This paper investigates how geopolitical risks influence the prediction performance on the
US stock market volatility with machine learning models. Further, it compares the predictive …

This paper aims to accurately forecast US stock market volatility by using international
market volatility information flows. The results show the significant ability of the combined …

The realized volatility forecasting of energy sector stocks facilitates the establishment of
corresponding risk warning mechanisms and investor decisions. In this paper, we collected …

Abstract The Russia-Ukraine conflict has brought ripple effects to the global economy. This
paper mainly investigates whether investor attention to the Russia-Ukraine conflict can affect …

An important ingredient in economic policy planning both in the public or the private sector is
risk management. In economics and finance, risk manifests through many forms and it is …

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 support vector regression (SVR) is a supervised machine learning technique that has
been successfully employed to forecast financial volatility. As the SVR is a kernel-based …

This paper tries to forecast gold volatility with multiple country-specific (GPR) indices and
compares the role of combined prediction models and dimension reduction methods …

Geopolitical events can impact volatilities of all assets, asset classes, sectors and countries.
It is shown that innovations to volatilities are correlated across assets and therefore can be …

By incorporating volatility information from nineteen commodity futures prices, this paper
compares the predictive ability of traditional individual AR-type and combination forecasting …