Sequential Gibbs Particle Filter Algorithm with an Application to Stochastic Volatility and Jumps Estimation

20 Pages Posted: 27 Jun 2018

See all articles by Jiri Witzany

Jiri Witzany

University of Economics in Prague

Milan Fičura

University of Economics, Prague - Faculty of Finance and Accounting

Date Written: June 12, 2018

Abstract

The aim of this paper is to propose and test a novel PF method called Sequential Gibbs Particle Filter allowing to estimate complex latent state variable models with unknown parameters. The framework is applied to a stochastic volatility model with independent jumps in returns and volatility. The implementation is based on a novel design of adapted proposal densities making convergence of the model relatively efficient as verified on a testing dataset. The empirical study applies the algorithm to estimate stochastic volatility with jumps in returns and volatility model based on the Prague stock exchange returns. The results indicate surprisingly weak jump in returns components and a relatively strong jump in volatility components with jumps in volatility appearing at the beginning of crisis periods.

Keywords: Bayesian methods, MCMC, Particle filters, stochastic volatility, jumps

JEL Classification: C11, C15, G1, G2

Suggested Citation

Witzany, Jiri and Fičura, Milan, Sequential Gibbs Particle Filter Algorithm with an Application to Stochastic Volatility and Jumps Estimation (June 12, 2018). Available at SSRN: https://ssrn.com/abstract=3194544 or http://dx.doi.org/10.2139/ssrn.3194544

Jiri Witzany (Contact Author)

University of Economics in Prague ( email )

Winston Churchilla Sq. 4
Prague 3, 130 67
Czech Republic

Milan Fičura

University of Economics, Prague - Faculty of Finance and Accounting ( email )

VŠE v Praze
Nám. W. Churchilla 4
130 67
Czech Republic

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