Quotation Frühwirth-Schnatter, Sylvia. 2009. Markov Chain Monte Carlo Methods for Parameter Estimation in Multidimensional Continuous Time Markov Switching Models. Workshop Financial Mathematics Meets Econometrics, Hausdorff Center for Mathematics, Bonn, Deutschland, 30.11.-1.12. Invited Talk


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Abstract

A multidimensional, continuous time model is considered where the observation process is a diffusion with drift and volatility coefficients being modeled as continuous time, finite state Markov chains with a common state process. For the econometric estimation of the states for drift and volatility and the rate matrix of the underlying Markov chain, both an exact continuous time as well as an approximate discrete time MCMC sampler is developped. These MCMC approaches are compared to various approaches based on ML estimation. Using simulated data, it is demonstrated that MCMC outperforms ML estimation for difficult cases like high rates. Finally, the modelis applied to daily stock index quotes from Argentina, Brazil, Mexico, and the US. Using BIC for model selection, a four state model is identified where the various states differ not only in the volatility of the various assets, but also in their correlation.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Markov Chain Monte Carlo Methods for Parameter Estimation in Multidimensional Continuous Time Markov Switching Models
Event Workshop Financial Mathematics Meets Econometrics
Year 2009
Date 30.11.-1.12
Country Germany
Location Hausdorff Center for Mathematics, Bonn
URL http://www.hcm.uni-bonn.de/events/eventpages/2009/financial-mathematics-meets-econometrics/schedule/#c1942
Invited Talk Y

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Frühwirth-Schnatter, Sylvia (Details)
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Institute for Statistics and Mathematics IN (Details)
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