A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

This study is the first to examine the impacts of overnight and intraday oil futures cross-market information on predicting the US stock market volatility the high-frequency data. In-sample estimations present that high overnight oil futures RV can lead to high RV of the S&P 500. Moreover, negative overnight returns are more powerful than positive components, implying the existence of the leverage effect. From statistical and economic perspectives, out-of-sample results indicate that the decompositions of overnight oil futures and intraday RVs, based on signed intraday returns, can significantly increase the models' predictive ability. Finally, when considering the US stock market overnight effect, the decompositions are still useful to predict volatility, especially during high US stock market fluctuations and high and low EPU states.     

Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model

A recent study by Rapach, Strauss, and Zhou (Journal of Finance, 2013, 68(4), 1633–1662) shows that US stock returns can provide predictive content for international stock returns. We extend their work from a volatility perspective. We propose a model, namely a heterogeneous volatility spillover–generalized autoregressive conditional heteroskedasticity model, to investigate volatility spillover. The model specification is parsimonious and can be used to analyze the time variation property of the spillover effect. Our in‐sample evidence shows the existence of strong volatility spillover from the US to five major stock markets and indicates that the spillover was stronger during business cycle recessions in the USA. Out‐of‐sample results show that accounting for spillover information from the USA can significantly improve the forecasting accuracy of international stock price volatility.     

A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility

In a conditional predictive ability test framework, we investigate whether market factors influence the relative conditional predictive ability of realized measures (RMs) and implied volatility (IV), which is able to examine the asynchronism in their forecasting accuracy, and further analyze their unconditional forecasting performance for volatility forecast. Our results show that the asynchronism can be detected significantly and is strongly related to certain market factors, and the comparison between RMs and IV on average forecast performance is more efficient than previous studies. Finally, we use the factors to extend the empirical similarity (ES) approach for combination of forecasts derived from RMs and IV.     

Forecasting volatility of emerging stock markets: linear versus non‐linear GARCH models

ARCH and GARCH models are substantially used for modelling volatility of time series data. It is proven by many studies that if variables are significantly skewed, linear versions of these models are not sufficient for both explaining the past volatility and forecasting the future volatility. In this paper, we compare the linear(GARCH(1,1)) and non‐linear(EGARCH) versions of GARCH model by using the monthly stock market returns of seven emerging countries from February 1988 to December 1996. We find that for emerging stock markets GARCH(1,1) model performs better than EGARCH model, even if stock market return series display skewed distributions. Copyright © 2000 John Wiley & Sons, Ltd.     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

Moving beyond Volatility Index (VIX): HARnessing the term structure of implied volatility

This paper considers how information from the implied volatility (IV) term structure can be harnessed to improve stock return volatility forecasting within the state-of-the-art HAR model. Factors are extracted from the IV term structure and included as exogenous variables in the HAR framework. We found that including slope and curvature factors leads to significant forecast improvements over the HAR benchmark at a range of forecast horizons, compared with the standard HAR model and HAR model with VIX as IV information set.     

The predictive value of temporally disaggregated volatility: evidence from index futures markets

This paper examines the benefits to forecasters of decomposing close-to-close return volatility into close-to-open (nighttime) and open-to-close (daytime) return volatility. Specifically, we consider whether close-to-close volatility forecasts based on the former type of (temporally aggregated) data are less accurate than corresponding forecasts based on the latter (temporally disaggregated) data. Results obtained from seven different US index futures markets reveal that significant increases in forecast accuracy are possible when using temporally disaggregated volatility data. This result is primarily driven by the fact that forecasts based on such data can be updated as more information becomes available (e.g., information flow from the preceding close-to-open/nighttime trading session). Finally, we demonstrate that the main findings of this paper are robust to the index futures market considered, the way in which return volatility is constructed, and the method used to assess forecast accuracy. Copyright © 2008 John Wiley & Sons, Ltd.     

Trading volume and realized volatility forecasting: Evidence from the China stock market

The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume-volatility nexus by decomposing and reconstructing the trading activity into short-run components that typically represent irregular information flow and long-run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out-of-sample realized volatility (RV) forecasts. This finding is robust both in one-step ahead and multiple-step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume-volatility linkage to EMD-HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy.     

