Bayesian computation in finance
In this paper we describe the challenges of Bayesian computation in Finance. We show that empirical asset pricing leads to a nonlinear non-Gaussian state space model for the evolutions of asset returns and derivative prices. Bayesian methods extract latent state variables and estimate parameters by calculating the posterior distributions of interest. We describe the use of direct estimation methods such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods based on particle filtering (PF).