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Dynamic Covariance Models for Multivariate Financial Time Series

Wu, Y and Hernández-Lobato, JM and Ghahramani, Z (2013) Dynamic Covariance Models for Multivariate Financial Time Series.

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Abstract

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

Item Type: Article
Uncontrolled Keywords: stat.ME stat.ME stat.ML
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:47
Last Modified: 06 Oct 2014 01:22
DOI:

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