Forecasting Stock Market Volatility in Central and Eastern European Countries

In recent years, considerable attention has focused on modelling and forecasting stock market volatility. Stock market volatility matters because stock markets are an integral part of the financial architecture in market economies and play a key role in channelling funds from savers to investors. The focus of this paper is on forecasting stock market volatility in Central and East European (CEE) countries. The obvious question to pose, therefore, is how volatility can be forecast and whether one technique consistently outperforms other techniques. Over the years a variety of techniques have been developed, ranging from the relatively simple to the more complex conditional heteroscedastic models of the GARCH family. In this paper we test the predictive power of 12 models to forecast volatility in the CEE countries. Our results confirm that models which allow for asymmetric volatility consistently outperform all other models considered. Copyright © 2011 John Wiley & Sons, Ltd.     

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

In this study, we explore the effect of cojumps within the agricultural futures market, and cojumps between the agricultural futures market and the stock market, on stock volatility forecasting. Also, we take into account large and small components of cojumps. We have several noteworthy findings. First, large jumps may lead to more substantial fluctuations and are more powerful than small jumps. The effect of cojumps and their decompositions on future volatility are mixed. Second, a model including large and small cojumps between the agricultural futures market and the stock market can achieve a higher forecasting accuracy, implying that large and small cojumps contain more useful predictive information than cojumps themselves. Third, our conclusions are robust based on various robustness tests such as the realized kernel, expanding forecasts, different forecasting windows, different jump tests, and different threshold values.     

Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

Stock market volatility and the forecasting performance of stock index futures

This study attempts to apply the general equilibrium model of stock index futures with both stochastic market volatility and stochastic interest rates to the TAIFEX and the SGX Taiwan stock index futures data, and compares the predictive power of the cost of carry and the general equilibrium models. This study also represents the first attempt to investigate which of the five volatility estimators can enhance the forecasting performance of the general equilibrium model. Additionally, the impact of the up‐tick rule and other various explanatory factors on mispricing is also tested using a regression framework. Overall, the general equilibrium model outperforms the cost of carry model in forecasting prices of the TAIFEX and the SGX futures. This finding indicates that in the higher volatility of the Taiwan stock market incorporating stochastic market volatility into the pricing model helps in predicting the prices of these two futures. Furthermore, the comparison results of different volatility estimators support the conclusion that the power EWMA and the GARCH(1,1) estimators can enhance the forecasting performance of the general equilibrium model compared to the other estimators. Additionally, the relaxation of the up‐tick rule helps reduce the degree of mispricing. Copyright © 2008 John Wiley & Sons, Ltd.     

Are industry‐level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index

Effectively explaining and accurately forecasting industrial stock volatility can provide crucial references to develop investment strategies, prevent market risk and maintain the smooth running of national economy. This paper aims to discuss the roles of industry‐level indicators in industrial stock volatility. Selecting Chinese manufacturing purchasing managers index (PMI) and its five component PMI as the proxies of industry‐level indicators, we analyze the contributions of PMI on industrial stock volatility and further compare the volatility forecasting performances of PMI, macroeconomic fundamentals and economic policy uncertainty (EPU), by constructing the individual and combination GARCH‐MIDAS models. The empirical results manifest that, first, most of the PMI has significant negative effects on industrial stock volatility. Second, PMI which focuses on the industrial sector itself is more helpful to forecast industrial stock volatility compared with the commonly used macroeconomic fundamentals and economic policy uncertainty. Finally, the combination GARCH‐MIDAS approaches based on DMA technique demonstrate more excellent predictive abilities than the individual GARCH‐MIDAS models. Our major conclusions are robust through various robustness checks.     

Forecasting exchange rate volatility: a multiple horizon comparison using historical,realized and implied volatility measures

Recent studies suggest realized volatility provides forecasts that are as good as option‐implied volatilities, with improvement stemming from the use of high‐frequency data instead of a long‐memory specification. This paper examines whether volatility persistence can be captured by a longer dataset consisting of over 15 years of intra‐day data. Volatility forecasts are evaluated using four exchange rates (AUD/USD, EUR/USD, GBP/USD, USD/JPY) over horizons ranging from 1 day to 3 months, using an expanded set of short‐range and long‐range dependence models. The empirical results provide additional evidence that significant incremental information is found in historical forecasts, beyond the implied volatility information for all forecast horizons. Copyright © 2008 John Wiley & Sons, Ltd.     

