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SUMMARY:Estimating multivariate GARCH and Stochastic Correlation models eq
 uation by equation (4 June) - Prof. Jean-Michael Zakoian (Ensae and CREST)
   
DTSTART:20140604T160000Z
DTEND:20140604T173000Z
UID:TALK52709@talks.cam.ac.uk
CONTACT:Cambridge-INET Institute\, Faculty of Economics
DESCRIPTION:A new approach is proposed to estimate a large class of multiv
 ariate volatility models. The method is based on estimating equation-by-eq
 uation the volatility parameters of the individual returns by quasi-maximu
 m likelihood in a first step\, and estimating the correlations based on vo
 latility-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 ti
 me-varying conditional correlation models\, we obtain the asymptotic prope
 rties of the two-step estimator. Our estimator can also be used to test th
 e restrictions imposed by a particular MGARCH specification. An applicatio
 n to financial series illustrates the interest of the approach.
LOCATION:Meade Room\, Faculty of Economics\, Cambridge
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