Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov...

This paper introduces structured machine learning regressions for prediction and nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the empirical problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial,...

We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23...

This paper examines, for the first time, the performance of machine learning models in realised volatility forecasting using big data sets such as LOBSTER limit order books and news stories from Dow Jones News Wires for 28 NASDAQ stocks over a sample period of July 27, 2007, to November 18,...

A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...

Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

We document the forecasting gains achieved by incorporating measures of signed, finite and infinite jumps in forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that vary by sector, volume and degree of jump activity. We use...