Analytical quasi maximum likelihood inference in multivariate volatility models
(2008) Metrika : international journal for theoretical and applied statistics — Vol. 67, n° 2, p. 219-239 (2008)
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- Abstract
- Quasi maximum likelihood estimation and inference in multivariate volatility models remains a challenging computational task if, for example, the dimension of the parameter space is high. One of the reasons is that typically numerical procedures are used to compute the score and the Hessian, and often they are numerically unstable. We provide analytical formulae for the score and the Hessian for a variety of multivariate GARCH models including the Vec and BEKK specifications as well as the recent dynamic conditional correlation model. By means of a Monte Carlo investigation of the BEKK–GARCH model we illustrate that employing analytical derivatives for inference is clearly preferable to numerical methods.
- Affiliations
Christian Albrechts Universität zu Kiel Institut für Statistik und Ökonometrie
Citations
Hafner, C., & Herwartz, H. (2008). Analytical quasi maximum likelihood inference in multivariate volatility models. Metrika : international journal for theoretical and applied statistics, 67(2), 219-239. https://doi.org/10.1007/s00184-007-0130-y (Original work published 2008)