stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors …

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 …

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 …

-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under …

Empirical volatility studies have discovered nonstationary, long-memory dynamics in the volatility of the stock market … found with nonparametric estimates of the fractional differencing parameter d, for financial volatility. In this paper, a …, stochastic volatility (SV-FIAR) model. Joint estimates of the autoregressive and fractional differencing parameters of volatility …

accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an … volatility processes and mixture distributions for the irregular components and the common cycle disturbances enable us to … that time-varying volatility is only present in the a selection of idiosyncratic components while the coefficients driving …

stochastic volatility model. Exchange rate pass-through is divided into impacts of exchange rate fluctuations to import prices …

This paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy …-tailed student-t distributions (with unknown degrees of freedom) and covariate effects in the observation and volatility equations … applied in detail to daily returns data on the S&P 500 index where several stochastic volatility models are formally compared …