Kernel-Based Copula Processes

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bled, Slovenia, 2009

16 Pages Posted: 5 Aug 2009

See all articles by Sebastian Jaimungal

Sebastian Jaimungal

University of Toronto - Department of Statistics

Eddie K. H. Ng

University of Toronto - The Edward S. Rogers Sr. Department of Electrical and Computer Engineering

Date Written: April 15, 2009

Abstract

Kernel-based Copula Processes (KCPs), a new versatile tool for analyzing multiple time-series, are proposed here as a unifying framework to model the interdependency across multiple time-series and the long-range dependency within an individual time-series. KCPs build on the celebrated theory of copula which allows for the modeling of complex interdependence structure, while leveraging the power of kernel methods for efficient learning and parsimonious model specification. Specifically, KCPs can be viewed as a generalization of the Gaussian processes enabling non-Gaussian predictions to be made. Such non-Gaussian features are extremely important in a variety of application areas. As one application, we consider temperature series from weather stations across the US. Not only are KCPs found to have modeled the heteroskedasticity of the individual temperature changes well, the KCPs also successfully discovered the interdependencies among different stations. Such results are beneficial for weather derivatives trading and risk management, for example.

Keywords: Copula, Kernel Methods, Gaussian Processes, Time-Series Analysis, Heteroskedasticity, Maximum Likelihood Estimation, Financial Derivatives, Risk Management

Suggested Citation

Jaimungal, Sebastian and Ng, Eddie K. H., Kernel-Based Copula Processes (April 15, 2009). European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bled, Slovenia, 2009. Available at SSRN: https://ssrn.com/abstract=1421592

Sebastian Jaimungal

University of Toronto - Department of Statistics ( email )

100 St. George St.
Toronto, Ontario M5S 3G3
Canada

HOME PAGE: http://http:/sebastian.statistics.utoronto.ca

Eddie K. H. Ng (Contact Author)

University of Toronto - The Edward S. Rogers Sr. Department of Electrical and Computer Engineering ( email )

Toronto, Ontario
Canada

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