence index – we find that the causal relationship between investor sentiment and stock returns strengthens when a tail quantile interval is considered. This finding implies that the investor sentiment could provide the incremental predictability for the stock returns under the extreme market situation, which cannot be found using a traditional Granger causality test. Interestingly, the findings can be explained by investors' loss aversion and herding behavior.     

Quantile Regression Analysis of the Asymmetric Return‐Volatility Relation

We use quantile regression to investigate the short‐term return‐volatility relation between stock index returns and changes in implied volatility index. Neither the leverage hypothesis nor the volatility feedback hypothesis effectively explains the asymmetric return‐volatility relation. Instead, behavioral explanations, such as the affect and representativeness heuristics, are supported by our results, particularly in the short‐term; the affect heuristic plays an important role. Moreover, in the context of an extreme volatility change distribution, the affect heuristic and time‐pressure dominate. Thus, we observe strong negative and asymmetric relations between each volatility index and its corresponding stock market index. The asymmetry increases monotonically from the median quantile to the uppermost quantile (i.e., 95%); therefore, ordinary least squares (OLS) regression underestimates this relation at upper quantiles. Additionally, the VIX presents the highest asymmetric return‐volatility relation, followed by the VSTOXX, VDAX, and VXN. Finally, the observed asymmetry is more pronounced with the new volatility index measure than with the old, at‐the‐money volatility index measure. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 33:235–265, 2013     

Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings*

In this paper we analyze whether presidential approval ratings can predict the S&P 500 returns over the monthly period of July 1941 to April 2018, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC‐MGARCH) model. Our results show that standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from presidential approval ratings to stock returns cannot be considered reliable. When we use the DCC‐MGARCH model, which is robust to such misspecifications, in 69% of the sample period, approval ratings in fact do strongly predict the S&P 500 stock return. Moreover, using the DCC‐MGARCH model we find that presidential approval rating is also a strong predictor of the realized volatility of S&P 500. Overall, our results highlight that presidential approval ratings is helpful in predicting stock return and volatility, when one accounts for nonlinearity and regime changes through a robust time‐varying model.     

Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility

Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time‐series models based on historical daily returns. In this study both the measurement and the forecasting of financial volatility is improved using high‐frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with and even outperform implied volatility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1005–1028, 2004     

Measuring and forecasting S&P 500 index‐futures volatility using high‐frequency data

In the 24‐hr foreign exchange market, Andersen and Bollerslev measure and forecast volatility using intraday returns rather than daily returns. Trading in equity markets only occurs during part of the day, and volatility during nontrading hours may differ from the volatility during trading hours. This paper compares various measures and forecasts of volatility in equity markets. In the absence of overnight trading it is shown that the daily volatility is best measured by the sum of intraday squared 5‐min returns, excluding the overnight return. In the absence of overnight trading, the best daily forecast of volatility is produced by modeling overnight volatility differently from intraday volatility. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:497–518, 2002     

How do Independent Directors Influence Corporate Risk‐Taking? Evidence from a Quasi‐Natural Experiment

Motivated by agency theory, we explore the effect of independent directors on corporate risk taking. To minimize endogeneity, we exploit the passage of the Sarbanes–Oxley Act as an exogenous shock that raises board independence. Our difference‐in‐difference estimates show that board independence diminishes risk‐taking significantly, as evidenced by the substantially lower volatility in the stock returns. In particular, board independence reduces total risk and idiosyncratic risk by 24.87% and 12.60%, respectively. The evidence is consistent with the notion that board independence represents an effective governance mechanism that prevents managers from taking excessive risk. Additional analysis based on propensity score matching also confirms our results. Our research design is based on a natural experiment and is far more likely to show causality, rather than merely an association.     

Examining the Return–Volatility Relation for Foreign Exchange: Evidence from the Euro VIX

We compare the return–volatility relation for the euro currency to the equivalent relation for the equity market, examining the sign, symmetry, and strength of the relation. We employ the euro‐currency exchange‐traded fund (FXE) and its associated option implied volatility index (the EVZ), whereas previous studies only employ equities and/or realized volatility. The equity studies find a negative asymmetric return–volatility relation for implied volatility, with a strong relation when large market movements occur. We find that the euro return–volatility relation can possess either a positive or negative sign, is asymmetric, and has a weaker relation. Thus, the sign and strength of the euro relation differs from the equivalent equity relation. Our quantile regressions show that both the positive and negative contemporaneous returns of the euro result in increased volatility in the extreme quantiles of the conditional distribution, with the contemporaneous effect showing a stronger relation when the euro depreciates. We also find that the volume of the euro‐currency ETF options affects the return–volatility relation for the euro ETF. Overall, the results here expand the concept originally restricted to equities, with the surprising results that the return‐implied volatility relation is weaker and the asymmetric return sometimes is positive for the euro currency. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 34:74–92, 2014     

