This study investigates whether the direction of US implied volatility, VIX index, can be
forecast. Multiple forecasts are generated based on standard econometric models, but, more …

Although machine learning is frequently associated with neural networks, it also comprises
econometric regression approaches and other statistical techniques whose accuracy …

This study identifies Indonesian investors' reactions to the drop in stock prices on the
Indonesia Stock Exchange market, during the early months of the COVID-19 crisis, before …

Volatility forecasting plays an integral role in risk management, investments and security
valuation for all assets with uncertain future payoffs. We enrich the literature by presenting …

The primary goal of this study is to effectively measure the impact of a severe random shock,
such as the COVID‐19 pandemic on aggregate economic activity in Greece, seven other …

This paper applies a plethora of machine learning techniques to forecast the direction of the
US equity premium. Our techniques include benchmark binary probit models, classification …

We use a quantile-boosting approach to compute out-of-sample forecasts of gold returns.
The approach accounts for model uncertainty and model instability, and it allows forecasts to …

This paper tests whether it is possible to improve point, quantile, and density forecasts of
realised volatility by conditioning on a set of predictive variables. We employ quantile …

Are high-frequency data always needed to generate precise forecasts of risk measures in
energy markets? This study attempts to shed light on this question. We study whether energy …

We propose a real-time quantile-regression approach to analyze whether widely studied
macroeconomic and financial variables help to forecast out-of-sample gold returns. The real …