Managers, typically, are unaware of the significant impact their decisions could have on the random mechanism driving a data generating process. Here, a new parametric Bayesian technique is introduced that would allow managers to obtain an estimate of the impact of their decisions on the...

We discuss the relevance of consistency to the Bayesian. Should consistency be dismissed as irrelevant or thought about seriously when constructing prior distributions? Strong opinions have been held on this matter, but it is probably fair to say it is a largely neglected area. Pioneers, such as...

This paper introduces a new family of Bayesian semi-parametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized facts of such returns, namely heavy tails, asymmetry, volatility clustering, and leverage. A Bayesian nonparametric prior...

We develop a new method to sample from posterior distributions in hierarchical models without using Markov chain Monte Carlo. This method, which is a variant of importance sampling ideas, is generally applicable to high-dimensional models involving large data sets. Samples are independent, so...