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...

Since Liu, Nissim and Thomas (LNT, 2002), researchers have been perplexed by how simple earnings forecasts using multiples, apparently outperform the theory-based residual income model in terms of pricing error. This paper explains mathematically how LNT (2002) find this curious result and...

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three...

Is univariate or multivariate modelling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead Expected Shortfall of a portfolio invested in the Fama-French and momentum factors. Apply ingextensive tests and...

This article studies the risk forecasting properties of three realized volatility models for three Chinese individual stocks, and reveals the important role that jumps can play in risk prediction. I firstly investigate dynamic pattern of jumps in three Chinese stocks, and find that relative to...

Momentum is one of the largest and most pervasive market anomalies. However, despite a high mean and Sharpe ratio, momentum suffers from large negative skewness that comes from momentum crash periods. These crashes occur in times of both market stress and market rebound and thus variables that...

We use economic policy uncertainty (EPU) shocks in combination with the mixed data sampling (MIDAS) approach to investigate long-run stock market variances and correlations, primarily for the US and the UK. The US long-run stock market variance depends significantly on US EPU shocks but not on...

Recently, tail risks have attracted much attention in the literature for their role in predicting the cross-sectional expected returns of stocks. Using a modified conditional value at risk (CVaR), the extreme loss and gain of stocks can be measured using the left-tail CVaR- and the right-tail...

This paper examines the cross-sectional properties of stock return forecasts based on Fama-MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999-December 2018 period. Our estimation approach is strictly out-of-sample, mimicking an investor who...