Asymmetric pricing of implied systematic volatility in the cross‐section of expected returns

Assuming a symmetric relation between returns and innovations in implied market volatility, Ang, A., Hodrick, R., Xing, Y., and Zhang, X. (2006) find that sensitivities to changes in implied market volatility have a cross‐sectional effect on firm returns. Dennis, P., Mayhew, S., and Stivers, C. (2006), however, find an asymmetric relation between firm‐level returns and implied market volatility innovations. We incorporate this asymmetry into the cross‐sectional relation between sensitivity to volatility innovations and returns. Using both portfolio sorting and firm‐level regressions, we find that sensitivity to VIX innovations is negatively related to returns when volatility is rising, but is unrelated when it is falling. The negative relation is robust to controls for other variables, suggesting only the increase in implied market volatility is a priced risk factor. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:34–54, 2011     

Fama–French in China: Size and Value Factors in Chinese Stock Returns

We investigate the size and value factors in the cross‐section of returns for the Chinese stock market. We find a significant size effect but no robust value effect. A zero‐cost small‐minus‐big (SMB) portfolio earns an average premium of 0.61% per month, which is statistically significant with a t‐value of 2.89 and economically important. In contrast, neither the market portfolio nor the zero‐cost high‐minus‐low (HML) portfolio has average premiums that are statistically different from zero. In both time‐series regressions and Fama–MacBeth cross‐sectional tests, SMB represents the strongest factor in explaining the cross‐section of Chinese stock returns. Our results contradict several existing studies which document a value effect. We show that this difference comes from the extreme values in a few months in the early years of the market with a small number of stocks and high volatility. Their impact becomes insignificant with a longer sample and proper volatility adjustment.     

Hedging and value at risk: A semi‐parametric approach

The non‐normality of financial asset returns has important implications for hedging. In particular, in contrast with the unambiguous effect that minimum‐variance hedging has on the standard deviation, it can actually increase the negative skewness and kurtosis of hedge portfolio returns. Thus, the reduction in Value at Risk (VaR) and Conditional Value at Risk (CVaR) that minimum‐variance hedging generates can be significantly lower than the reduction in standard deviation. In this study, we provide a new, semi‐parametric method of estimating minimum‐VaR and minimum‐CVaR hedge ratios based on the Cornish‐Fisher expansion of the quantile of the hedged portfolio return distribution. Using spot and futures returns for the FTSE 100, FTSE 250, and FTSE Small Cap equity indices, the Euro/US Dollar exchange rate, and Brent crude oil, we find that the semiparametric approach is superior to the standard minimum‐variance approach, and to the nonparametric approach of Harris and Shen (2006). In particular, it provides a greater reduction in both negative skewness and excess kurtosis, and consequently generates hedge portfolios that in most cases have lower VaR and CVaR. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:780–794, 2010     

我国股票市场收益、交易量、波动性动态关系的实证分析

本文对我国股票市场上证指数和深圳成指的收益、交易量、波动性之间的动态关系进行了实证研究,研究结果表明:收益和绝对收益与交易量之间均存在正相关关系;收益与交易量以及绝对收益与交易量之间存在双向Granger因果关系(线性或非线性);深圳成指收益的波动方差对收益具有正向作用,而上证指数收益的波动方差对收益没有直接的影响;上证指数和深圳成指的成交量对股指收益的波动方差不具有解释作用.     

The relationship between currency carry trades and U.S. stocks

The article examines the relationship between daily returns of currency carry trades and U.S. stocks from January 1995 through September 2010. Carry trade and stock returns are highly correlated with no Granger‐causality in either direction. An EGARCH model shows that significant volatility spillovers flow from the stock market to the carry‐trade market, but not vice versa. The markets are more correlated in periods of high volatility. Volatilities in both markets also increase more with negative innovations than positive innovations. A sectoral analysis of the index suggests that volatilities of cyclical stocks have more impact than noncyclical stocks on carry trades.     

