Gaussian Process Conditional Copulas with Applications to Financial Time Series
Abstract
The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms of a copula function. Typically the copula function is assumed to be constant but this may be inaccurate when there are covariates that could have a large influence on the dependence structure of the data. To account for this, a Bayesian framework for the estimation of conditional copulas is proposed. In this framework the parameters of a copula are non-linearly related to some arbitrary conditioning variables. We evaluate the ability of our method to predict time-varying dependencies on several equities and currencies and observe consistent performance gains compared to static copula models and other time-varying copula methods.
Cite
@article{arxiv.1307.0373,
title = {Gaussian Process Conditional Copulas with Applications to Financial Time Series},
author = {José Miguel Hernández-Lobato and James Robert Lloyd and Daniel Hernández-Lobato},
journal= {arXiv preprint arXiv:1307.0373},
year = {2013}
}