Abstract and Keywords
This article looks at the usefulness of Bayesian methods in finance. It covers all the major topics in finance. It discusses the predictability of the mean of asset returns, central to finance, as it relates to the efficiency of financial markets. It reviews the economic relevance of predictability and its impact on optimal allocation. It also describes the Markov chain Monte Carlo (MCMC) and particle filtering algorithms that are important in modern Bayesian financial econometrics. MCMC algorithms have resulted in a tremendous growth in the use of stochastic volatility models in financial econometrics. This article also contains some major contributions of Bayesian econometrics to the literature on empirical asset pricing. Many of the other themes in modern Bayesian econometrics, including the use of shrinkage and the interaction between theory and econometrics are discussed. This article ends up with the discussion of a promising recent development in finance: filtering with parameter learning.
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