Statistical Modelling 8 (2008), 263–283
GLM-methods for volatility models
Joan del Castillo
Servei d'Estadística de la UAB Universitat Autonoma de Barcelona
Spain
Younjo Lee
Department of Statistics, Seoul National University,
San 56-1, Sillim-dong, Gwanak-gu,
Seoul 151-742
South Korea
eMail:
youngjo@plaza.snu.ac.kr
Abstract:
We propose a multivariate volatility model for the behaviour of eight
international equity indices. We show that many volatility models with
heavy tails in financial work can be viewed as the GLM class of models
with random effects in the dispersion. Hence, the h-likelihood approach,
which provides efficient and simpler algorithms for GLM class, can be
used as an estimation method for models used in finance. A comparison
of the h-likelihood estimators with the ML estimators is made and its
relative merits are discussed.
Keywords:
Generalized linear models; Lévy processes;
normal inverse Gaussian distribution; likelihood for random-effect models;
portfolio selection
back