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Gaussian Process Conditional Copulas with Applications to Financial Time Series
José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

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 innacurate 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.

Author Information

José Miguel Hernández-Lobato (University of Cambridge)
James R Lloyd (University of Cambridge)
Daniel Hernández-lobato (Universidad Autonoma de Madrid)

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