TY - CHAP
TI - Markov Chain Monte Carlo Methods for Parameter Estimation in Multidimensional
Continuous Time Markov Switching Models
AB - 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.
AF - Workshop Financial Mathematics Meets Econometrics
PP - Hausdorff Center for Mathematics, Bonn
UR - http://www.hcm.uni-bonn.de/events/eventpages/2009/financial-mathematics-meets-econometrics/schedule/#c1942
PY - 2009-12-01
AU - Frühwirth-Schnatter, Sylvia
ER -