We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return) and betas (to a choice set of explanatory factors) in a multivariate setting. This...

Suppose a fund manager uses predictors in changing portfolio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by...

We use purely statistical methods to determine if the pricing kernel is the intertemporal marginal rate of substitution under recursive utility. We introduce a nonparametric Bayesian method that treats the pricing kernel as a latent variable and extracts it and its transition density from...

Under an assumption of normality, we explore a non-orthogonal Bayesian technique in which redundant information can in principle be filtered out of the posterior distribution by the explicit coupling of the prior and likelihood functions. The Black-Litterman forecasting model widely used by...

Deriving an optimal asset allocation for institutional investors hinges crucially on the quality of inputs used in the optimization. If the mean vector and the covariance matrix are known with certainty, the classical mean-variance optimization of Markowitz (1952) produces optimal portfolios....

We use the Bayesian method introduced by Gallant and McCulloch (2009) to estimate consumption-based asset pricing models featuring smooth ambiguity preferences. We rely on semi-nonparametric estimation of a flexible auxiliary model in our structural estimation. Based on the market and aggregate...