Estimating Correlated Jumps and Stochastic Volatilities
IES Working Paper No. 35/2011
36 Pages Posted: 19 Aug 2011 Last revised: 18 Nov 2011
Date Written: June 18, 2011
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
JEL Classification: C11, C15, G1
Suggested Citation: Suggested Citation