# Michael S. Johannes

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This chapter discusses Markov Chain Monte Carlo (MCMC) based methods for estimating continuous-time asset pricing models. We describe the Bayesian approach to empirical asset pricing, the mechanics of MCMC algorithms and the strong theoretical underpinnings of MCMC algorithms. We provide a tutorial on building MCMC algorithms and show how to estimate equity(More)
This article develops a direct filtration-based maximum likelihood methodology for estimating the parameters and realizations of latent affine processes. Filtration is conducted in the transform space of characteristic functions, with a version of Bayes' rule used for recursively updating the joint characteristic function of latent variables and the data(More)
In this paper we empirically compare different term structure models when it comes to the pricing and hedging of caps and swaptions. We analyze the influence of the number of factors on the pricing and hedging results, and investigate which type of data !interest rate data or derivative price data! should be used to estimate the model parameters to obtain(More)
Particle learning provides a simulation-based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive and propagation rule to build a resampling-sampling framework. Predictive inference and sequential Bayes factors are a direct by-product. Our approach(More)
This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuous-time asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for exploring these high-dimensional, complex distributions. We first(More)
This paper analyzes sequential learning in the context of predictive regression models. To do this, we develop new particle based methods for sequential learning about parameters, state variables, hypotheses, and models. This sequential perspective allows us to quantify how investor's views about predictability and models varies over time, and naturally(More)
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State(More)