Bayesian Hidden Markov Models for Financial Data

Conference paper
First Online:
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Hidden Markov Models, also known as Markov Switching Models, can be considered an extension of mixture models, allowing for dependent observations. The main problem associated with Hidden Markov Models is represented by the choice of the number of regimes, i.e. the number of generating data processes, which differ one from another just for the value of the parameters. Applying a hierarchical Bayesian framework, we show that Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of the model, as well as the number of regimes, and to simulate the posterior predictive densities of future observations. Assuming a mixture of normal distributions, all the parameters of the model are estimated using a well known exchange rate data set.

Keywords

Markov Chain Monte Carlo Markov Chain Monte Carlo Method Dirichlet Process Markov Chain Monte Carlo Algorithm Financial Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.DIEF, Università di MacerataMacerataItaly

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