The episodes of stock market crises in Europe and the U.S.A. since the year 2000, and the fragility of the international stock markets, have sparked the interest of researchers in understanding and in modeling the markets’ rising volatilities in order to prevent against crises. Portfolio...

This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...

The dynamic conditional correlation (DCC) and co-range models are two main frameworks widely used to employ range-based univariate volatility. Using the two approaches, we build novel multivariate range-based EGARCH (REGARCH) models: a DCC-REGARCH and co-range REGARCH (CRREGARCH) model, as well...

This paper investigates whether geopolitical risks (GPRs) contains incremental information content to predict crude oil futures volatility under high-frequency Heterogeneous Autoregressive (HAR) model specifications. Moreover, considering structural breaks in crude oil volatility, we extend the...

The present study analyses the volatility spillover of exchange rate on South African Stock Market.The Capital market of South Africa has yardstick of African markets. The economic factors arecrucially impacting the returns of stock of South Africa. The study collected data fromJohannesburg...

This paper expands the emerging literature on volatility forecasting for China’s oil market by exploring the predictive ability of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the...

This article mainly studies the crude oil volatility index (OVX) forecasting from the perspective of the cross-market asymmetric effects of Chinese stock market jumps. The positive and negative jumps are used to represent the asymmetric shocks which are calculated based on the high-frequency...

Variance-optimal hedging in a discrete-time framework is a practical options strategy that aims to reduce the residual risk. It has been widely used in volatility trading desks. In this paper, we solve the variance-optimal hedging problem for affine GARCH models both semi-explicitly and through...

This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model and adopts a standardized Student-t and a standardized skewed Student-t distributions for the return equation. The Bayesian estimators show more...

This paper examines the lognormality assumption of per capita, real consumption growth, which is a common assumption in asset pricing models. We found that shocks to household consumption growth are persistent, negatively skewed, and have excess kurtosis. Therefore, we revisited the fundamental...