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

The goal of this article is an exact Bayesian analysis of the Heston (1993) stochastic volatility model. We carefully … study the effect different parameterizations of the latent volatility process and the parameters of the volatility process …

estimate complex latent state variable models with unknown parameters. The framework is applied to a stochastic volatility … model with independent jumps in returns and volatility. The implementation is based on a novel design of adapted proposal … algorithm to estimate stochastic volatility with jumps in returns and volatility model based on the Prague stock exchange …

Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are … divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical … multivariate stochastic volatility model is proposed as a robust alternative. …

capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in … evidence for time-varying volatility in the yield factors. This is mostly true for the level and slope volatility revealing … also the highest persistence. It turns out that the inclusion of stochastic volatility improves the model's goodness …

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 …

Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent … countries or regions. This paper estimates dynamic panel data models with stochastic volatility by maximizing an approximate … particle filter-based estimator. When the volatility of volatility is high, or when regressors are absent but stochastic …

is given for the stochastic volatility model with leverage. …

volatility model for asset returns, a Gaussian nonlinear local-level model for interest rates, and a multivariate stochastic … volatility model for the realized covariance matrix of asset returns …