Abstract
The paper develops a Markov switching multifractal model with dynamic conditional correlations. The objective is to give more flexibility to the initial bivariate Markov switching multifractal model [MSM] (Calvet et al. (2006)) by introducing some time dependency in the comovement structure. The new defined model is applied to stock index data (CAC, DAX, FTSE, NYSE) between 1996 and 2008 and compared to both the standard MSM and the DCC model of Engle and Sheppard (2002). The MSMDCC models present, in sample, better fit than the MSM and DCC models. Moreover, by combining these two setups, MSMDCC improves forecast performances for longer horizons, and provides a better understanding of market comovements during crisis episodes.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
