tested. Empirical results are confronted with the predictions of stochastic volatility models. The study reveals that while … the modeling of stochastic volatility gives more robust models, the market does not process information on the realized …

vector autoregressive models that incorporate some form of time-varying volatility, precisely stochastic volatility (both … with constant and time-varying autoregressive coefficients), stochastic volatility following a stationary AR process …, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The comparison is based on the accuracy of …

large. We contribute to this strand of the literature proposing a block-type parameterization for multivariate stochastic … volatility models. The empirical analysis on stock returns on US market shows that 1% and 5 % Value-at-Risk thresholds based on …

. This explains why stochastic volatility often appears to be more persistent when estimated from a larger sample as then the …We extend the well established link between structural change and estimated persistence from GARCH to stochastic … volatility (SV) models. Whenever structural changes in some model parameters increase the empirical autocorrelations of the …

This paper generalizes the basic Wishart multivariate stochastic volatility model of Philipov and Glickman (2006) and …

This paper describes a maximum likelihood method for estimating the parameters of Heston's model of stochastic … volatility using data on an underlying market index and the prices of options written on that index. Parameters of the physical …