Exact Bayesian inference for Markov switching diffusions
Authors
Research Topics
Paper Information
-
Journal:
Journal of the Royal Statistical Society Series B -
DOI:
10.1093/jrsssb/qkag115 -
Published:
July 22, 2026 -
Added to Tracker:
Jul 23, 2026
Abstract
Abstract We develop the first exact Bayesian methodology for the problem of inference in discretely observed regime switching diffusions. Switching diffusion models extend ordinary diffusions by allowing for jumps in instantaneous drift and volatility. The jumps are driven by a latent, continuous-time Markov switching process. We address the problem through an MCMC and an MCEM algorithm that target the exact posterior of diffusion parameters and the latent regime process. The algorithms are exact in the sense that they target the correct posterior distribution of the continuous model, so that the errors are due to Monte Carlo only. We illustrate the method on numerical examples, including an empirical analysis of the method’s scalability in the length of the time series, and find that it is comparable in computational cost with discrete approximations while avoiding their shortcomings.
Author Details
Timothée Stumpf-Fétizon
AuthorKrzysztof Łatuszyński
AuthorJan Palczewski
AuthorGareth Roberts
AuthorResearch Topics & Keywords
Bayesian Statistics
Research AreaCitation Information
APA Format
Timothée Stumpf-Fétizon
,
Krzysztof Łatuszyński
,
Jan Palczewski
&
Gareth Roberts
(2026)
.
Exact Bayesian inference for Markov switching diffusions.
Journal of the Royal Statistical Society Series B
, 10.1093/jrsssb/qkag115.
BibTeX Format
@article{paper1482,
title = { Exact Bayesian inference for Markov switching diffusions },
author = {
Timothée Stumpf-Fétizon
and Krzysztof Łatuszyński
and Jan Palczewski
and Gareth Roberts
},
journal = { Journal of the Royal Statistical Society Series B },
year = { 2026 },
doi = { 10.1093/jrsssb/qkag115 },
url = { https://doi.org/10.1093/jrsssb/qkag115 }
}