Regime‐switching in stock index and Treasury futures returns and measures of stock market stress

We investigate bivariate regime‐switching in daily futures‐contract returns for the US stock index and ten‐year Treasury notes over the crisis‐rich 1997–2005 period. We allow the return means, volatilities, and correlation to all vary across regimes. We document a striking contrast between regimes, with a high‐stress regime that exhibits a much higher stock volatility, a much lower stock–bond correlation, and a higher mean bond return. The high‐stress regime is associated with higher average values of stock‐implied volatility, stock illiquidity, and stock and bond futures trading volume. The lagged implied volatility from equity‐index options is useful in modeling the time‐varying transition probabilities of the regime‐switching process. Our findings support the notions that: (1) stock market stress can have a material influence on Treasury bond pricing, and (2) the diversification benefits of combined stock–bond holdings tend to be greater during times with relatively high stock market stress. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:753–779, 2010     

Empirical tests of canonical nonparametric American option‐pricing methods

Alcock and Carmichael (2008, The Journal of Futures Markets, 28, 717–748) introduce a nonparametric method for pricing American‐style options, that is derived from the canonical valuation developed by Stutzer (1996, The Journal of Finance, 51, 1633–1652). Although the statistical properties of this nonparametric pricing methodology have been studied in a controlled simulation environment, no study has yet examined the empirical validity of this method. We introduce an extension to this method that incorporates information contained in a small number of observed option prices. We explore the applicability of both the original method and our extension using a large sample of OEX American index options traded on the S&P100 index. Although the Alcock and Carmichael method fails to outperform a traditional implied‐volatility‐based Black–Scholes valuation or a binomial tree approach, our extension generates significantly lower pricing errors and performs comparably well to the implied‐volatility Black–Scholes pricing, in particular for out‐of‐the‐money American put options. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:509–532, 2010     

MCMC ESTIMATION OF LÉVY JUMP MODELS USING STOCK AND OPTION PRICES

We examine the performances of several popular Lévy jump models and some of the most sophisticated affine jump‐diffusion models in capturing the joint dynamics of stock and option prices. We develop efficient Markov chain Monte Carlo methods for estimating parameters and latent volatility/jump variables of the Lévy jump models using stock and option prices. We show that models with infinite‐activity Lévy jumps in returns significantly outperform affine jump‐diffusion models with compound Poisson jumps in returns and volatility in capturing both the physical and risk‐neutral dynamics of the S&P 500 index. We also find that the variance gamma model of Madan, Carr, and Chang with stochastic volatility has the best performance among all the models we consider.     

Aggregate Volatility and Market Jump Risk: An Option‐Based Explanation to Size and Value Premia

It is well documented that stock returns have different sensitivities to changes in aggregate volatility, however less is known about their sensitivity to market jump risk. By using S&P 500 crash‐neutral at‐the‐money straddle and out‐of‐money put returns as proxies for aggregate volatility and market jump risk, I document significant differences between volatility and jump loadings of value versus growth, and small versus big portfolios. In particular, small (big) and value (growth) portfolios exhibit negative (positive) and significant volatility and jump betas. I also provide further evidence that both volatility and jump risk factors are priced and negative. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 34:34–55, 2014     

Time‐varying jump risk premia in stock index futures returns

This study tests the presence of time‐varying risk premia associated with extreme news events or jumps in stock index futures return. The model allows for a dynamic jump component with autoregressive jump intensity, long‐range dependence in volatility dynamics, and a volatility in mean structure separately for the normal and extreme news events. The results show significant jump risk premia in four stock market index futures returns including the DAX, FTSE, Nikkei, and S&P500 indices. Our results are robust to various specifications of conditional variance including the plain GARCH, component GARCH, and Fractionally Integrated GARCH models. We also find the time‐varying risk premium associated with normal news events is not significant across all indices. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark 32:639–659, 2012     

中国股票市场行为与投资者情绪

The relationship among stock returns, market volatilities and individual investor sentiment is an important topic in behavioral economics and finance. This paper uses a unique data set—China’s newly opened stock trading accounts to test the relationships among stock returns, volatilities and individual investor sentiment in the Chinese stock market. It is found that there is a positive relationship between shifts in sentiment and stock returns, and that shifts in sentiment are negatively correlated with market volatility, that is to say, volatility increases (decreases) when investors become more bearish (bullish).      

Volatility Spillovers and the Risk-Return Relation Between Stock and Foreign Exchange Markets in Brazil

We examine the evidence of mean and volatility spillovers between stock and foreign exchange markets in Brazil with multivariate GARCH models and nonlinear Granger causality tests. We also use a multivariate GARCH-in-mean model to assess the relationship between risk and return in these markets. The results indicate that the stock market leads the foreign exchange market in price formation and that nonlinear Granger causalities from the exchange market to the stock market do occur. Part of these nonlinear causalities are explained by volatility spillovers. We show that exchange rate volatility affects not only stock market volatility but also stock returns.