Last update 26-5-2000 by fkc
This thesis concerns estimation of stochastic volatility models in
finance, discrete time models as well as continuous time
models.
Returns of financial time series are investigated for
characteristics that may be used for volatility modelling. The
well-known GARCH models as well as some related models are
investigated, and their abilities to capture the characteristics of
financial returns are evaluated in a large estimation study where the
traditional estimation methods as well as an alternative recursive
estimation method are used.
Parameters of bivariate Stochastic Volatility Models are
traditionally very difficult to estimate. This thesis investigates an
indirect estimation procedure, were the parameters of continuous time
models are obtained from discrete time model parameter estimates. A
new discrete time stochastic volatility model, SSSH-GARCH, is
introduced.