Dynamic factor, leverage and realized covariances in multivariate stochastic volatility

Realized stochastic volatility with leverage and long memory

! ! The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. In addition to the stochastic volatility model with leverage for the daily returns, ARFIMA process is jointly considered for the realized volatilities. Using a state space representation of the model, we estimate parameters by Markov chain Monte Carlo met...

Asymmetry and Leverage in Realized Volatility

Stochastic volatility and stochastic leverage

This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by m...

Dynamic Leverage and Threshold Effects in Stochastic Volatility Models*

In this paper we examine two methods for modelling asymmetries, namely dynamic leverage and threshold effects, in Stochastic Volatility (SV) models, one based on the threshold effects (TE) indicator function of Glosten, Jagannathan and Runkle (1992), and the other on dynamic leverage (DL), or the negative correlation between the innovations in returns and volatility. A general dynamic leverage ...

Time varying covariances: a factor stochastic volatility approach

We propose a factor model which allows a parsimonious representation of the time series evolution of covariances when the number of series being modelled becomes very large. The factors arise from a standard stochastic volatility model as does the idiosyncratic noise associated with each series. We use an efficient method for deriving the posterior distribution of the parameters of this model. ...