This paper investigates the performance of a factor-augmented regression (FAR) model with a mixture of stationary and nonstationary factors in stock return prediction. For comparison purpose, we also consider a traditional FAR model with only stationary factors. In an application with a dataset...

A model of portfolio return dynamics is considered in which the price of risk is permitted to be heterogeneous. In doing this, a novel method is proposed that delivers improved out-of-sample forecasts of portfolio returns. The main innovation is the use of a set of predictors that account for...

The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with time varying parameters. The parameters are estimated by means of a sequential matching procedure which adopts as auxiliary model a time-varying generalization of the HAR model...

Ever since Harry Markowitz published his seminal paper on portfolio selection, investors have incorporated estimates of future volatilities and correlations into their asset allocation process. While portfolio construction methods continue to evolve, many investors continue to forecast...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial...

This paper proposes a latent dynamic factor model for low- as well as high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and allows for flexible dynamic dependence patterns by combining common latent factors driven by HAR dynamics and...

Researchers in finance very often rely on highly persistent – nearly integrated – explanatory variables to predict returns. However, statistical inference in predictive regressions depends critically upon the stochastic properties of the posited explanatory variable, and in particular, of...