Abstract:
Dependence of financial variables is a key concern for financial risk analysts and
investors. With the increasing application of copula in finance, there is need to have
robust and consistent estimators of copula parameters. Though the existing parametric
and semi parametric estimators are robust, they capture static dependence between
variables. There is documented evidence of time variation of the dependence between
financial time series data. This thesis uses the moving window estimator to capture time
varying dependence of financial time series data. The moving window estimation
technique is formulated as an extension of the semi parametric copula based
multivariate dynamical model to the changing values in the sub samples. Thus the
moving window estimator inherits the consistency and asymptotic normality of the semi
parametric estimator. The thesis applies the semi parametric and moving window
copula estimation techniques to capture and test for dependence of the daily equity and
foreign exchange returns data in Kenya. The multivariate dependence test reveals
significant positive correlation between the Nairobi Securities Exchange 20-share
index and the Kenya Shilling versus the United States dollar exchange rate. Amongst
the parametric copula models fitted into the data, the t copula with 10 degrees of
freedom is found to be the most appropriate for capturing the static dependence over the
entire study period
