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Estimating Correlated Jumps and Stochastic Volatilities

Jiří Witzany

No 2011/35, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

Abstract: We formulate a bivariate stochastic volatility jump-diffusion model with correlated jumps and volatilities. An MCMC Metropolis-Hastings sampling algorithm is proposed to estimate the model’s parameters and latent state variables (jumps and stochastic volatilities) given observed returns. The methodology is successfully tested on several artificially generated bivariate time series and then on the two most important Czech domestic financial market time series of the FX (CZK/EUR) and stock (PX index) returns. Four bivariate models with and without jumps and/or stochastic volatility are compared using the deviance information criterion (DIC) confirming importance of incorporation of jumps and stochastic volatility into the model.

Keywords: jump-diffusion; stochastic volatility; MCMC; Value at Risk; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C15 G1 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2011-11, Revised 2011-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst, nep-ore and nep-rmg
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Journal Article: Estimating Correlated Jumps and Stochastic Volatilities (2013) Downloads
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