In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

This article presents two specifications for the stochastic volatility model, in order to compare them for the chosen period. It is used the stochastic volatilit model with stationary variance and non stationary variance, similar to Morais and Portugal, to model the Bovespa Index between 2000...

Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting...

The financial econometrics literature includes several multivariate GARCH models where the model parameter matrices depend on a clustering of financial assets. Those classes might be defined a priori or data-driven. When the latter approach is followed, one method for deriving asset groups is...

Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore...

This paper introduces a novel quantile approach to harness the high-frequency information and improve the daily conditional quantile estimation. Specifically, we model the conditional standard deviation as a realized GARCH model and employ conditional standard deviation, realized volatility,...

This paper introduces a unified multivariate overnight GARCH-Ito model for volatility matrix estimation and prediction both in the low- and high-dimensional set-up. To account for whole-day market dynamics in the financial market, the proposed model has two different instantaneous volatility...

This article studies the risk forecasting properties of three realized volatility models for three Chinese individual stocks, and reveals the important role that jumps can play in risk prediction. I firstly investigate dynamic pattern of jumps in three Chinese stocks, and find that relative to...

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility matrix and facilitates estimation of large...

Using a unique database, this paper examines the interconnection among stress indicators of the Spanish financial markets during the period of January 1999 to April 2021, applying both the connectedness framework and the Time-Varying Parameter Vector Autoregressive connectedness approach. Our...