. The first step applies a quasi-maximum likelihood estimation procedure, with the realized volatility as a proxy for the … conditional quantile estimation. Specifically, we model the conditional standard deviation as a realized GARCH model and employ … conditional standard deviation, realized volatility, realized quantile, and absolute overnight return as innovations in the …

This paper introduces a unified multivariate overnight GARCH-Ito model for volatility matrix estimation and prediction … proposed model has two different instantaneous volatility processes for the open-to-close and close-to-open periods, while each … weighted least squares estimation procedure for estimating model parameters with open-to-close high-frequency and close …

This paper develops an efficient estimation procedure for time-varying parameter autoregressive models with stochastic … volatility. Necessary restrictions are imposed on the time-varying autoregressive parameters, thus stability conditions are … likelihood estimator. A simulation study highlights the precision of the procedure in the joint estimation of parameters and …

This paper develops an efficient estimation procedure for time-varying parameter autoregressive models with stochastic … volatility. Necessary restrictions are imposed on the time-varying autoregressive parameters, thus stability conditions are … likelihood estimator. A simulation study highlights the precision of the procedure in the joint estimation of parameters and …

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. …

-varying parameter models that incorporate both stochastic volatility and a Heckman-type two-step estimation procedure that deals with …

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 …