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...

Building on the success of Ferreira and Santa-Clara (2011) in separately forecasting the return components of the stock market, this paper examines the links between economic regimes and these components to predict the aggregate U.S. stock market. We propose a three-step methodology that we call...

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models,...

We propose a quantile regression approach to equity premium forecasting. Robust point forecasts are generated by both fixed and time-varying weighting schemes, thus exploiting the entire distributional information associated with each predictor. Further gains are achieved by incorporating the...

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...

We present a novel approach to analyzing stock return predictability that accommodates (i) arbitrary predictor persistence, (ii) panels with common factors, (iii) multiple predictors, (iv) short- and long-horizon analysis, and relies on standard inference from least-squares estimation of a...

In this paper, we test for the structural stability of both bivariate and multivariate predictive regression models for equity premium in South Africa over the period of 1990:01 to 2010:12, based on 23 financial and macroeconomic variables. We employ a wide range of methodologies, namely, the...

This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a...

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

Motivated by recurrent neural networks, this paper proposes a recurrent support vector regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR based ARMA model is compared with five...