Forecasting the realized volatility: the role of jumps

This article investigates the role of jump components dependent on the ABD-LM jump test in forecasting volatility. Our out-of-sample forecasting results show that compared with the ABD-LM jump component, its decomposition forms based on signed returns can significantly improve the models’ forecasting performance and our findings have important implications for investors and policymakers.     

Forecasting the oil futures price volatility: A new approach

This study provides a new perspective of modelling and forecasting realized range-based volatility (RRV) for crude oil futures. We are the first to improve the Heterogeneous Autoregressive model of Realized Range-based Volatility (HAR-RRV) model by considering the significant jump components, signed returns and volatility of realized range-based volatility. The empirical results show that the volatility of volatility significantly exists in the oil futures market. Moreover, our new proposed models with significant jump components, signed returns and volatility of volatility can gain higher forecast accuracy than HAR-RRV-type models. The results are robust to different forecasting windows and forecasting horizons. Our new findings are strategically important for investors making better decisions.     

Testing the efficiency of the futures market for crude oil in the presence of a structural break

The efficiency of the futures market for crude oil has been the subject of significant study, with the basis regression representing a popular methodology. However, the parameters of this model are subject to a structural break, casting doubt on any conclusion regarding the efficiency of the futures market. To address this problem, this article employs a simple generalization which is capable of testing the efficiency of a futures market in the presence of a structural break. Using this approach, strong evidence of inefficiency is found in the one month futures contract for West Texas Intermediate for the period between 1985 and 2013, which is otherwise not detected.     

Covariance breakdowns and connectedness of crude oil futures markets with non-synchronous data

ABSTRACT

This article identifies the breakdowns in the covariance of three benchmark crude oil futures markets (WTI, Brent and Dubai) and investigates the changes of market connectedness across the breakdown periods. As the crude oil futures are traded in different regions, this article eliminates the non-synchronous trading data by employing the Vector Moving Average structure and the Bayesian data augmentation approach, which keeps the integrity of original data without changing its properties. The results show that there are significant breaks in the covariance structure of crude oil futures markets. The breakdown periods are consistent with the periods when the market volatilities are at high level and the returns are volatile. The changes of market connectedness are independent of the covariance states, which supports the globalization hypothesis for the crude oil market. The results also suggest that there is more information flow out of the WTI than to the WTI during the sample period, particularly during the breakdown periods in 2008–2009.     

Uncertainty and crude oil market volatility: new evidence

ABSTRACT

The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.     

Economic policy uncertainty and the Chinese stock market volatility: new evidence

This study investigates the impacts of the economic policy uncertainty (EPU) indexes of China and the G7 countries on Chinese stock market volatility and further constructs a new diffusion index based on these indexes using principal component analysis (PCA) to achieve enhanced predictive ability. The in-sample results indicate that the EPU indexes of China and some of the G7 countries show a significantly negative impact on future volatility. Moreover, our constructed diffusion index also has a significantly negative impact. Furthermore, the out-of-sample results show that this diffusion index exhibits a significantly higher forecast accuracy than the EPU itself and combination forecasts. Finally, various robustness checks are consistent with our main conclusions. Overall, we construct a new and useful indicator that can substantially increase forecast accuracy with respect to the Chinese stock market.     

Forecasting volatility in the Indian equity market using return and range-based models

In this article, we assess the time-varying volatility of the National Stock Exchange in the Indian equity market using unconditional estimators and asymmetric conditional econometric models. The volatility estimate and forecast is computed from the interday return and intraday range-based data of the exchange’s flagship index, CNX NIFTY, for the time period spanning 1 January 2009 through 31 December 2013. These are our findings: First, we determine that the time-varying volatility of the index is asymmetric with qualities of stationarity and leptokurtic distribution. Second, the one-step-ahead volatility forecast derived from the univariate time series parameters through the GJR-GARCH ?????process indicates that the model evaluation criteria of the autoregressive process tends towards range-based models vis-à-vis a return-based model. The validity of this methodology is further analysed with the superior predictive ability test, the outcome of which supports the use of range-based conditional models. Finally, among the evaluated range-based model variants, the model confidence set procedure favours the Yang–Zhang estimator as being better suited to forecast the exchange’s volatility than the ones by Parkinson, Garman–Klass and Rogers–Satchell.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

Is economic policy uncertainty important to forecast the realized volatility of crude oil futures?

