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

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. …

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 …

Cholesky multivariate stochastic volatility model. It establishes that estimated covariance matrices, obtained under … alternative orderings of variables, are systemically different when the data exhibits independent volatility dynamics … individual volatility paths becomes. This paper shows that this property is important for empirical applications as alternative …

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

We propose a moving average stochastic volatility in mean model and a moving average stochastic volatility model with … leverage. For parameter estimation, we develop efficient Markov chain Monte Carlo algorithms and illustrate our methods, using … simulated data and a real data set. We compare the proposed specifications against several competing stochastic volatility …

Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit … volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based … on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility …

subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of … the observed time series. We develop a simulated maximum likelihood estimation method based on importance sampling and … increased during the 2008 financial crisis while it has recently returned to its pre-crisis level. The extracted volatility …

A two-step estimation method of stochastic volatility models is proposed. In the first step, we nonparametrically … estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed … estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps …