The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However,...

This paper proposes a data-based measure of model performance to discriminate among competing asset pricing models of return predictability. I form a set of variance bounds on pricing kernels based on different systems for predicting asset returns. For a given asset pricing model, I define the...

The proliferation of anomalies and the resulting `factor zoo' has challenged finance researchers to identify firm characteristics that are genuinely related to the cross-sectional variation in expected stock returns. We address this challenge using a Bayesian ensemble of trees approach, namely,...

This paper examines the economic implications of new factor models and shows that the Hou, Xue, and Zhang (HXZ, 2015a) four-factor model outperforms the Fama and French (FF5, 2015a) five-factor model for investing in anomalies in- and out-of-sample. The difference in certainty-equivalent returns...

We develop a transparent Bayesian approach to quantify uncertainty about linear stochastic discount factor models. We show that, for a Bayesian decision-maker, the model probability decreases with historical maximum in-sample Sharpe ratios and increases with model dimensions. We apply our...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

We investigate whether firm fundamentals can explain the shape of option implied volatility (IV)curve. Extending Geske's (1977) compound option model, we link firm fundamentals to the IV curvetheoretically. Using options on all available US-listed companies, we find empirically that...

Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the...

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

We propose a novel methodology that jointly estimates the proportions of skilled/unskilled funds and the cross-sectional distribution of skill in the mutual fund industry. We model this distribution as a three-component mixture of a point mass at zero and two components — one negative, one...