This article presents a comprehensive analysis of the relative ability of three information sets --- daily trading volume, intraday returns and overnight returns --- to predict equity volatility. We investigate the extent to which statistical accuracy of one-day-ahead forecasts translates into...

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. This paper proposes a forecasting approach for the equity risk premium with two novel features. First, individual month-ahead forecasts are obtained from parsimonious threshold...

The increasing availability of intraday financial data has led to improvements in daily volatility forecasting through long-memory models of realized volatility. This paper demonstrates the merit of the non-parametric Nearest Neighbor (NN) approach for S&P 100 realized variance forecasting. A...

This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight...

We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because it meets by construction ex post the correct out-of-sample conditional coverage criterion. This enables a Wald-type conditional quantile forecast encompassing...