A0338
Title: Multivariate Markov switching BEKK models: Filtering, estimation and data analysis
Authors: Jie Cheng - Keele University (United Kingdom) [presenting]
Abstract: The focus is on extending the standard multivariate BEKK model, as detailed in an existing study, by allowing both the unconditional correlation and the parameters to be driven by an unobservable Markov chain. In particular, two estimation algorithms are proposed using extended Kalman filters derived from suitable state space representations of the considered model. Numerical examples make evident the effectiveness of the proposed nonlinear estimations. Finally, real-data applications on some financial returns show empirical evidence that the high volatility persistence and correlation changes of such returns can be well explained by estimating multivariate Markov switching BEEK parameters via the two efficient proposed algorithms.
