Motivated by the importance of geopolitical risk and its possible predictive power for oil
volatility, this paper aims to quantitatively investigate the role of geopolitical risk (GPR) …

This study examines the predictive power of the global financial cycle (GFCy) over oil market
volatility using the GARCH-MIDAS framework. The GARCH-MIDAS model provides an …

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 …

In this article, we use a deep quantile estimator, based on neural networks and their
universal approximation property to examine a non-linear association between the …

Since the late nineties, the Basel Accords require financial institutions to measure their
financial risk by reporting daily predictions of Value at Risk (VaR) based on 10‐day returns …

This paper investigates whether the macroeconomic uncertainty factors can explain and
forecast China's INE crude oil futures market volatility. We use the GARCH-MIDAS model to …

In this paper, we employ the generalized autoregressive conditional heteroscedasticity-
mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of 19 dollar …

We examine the power of global economic conditions (GECON) in forecasting the daily
return volatility of various international Real Estate Investment Trusts (REITs) indices. To this …

In this paper, we employ the generalized autoregressive conditional heteroscedasticity-
mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level …

In this paper, we effectively extend the Realized-EGARCH (R-EGARCH) framework by
allowing the conditional variance process to incorporate exogenous variates related to …