Parameter estimation in continuous stochastic volatility models
Abstract
Continuous-time di usion processes are often used in literature to
model dynamics of nancial markets. In such kinds of models a rel-
evant role is played by the variance of the process. So assumptions
on the functional form of such variance have to be made in order to
analyse the distribution of the resulting process and to make inference
on the model. In this paper the variance is also modelled by means of
a di usion process. This comes out as continuous time approximation
of a GARCH(1; 1) process. Inference on the parameters and properties
of the involved estimators are discussed under di erent choices of the
frequency data. Simulations on the model are also performed.