In this research, we first investigate whether economic policy uncertainty (EPU) index can increase the HAR-RV-type models’ forecast accuracy. In addition, we explore how EPU index can be effectively used to gain larger economic values in the oil futures market. To this end, this research provides a new perspective on setting thresholds for EPU and examines whether these thresholds can help improve both the forecast accuracy and economic values. Empirical results suggest that the HAR-RV-type models including EPU can generate more accurate forecasts and economic values. The HAR-RV-type models including above-threshold EPU can further improve the forecast accuracy and yield higher economic values by setting specific thresholds for a range of horizons. The findings highlight the importance of EPU and effective way of using EPU in risk management and portfolio strategies that is crucial for investors and policymakers.     

Forecasting the volatility of crude oil futures using high-frequency data: further evidence

We forecast the realized volatility of crude oil futures market using the heterogeneous autoregressive model for realized volatility and its various extensions. Out-of-sample findings indicate that the inclusion of jumps does not improve the forecasting accuracy of the volatility models, whereas the “leverage effect” pertaining to the difference between positive and negative realized semi-variances can significantly improve the forecasting accuracy in predicting the short- and medium-term volatility. However, the signed jump variations and its decomposition couldn’t significantly enhance the models’ forecasting accuracy on the long-term volatility.     

Forecasting the aggregate stock market volatility in a data-rich world

ABSTRACT

In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing literature, we determine several candidate predictors that have 22 technical indicators and 14 macroeconomic and financial variables. Our out-of-sample results reveal several noteworthy findings. First, few macroeconomic and financial variables and most of technical indicators have superior performance relative to the benchmark model. Second, combination forecasts are able to significantly beat the benchmark and some signal predictors Third, the lasso and elastic models with all predictors can generate more accurate forecasts than the benchmark and some other predictors in both the statistical and economic sense. Fourth, the lasso and elastic models exhibit higher forecast accuracy during periods of expansions and recessions. Finally, our findings are robust to several tests, such as different forecasting windows, forecasting models, and forecasting evaluations.     

Volatility forecasting: combinations of realized volatility measures and forecasting models

This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.     

The heterogeneous dependence between global crude oil and Chinese commodity futures markets: evidence from quantile regression

This paper explores the dependence between global crude oil and Chinese commodity futures markets across different quantiles of the return distributions. Based on weekly data from 11 June 2004 to 7 July 2017, we address this issue by applying a quantile regression method. This technique provides a more detailed investigation of the dependence. Moreover, considering the structural breaks caused by market turmoil or financial crises, we divide the full period of every commodity sector market into sub-periods based on these break dates to further explore the dependence changes. The empirical results indicate that the dependence between global crude oil and Chinese commodity futures markets is different across quantiles in different commodity sectors. The dependence is significantly positive, except in markets with high expected returns. Additionally, the effects caused by structural breaks are distinctly heterogeneous across quantiles. The effect of the same break on the degree of dependence exhibits an increasing tendency as the quantile level increases, which suggests that markets with high expected returns are more susceptible to crises. Finally, we apply a prediction analysis to further verify the heterogeneity of the commodity sectors, which may be the cause of the heterogeneous dependence.     

Impacts of derivative markets on spot market volatility and their persistence

In this article, we investigate the impacts of futures and options markets on the volatility of the underlying market. Unlike earlier studies, the focus is on their persistence over time. Tests on the Hang Seng index yield several interesting results that often contrast with previous findings. Empirical results suggest that the quality of new information generated by derivative trading determines the impacts on the spot market volatility. The futures market provides new, material information reducing spot market volatility. The Options market, on the other hand, generates noisy information and distorts price, which is followed by an increase in volatility and a decrease in its sensitivity to price change. While the impact of futures persists, that of options mostly disappears as the market matures. Our conjecture is that the futures market is mainly driven by informed, experienced participants, while the options market attracts new, inexperienced investors.     

