general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns … provides a feasible basis for undertaking the nontrivial task of model comparison. Furthermore, we introduce new volatility … model, namely SV-GARCH which attempts to bridge the gap between GARCH and stochastic volatility specifications. In nesting …

This work deals with multivariate stochastic volatility models, which account for a time-varying variance … the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for …

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump …

evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical …

-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP …

macroeconomic variables and multivariate stochastic volatility models with 100 stock returns. Finally, we perform impulse response …

In this paper we exploit properties of the likelihood function of the stochastic volatility model to show that it can …

wide variety of stocks, bonds and options. Evidence suggests that both the expected return and the volatility vary over … considerable effort has been devoted to the modelling of time-varying volatility. Recent attention has moved to examining the … daily stock market volatility in a sample of significant emerging stock markets using an Asymetric Volatility Model (ASV …