Estimating multivariate GARCH and Stochastic Correlation models equation by equation (4 June)

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• Prof. Jean-Michael Zakoian (Ensae and CREST)
• Wednesday 04 June 2014, 17:00-18:30
• Meade Room, Faculty of Economics, Cambridge.

If you have a question about this talk, please contact Cambridge-INET Institute, Faculty of Economics.

Event webpage: http://www.inet.econ.cam.ac.uk/our-events/Jean-Michael-Zakoian-talk

A new approach is proposed to estimate a large class of multivariate volatility models. The method is based on estimating equation-by-equation the volatility parameters of the individual returns by quasi-maximum likelihood in a first step, and estimating the correlations based on volatility-standardized returns in a second step. Instead of estimating a $d$-multivariate volatility model we thus estimate $d$ univariate GARCH -type equations plus a correlation matrix, which is generally much simpler and numerically efficient. The strong consistency and asymptotic normality of the first-step estimator is established in a very general framework. For generalized constant conditional correlation models, and also for some time-varying conditional correlation models, we obtain the asymptotic properties of the two-step estimator. Our estimator can also be used to test the restrictions imposed by a particular MGARCH specification. An application to financial series illustrates the interest of the approach.

This talk is part of the Cambridge-INET Institute, Faculty of Economics series.

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• Meade Room, Faculty of Economics, Cambridge

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