We propose a new method to improve density forecasts of the equity premium using information from options markets. We obtain predictive densities from stochastic volatility (SV) and GARCH models, which we then tilt using the second moment of the risk-neutral distribution implied by options...

Using a Bayesian time‐varying beta model, we explore how the systematic risk exposures of hedge funds vary over time conditional on some exogenous variables that managers are assumed to use in changing their trading strategies. In such a setting, we impose a structure on fund returns, betas...

In this paper we reviewed two findings pertinent for using asset market data to make inferences about the intangible capital stock. We presented evidence familiar from the empirical finance literature that returns are heterogeneous when firms are grouped according to their ratio of market equity...

Over the last two decades, alternative expected return proxies have been proposed with substantially lower variation than realized returns. This helped to reduce parameter uncertainty and to identify many seemingly robust relations between expected returns and variables of interest, which would...

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,...

We examine the effects of estimation risk and Bayesian learning on equilibrium asset prices when there is uncertainty about both the first and second moments of consumption and dividend growth rates. For the 1891-2007 period, our model generates a sizable average annual equity premium,...

Gaussian affine term structure models attribute time‐varying bond risk premia to changing risk prices driven by the conditional means of the risk factors, while structural models with recursive preferences credit it to stochastic volatility. We reconcile these competing channels by introducing...