PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Copula Processes
Andrew Wilson and Zoubin Ghahramani
In: Advances in Neural Information Processing Systems 23, 6-9 Dec 2010, Vancouver, Canada.

Abstract

We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. As an example, we develop a stochastic volatility model, Gaussian Copula Process Volatility (GCPV), to predict the latent standard deviations of a sequence of random variables. To make predictions we use Bayesian inference, with the Laplace approximation, and with Markov chain Monte Carlo as an alternative. We find our model can outperform GARCH on simulated and financial data. And unlike GARCH, GCPV can easily handle missing data, incorporate covariates other than time, and model a rich class of covariance structures.

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EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7062
Deposited By:Andrew Wilson
Deposited On:27 January 2011