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In this study, we first investigate the impacts of economic policy uncertainty (EPU), monetary policy uncertainty (MPU), and both of them on oil market volatility. We have several noteworthy findings. First, the EPU index can significantly increase the predictive ability compared to benchmark model for the oil market. Second, the high MPU index leads to high fluctuations with respect to oil market, and can remarkably help in forecasting oil volatility. Third, we first find that the MPU and EPU have useful complementary information, and considered both of them together is more powerful to predict oil volatility than separate them. Our conclusions are robust to different forecasting windows, measures and monetary policy uncertainty. • The EPU index can significantly increase the predictive ability compared to benchmark model for the oil market. • The high MPU index leads to high fluctuations with respect to oil market. • The MPU and EPU have useful complementary information. • Considered both of them together is more powerful to predict oil volatility than separate them. • Our conclusions are robust. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of global economic policy uncertainty (GEPU) on the volatility of the Chinese stock market and whether GEPU has predictive power for the volatility of the Chinese stock market. We apply the generalized autoregressive conditional heteroscedastic mixed data sampling (GARCH–MIDAS) model to evaluate the impact of the monthly GEPU index on the daily Shanghai Composite index. Our empirical results show that GEPU has a positive and significant influence on the volatility of the Chinese stock market, which reflects that Chinese stock market has been gradually integrated into the global economy. In addition, the forecasts generated by the GARCH–MIDAS model with GEPU and RV (Realized Volatility) produce substantially smaller errors than the GARCH (1,1) model and the GARCH–MIDAS model with RV (measured by RMSE, RMAE, RMSD, RMAD loss functions and DM test), which confirms the important role of GEPU for predicting the volatility of the Chinese stock market. Overall, our findings imply that GEPU is an additional driving factor of long-term volatility in the Chinese stock market. [ABSTRACT FROM AUTHOR]

This paper investigates the role of news implied volatility (NVIX), a measure of uncertainty, in long-term stock market volatility in developed markets. The results showed that NVIX has a positive and significant impact on stock market variances in the full sample period using the GARCH–MIDAS model. Furthermore, out-of-sample forecasting results showed that including NVIX generally improves forecasting performance; that is, the GARCH–MIDAS model, with the long-term component driven by realized volatility (RV) and NVIX, outperforms those with only RV in terms of forecasting performance. [ABSTRACT FROM AUTHOR]

In this paper, we extend the GARCH-MIDAS model proposed by Engle et al. (2013) to account for the leverage effect in short-term and long-term volatility components. Our in-sample evidence suggests that both short-term and long-term negative returns can cause higher future volatility than positive returns. Out-of-sample results show that the predictive ability of GARCH-MIDAS is significantly improved after taking the leverage effect into account. The leverage effect for short-term volatility component plays more important role than the leverage effect for long-term volatility component in affecting out-of-sample forecasting performance. [ABSTRACT FROM AUTHOR]

Forecasting financial market volatility is an important issue in the area of econophysics, and revealing the determinants of the market volatility has drawn much attentions of the academics. In order to better predict market volatilities, we use news-based implied volatility (NVIX) to measure uncertainty, and examine the predictive power of NVIX on the stock market volatility in both long and short-term among Asia-Pacific markets via GARCH-MIDAS model. We find that NVIX does not well explain long-term volatility variants in the full sample period, and it is positively associated with market volatility through a subsample analysis starting from the Financial Crisis. We also find that NVIX is more efficient in determining short-term volatility than the long-term volatility, indicating that the impact of NVIX is short-lived and information that investors concern could be quickly reflected in the stock market volatilities. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of hot money on the return and volatility of the Chinese stock market using a nonlinear Granger causality test and a new GARCH-class model based on mixed data sampling regression (GARCH–MIDAS). The empirical results suggest that no linear or nonlinear causality exists between the growth rate of hot money and the Chinese stock market return, implying that the Chinese stock market is not driven by hot money and vice versa. However, hot money has a significant positive impact on the long-term volatility of the Chinese stock market. Furthermore, the dependence between the long-term volatility caused by hot money and the total volatility of the Chinese stock market is time-variant, indicating that huge volatilities in the stock market are not always triggered by international speculation capital flow and that Chinese authorities should further focus on more systemic reforms in the trading rules and on effectively regulating the stock market. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the impact of economic policy uncertainty on stock volatility forecast. We apply the GARCH–MIDAS model which can directly incorporate low-frequency economic policy uncertainty index and high-frequency stock return to model and forecast stock index volatility. The in-sample estimation results show that both level and variance of the change rate of Chinese economic policy uncertainty index provide useful information beyond realized volatility for stock index volatility forecast. The out-of-sample forecast results indicate that a combination of realized volatility and economic policy uncertainty in GARCH–MIDAS model can significantly improve the forecast performance of stock index volatility. • The relationship between economic policy uncertainty and stock index volatility. • The significant impact of economic policy uncertainty on stock index volatility. • The significant improvement of the forecast performance of stock index volatility. [ABSTRACT FROM AUTHOR]