An MCMC computational approach for a continuous time state-dependent regime switching diffusion process

Hibbah EH, El Maroufy H, Fuchs C, Ziad T (2019)
JOURNAL OF APPLIED STATISTICS.

Zeitschriftenaufsatz | E-Veröff. vor dem Druck| Englisch
 
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Autor/in
Hibbah, El Houcine; El Maroufy, Hamid; Fuchs, ChristianeUniBi; Ziad, Taib
Abstract / Bemerkung
State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states, and the parameters of the models. The latent imputed data is updated at random time intervals in block using a Metropolis Hastings algorithm. The switching states are computed by an adaptation of a forward filtering backward smoothing algorithm to the HSD model. The parameters are estimated after prior specifications and conditional posterior densities formulation using Gibbs sampler or Metropolis Hastings algorithm.
Stichworte
Hybrid switching diffusion model; data imputation; hidden states; computation; random time imputation; states computation
Erscheinungsjahr
2019
Zeitschriftentitel
JOURNAL OF APPLIED STATISTICS
ISSN
0266-4763
eISSN
1360-0532
Page URI
https://pub.uni-bielefeld.de/record/2938376

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Hibbah EH, El Maroufy H, Fuchs C, Ziad T. An MCMC computational approach for a continuous time state-dependent regime switching diffusion process. JOURNAL OF APPLIED STATISTICS. 2019.
Hibbah, E. H., El Maroufy, H., Fuchs, C., & Ziad, T. (2019). An MCMC computational approach for a continuous time state-dependent regime switching diffusion process. JOURNAL OF APPLIED STATISTICS. doi:10.1080/02664763.2019.1677573
Hibbah, E. H., El Maroufy, H., Fuchs, C., and Ziad, T. (2019). An MCMC computational approach for a continuous time state-dependent regime switching diffusion process. JOURNAL OF APPLIED STATISTICS.
Hibbah, E.H., et al., 2019. An MCMC computational approach for a continuous time state-dependent regime switching diffusion process. JOURNAL OF APPLIED STATISTICS.
E.H. Hibbah, et al., “An MCMC computational approach for a continuous time state-dependent regime switching diffusion process”, JOURNAL OF APPLIED STATISTICS, 2019.
Hibbah, E.H., El Maroufy, H., Fuchs, C., Ziad, T.: An MCMC computational approach for a continuous time state-dependent regime switching diffusion process. JOURNAL OF APPLIED STATISTICS. (2019).
Hibbah, El Houcine, El Maroufy, Hamid, Fuchs, Christiane, and Ziad, Taib. “An MCMC computational approach for a continuous time state-dependent regime switching diffusion process”. JOURNAL OF APPLIED STATISTICS (2019).