parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of …

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 …

. Our method uses (local in time) measures of risk factor risk using a multivariate stochastic volatility model for five …

. This explains why stochastic volatility often appears to be more persistent when estimated from a larger sample as then the … Artikels auf die Klasse der Stochastic Volatility (SV)-Modelle erweitert. Durch Strukturbrüche vergrößern sich die empirischen …We extend the well established link between structural change and estimated persistence from GARCH to stochastic …

distortions during the crisis, we propose generalisations with a time varying central tendency, jumps and stochastic volatility … nd that a process for the log of the observed VIX combining central tendency and stochastic volatility reliably prices …

The leverage parameter is shown to turn up as part of the third-order moment when a stochastic volatility process is …