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arXiv · 1307.0373

Gaussian Process Conditional Copulas with Applications to Financial Time Series

José Miguel Hernández-Lobato·James Robert Lloyd·Daniel Hernández-Lobato
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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.

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José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato. 2013-07-01. Gaussian Process Conditional Copulas with Applications to Financial Time Series. https://arxiv.org/abs/1307.0373

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