Analytical quasi maximum likelihood inference in multivariate volatility models
2003-08-06
Research Paper
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Quasi maximum likelihood estimation and inference in multivariate volatility models remains a challenging computational task if, for example, the dimension 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 and show in a simulation study that they clearly outperform numerical methods. As an example, we use the popular BEKK-GARCH model, for which we derive first and second order derivatives.
Keywords
Classifications using
Journal of Economic Literature (JEL) Classification System
- C14 : Semiparametric and Nonparametric Methods
- C22 : Time-Series Models; Dynamic Quantile Regressions
Automatically Extracted Terms
- model
- matrix
- derivative
- multivariate
- estimate
- multivariate volatility models
- sample
- volatility
- innovation
- covariance matrix
- covariance
- likelihood
- garch
- parameter
- vector
- t t ht
- size estimates
- hessian
- estimation
- 2 dn dn
