Estimation of SVJD Models with Bayesian Methods and Power-Variation Estimators

35 Pages Posted: 1 Aug 2018

See all articles by Milan Fičura

Milan Fičura

University of Economics, Prague - Faculty of Finance and Accounting

Jiri Witzany

University of Economics in Prague

Date Written: March 30, 2017

Abstract

Methodology is proposed of how to utilize high-frequency power-variation estimators in the Bayesian estimation of Stochastic-Volatility Jump-Diffusion (SVJD) models. Realized variance is used as an additional source of information for the estimation of stochastic variances, while the Z-Estimator is used as an additional source of information for the estimation of asset price jumps. The models are estimated by a combination of a MCMC algorithm and a SIR Particle Filter. The performance of the models is evaluated on simulated times series as well as real world financial time series of the 4 major foreign exchange rates.

Keywords: Stochastic Volatility, Bayesian Inference, MCMC, Particle Filters, Realized Variance, Bipower Variation, Z-Estimator, Jump Clustering, Self-Exciting Jumps, Hawkes Process

JEL Classification: C11, C14, C15, C22, G1

Suggested Citation

Fičura, Milan and Witzany, Jiri, Estimation of SVJD Models with Bayesian Methods and Power-Variation Estimators (March 30, 2017). Available at SSRN: https://ssrn.com/abstract=3212975 or http://dx.doi.org/10.2139/ssrn.3212975

Milan Fičura (Contact Author)

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

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

Jiri Witzany

University of Economics in Prague ( email )

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

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