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

-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 …

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. …

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 …

and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very …

A wide variety of conditional and stochastic variance models has been used to estimate latent volatility (or risk). In … both the conditional and stochastic volatility literature, there has been some confusion between the definitions of … volatilities. Then we develop a new asymmetric volatility model, which takes account of small and large, and positive and negative …