Dynamics of volatility transmission between the U.S. and the Chinese agricultural futures markets

The U.S. and China are two of the biggest players in the world agricultural market. The literature documents that volatility in the U.S. agricultural futures market spills over significantly to that of China. This article provides further insights into the spillovers from China to the U.S. as well as the time horizon and dynamics of the bidirectional spillovers through the application of a multivariate extension of the heterogeneous autoregressive model, in relation to four commodities – soybean, wheat, corn and sugar. The results confirm the existence of significant spillovers from the U.S. to China for four commodities, which are primarily generated by the shorter-term volatility components in the U.S., and provide evidence for the increasing pricing power of the Chinese market. The findings are robust against various specifications and have important investment and policy implications.     

Cyclicality of stock market volatility

By utilizing the significance and stochastic dominance tests, this paper formally tests the relationship between stock market volatility and the business cycle. Results show that, for most matured markets, stock market volatility is countercyclical, while for emerging markets, the volatility can be procyclical.     

The information content of implied volatility in developed versus developing FX markets

This article predicts the daily movement of monthly foreign exchange (FX) rate volatility using a linear combination of a time-series model and implied volatilities from options. The focus is on analysing the FX volatilities in three developing economies (the Brazilian real (BRL), the Indian rupee (INR) and the Russian ruble (RUB)) against the US dollar (USD). The empirical exercise utilizes two time-series models, mixed data sampling (MIDAS) and GARCH. The analysis indicates that for both developed and developing economies the predictive power of MIDAS and that of GARCH is comparable. Further on in this article, we will ascertain whether the relationship between realized and implied volatility is fundamentally different in the case of developing economies from that among developed economies. Thus, we compare the pairs USD/BRL, USD/INR and USD/RUB against EURO/USD and USD/Japanese yen to determine the information content and predictive power of implied volatilities. Plots of the MIDAS coefficients show that the volatility is more persistent in developing economies than in developed economies.     

Predicting stock realized variance based on an asymmetric robust regression approach

This paper introduces an asymmetric robust weighted least squares (ARLS) approach to improve the forecasting performance of the heterogeneous autoregressive model for realized volatility. The ARLS approach down-weights extreme observations to limit the bad influence of outliers on the estimated parameters. Compared with existing robust regression methods, our model further takes into account the asymmetry of outliers using a class of kernel functions. Out-of-sample results show the ARLS approach can generate more accurate forecasts of the S&P 500 index realized volatility in the statistical and economic senses. The model that considers the asymmetry of outliers gains superior performance among various robust regression competitors. The forecasting improvements also hold in other international stock markets. More importantly, the source of the predictive ability of the ARLS model comes from the less biased and more efficient parameter estimation.     

A lottery-preference-based explanation of realized kurtosis puzzle in Chinese stock market

High kurtosis corresponds to fat tails on both sides and under risk-aversion assumption investors’ dislike of left-tail loss outweighs their preference for right-tail gain. Therefore, high kurtosis characteristic of stock should predict high expected returns. However, the high-frequency-data-based empirical results on Chinese stock market are just the opposite, which we refer to as the ‘realized kurtosis puzzle’. Using the double sorts and firm-level cross-sectional regression methods, we further demonstrate investors’ preference for lottery-like stocks or lottery preference is key to solve the puzzle. Our further empirical research verifies stocks with higher retail investors’ shareholding proportion and unavailable for short show stronger ‘realized kurtosis puzzle’. In addition, the puzzle is particularly significant in high lottery preference periods while less apparent in low lottery preference times.     

The empirical study on price discovery of cornstarch futures market in China

This article aims at exploring the performance of the price discovery function of cornstarch futures market in China. In order to test the stationarity of the cash and futures prices of cornstarch, the augmented Dickey–Fuller test is applied. Both prices are integrated of order one. Then, the Johansen cointegration test is conducted to test the cointegrating relationship between those two prices. Finally, the Granger causality test is performed to observe the direction of causality. The evidence shows that there is a long-run relationship between cash and futures prices and the futures price Granger causes cash price. As a whole, price discovery of cornstarch market in China is present although it is a newly emerged market.