Bayesian statistical methods are naturally oriented towards pooling in a rigorous way information from separate sources … volatility or options prices. We develop a formal Bayesian framework where we can merge the backward looking information as … forecasting options prices out of sample (i.e. one-day ahead) our Bayesian estimators outperform standard forecasts that use …

between rational and irrational beliefs. Relatedly, the Bayesian approach, when applied to the Grand State Space, is … because there are many situations in which there is not sufficient information for an individual to generate a Bayesian prior …. It follows that the Bayesian approach is neither sufficient not necessary for the rationality of beliefs. …

process by which beliefs are generated and justified. We argue that the Bayesian approach is unsatisfactory for this purpose …, for several reasons. First, the Bayesian approach begins with a prior, and models only a very limited form of learning …, namely, Bayesian updating. Thus, it is inherently incapable of describing the formation of prior beliefs. Second, there are …

Economic modeling assumes, for the most part, that agents are Bayesian, that is, that they entertain probabilistic …

Economic modeling assumes, for the most part, that agents are Bayesian, that is, that they entertain probabilistic …

process by which beliefs are generated and justified. We argue that the Bayesian approach is unsatisfactory for this purpose …, for several reasons. First, the Bayesian approach begins with a prior, and models only a very limited form of learning …, namely, Bayesian updating. Thus, it is inherently incapable of describing the formation of prior beliefs. Second, there are …

Bayesian methodology for estimating factor risk premia and hence equity risk premia for both traded and non-traded factors …