A threshold factor multivariate stochastic volatility model

A new multivariate stochastic volatility model is developed in this paper. The main feature of this model is to allow threshold asymmetry in a factor covariance structure. The new model provides a parsimonious characterization of volatility and correlation asymmetry in response to market news. Statistical inferences are drawn from Markov chain Monte Carlo methods. We introduce news impact analysis to analyze volatility asymmetry with a factor structure. This analysis helps us to study different responses of volatility to historical market information in a multivariate volatility framework. Our model is successful when applied to an extensive empirical study of twenty stocks. Copyright © 2009 John Wiley & Sons, Ltd.     

The predictability of stock market volatility in emerging economies: Relative roles of local,regional, and global business cycles

This paper explores the role of business cycle proxies, measured by the output gap at the global, regional, and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic output gap is found to capture significant predictive information for India and China particularly, the business cycles associated with emerging economies and the world in general are strongly important for the BRIC countries and weakly for South Africa, especially in the postglobal financial crisis era. The findings suggest that despite the increase in the financial integration of world capital markets, emerging economies can still bear significant exposures to idiosyncratic risk factors, an issue of high importance for the profitability of global diversification strategies.     

Power transformation models and volatility forecasting

This paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where one‐period‐ahead forecasts are considered, the power autoregressive models are ranked first by a range of error metrics. Over longer forecast horizons, however, generalized autoregressive conditional heteroscedasticity models are preferred. A value‐at‐risk‐based forecast assessment indicates that, while the forecast errors are independent, they are not independent and identically distributed, although this latter result is sensitive to the choice of forecast horizon. Our results are robust across a number of different asset markets. Copyright © 2008 John Wiley & Sons, Ltd.     

Gamma stochastic volatility models

This paper presents gamma stochastic volatility models and investigates its distributional and time series properties. The parameter estimators obtained by the method of moments are shown analytically to be consistent and asymptotically normal. The simulation results indicate that the estimators behave well. The in‐sample analysis shows that return models with gamma autoregressive stochastic volatility processes capture the leptokurtic nature of return distributions and the slowly decaying autocorrelation functions of squared stock index returns for the USA and UK. In comparison with GARCH and EGARCH models, the gamma autoregressive model picks up the persistence in volatility for the US and UK index returns but not the volatility persistence for the Canadian and Japanese index returns. The out‐of‐sample analysis indicates that the gamma autoregressive model has a superior volatility forecasting performance compared to GARCH and EGARCH models. Copyright © 2006 John Wiley _ Sons, Ltd.     

Forecasting volatility

In this paper, we investigate the time series properties of S&P 100 volatility and the forecasting performance of different volatility models. We consider several nonparametric and parametric volatility measures, such as implied, realized and model‐based volatility, and show that these volatility processes exhibit an extremely slow mean‐reverting behavior and possible long memory. For this reason, we explicitly model the near‐unit root behavior of volatility and construct median unbiased forecasts by approximating the finite‐sample forecast distribution using bootstrap methods. Furthermore, we produce prediction intervals for the next‐period implied volatility that provide important information about the uncertainty surrounding the point forecasts. Finally, we apply intercept corrections to forecasts from misspecified models which dramatically improve the accuracy of the volatility forecasts. Copyright © 2006 John Wiley & Sons, Ltd.     

Mixed-frequency forecasting of crude oil volatility based on the information content of global economic conditions

This paper subjects six alternative indicators of global economic activity to empirically examine their relative predictive powers in the forecast of crude oil market volatility. GARCH-MIDAS approach is constructed to accommodate all the relevant series at their available data frequencies, thereby circumventing information loss and any associated bias. We find evidence in support of global economic activity as a good predictor of energy market volatility. Our forecast evaluation of the various indicators places a higher weight on the newly developed indicator of global economic activity which is based on a set of 16 variables covering multiple dimensions of the global economy, whereas other indicators do not seem to capture. Furthermore, we find that accounting for any inherent asymmetry in the global economic activity proxies improves the forecast accuracy of the GARCH-MIDAS-X model for oil volatility. The results leading to these conclusions are robust to multiple forecast horizons and consistent across alternative energy sources.