The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized … tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the …. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions …

most important factor in reducing portfolio variance is the use of a flexible model for time varying volatility, rather … good on the present set of measures as the stochastic volatility models, with or without dynamic correlation. …

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 … applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy and in ation-gap persistence …

dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … volatility forecasting of stock returns and exchange rates. …

volatility of individual stock returns and exchange rate returns. …

by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over …

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

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 …