Cover Image

An Infinite Hidden Markov Model with Stochastic Volatility

This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density beyond that attributed to parametric latent volatility. The new model nests several special cases as well as the SV-DPM. We also discuss posterior and predictive density simulation methods for the model. Applied to equity returns, foreign exchange rates, oil price growth and industrial production growth, the new model improves density forecasts, compared to the SV-DPM, a stochastic volatility with Student-t innovations and other fat-tailed volatility models
Year of publication:
[2022]
Authors: Chenxing ; Maheu, John M. ; Yang, Qiao
Publisher: [S.l.] : SSRN
Subject: Markov-Kette | Markov chain | Stochastischer Prozess | Stochastic process | Theorie | Theory | Volatilität | Volatility | Bayes-Statistik | Bayesian inference | Schätzung | Estimation
Extent:
1 Online-Ressource (30 p)
Series:
Type of publication: Book / Working Paper
Language: English
Notes:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 29, 2022 erstellt
Other identifiers:
10.2139/ssrn.4069359 [DOI]
Classification: c58 ; C14 - Semiparametric and Nonparametric Methods ; C32 - Time-Series Models ; C11 - Bayesian Analysis ; C34 - Truncated and Censored Models
Source:
ECONIS - Online Catalogue of the ZBW
Persistent link: https://ebvufind01.dmz1.zbw.eu/10013295177
Similar items by subject
Similar items by person