Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

This study combined time-varying parameter vector autoregression (TVP-VAR) and a
spillover index model to analyze the static, total, and net spillover effects of energy and stock …

The purpose of this article is to investigate whether various uncertainty measures provide
incremental information for the prediction the volatility of crude oil futures under high …

This study develops a shrinkage method, LASSO with a Markov regime-switching model
(MRS-LASSO), to predict US stock market volatility. A set of 17 well-known macroeconomic …

Based on the high-frequency heterogeneous autoregressive (HAR) model, this paper
investigates whether coronavirus news (in China and globally) contains incremental …

This research uses country-specific economic policy uncertainty (EPU) indices to predict the
volatility of European Union Allowance (EUA) futures and compares dimension reduction …

The shocks on certain market spread to other markets due to the financial linkages of global
economy, which is known as volatility spillover effect. In this study, we propose a volatility …

The stock market is considered one of the most complicated financial systems, comprising
several components or inventories, whose prices vary substantially over time. Bursaries …

Numerous studies have purported to show that using online investor sentiment measures
can enhance the accuracy of forecasting stock return volatility. However, many of these …

We forecast the realized covariance matrix of asset returns in the US equity market by
exploiting the predictive information of graphs in volatility and correlation. Specifically, we …