CUED Publications database

Gaussian process conditional copulas with applications to financial time series

Hernández-Lobato, JM and Lloyd, JR and Hernández-Lobato, D (2013) Gaussian process conditional copulas with applications to financial time series. In: UNSPECIFIED.

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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 timevarying copula methods.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:29
Last Modified: 16 Nov 2017 02:19
DOI: