Gaussian process conditional copulas with applications to financial time series
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
NIPSDate
2013Citation
NIPS 2013: Annual Conference on Neural Information Processing Systems, 26. Lake Tahoe: NIPS, 2013. 1-9.ISBN
9781632660244Funded by
We thank David López-Paz and Andrew Gordon Wilson for interesting discussions. José Miguel Hernández-Lobato acknowledges support from Infosys Labs, Infosys Limited. Daniel Hernández- Lobato acknowledges support from the Spanish Dirección General de Investigación, project ALLS (TIN2010-21575-C02-02).Editor's Version
http://papers.nips.cc/paper/5084-gaussian-process-conditional-copulas-with-applications-to-financial-time-seriesSubjects
InformáticaNote
This is an electronic version of the paper presented at the Annual Conference on Neural Information Processing Systems, held in Lake Tahoe on 2013Rights
© Los autoresAbstract
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.
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Google Scholar:Hernández-Lobato, José Miguel
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Lloyd, James R.
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Hernández Lobato, Daniel
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