Non-parametric dependence modeling for financial markets using conditional Kendall's tau

Abstract

In this thesis, we have examined conditional dependence in a financial context using conditional Kendall’s tau (CKT). The conditional Kendall’s tau is a measure of concordance between two random variables given some covariates. This thesis covers topics related to conditional Kendall’s tau such as (conditional) copulas. We study non-parametric estimators of the conditional Kendall’s tau using kernel density estimation and kernel regression. An application of the non-parametric estimator to the returns of thirteen different financial assets is finally provided. The assets consist of stock indices, bonds, futures and exchange rates. Further, we apply Principal Component Analysis (PCA) on the conditional Kendall’s tau data matrix to increase the interpretability. In general, it seems that conditional dependence is slightly larger in the tails for all assets. Moreover, the conditional dependence for each group of assets is discussed. It seems that the degree
of the conditional dependence relates to characteristics of an asset such as geographical properties and type of asset.