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Activity Number: 402 - Variance, Change Points, and Outliers
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #323610
Title: Multivariate Stochastic Volatility Modeling via the Spectral Decomposition
Author(s): Victor Solo*
Companies: University of New South Wales
Keywords: Stochastic Volatility ; Lie Group ; Asset Pricing

A lot of recent effort has gone into multivariate stochastic volatility modeling because of its compelling applications in asset pricing. Current approaches include multivariate GARCH, Cholesky decomposition and the Wishart process method. However these methods do not directly model the eigenvalues which carry interpretable information about the evolution of volatility. After reviewing related work, we sketch, in a simple case, the beginnings of a new approach based on spectral decomposition of the covariance matrix. The matrix of eigenvectors evolves as a Brownian motion in a Lie group and a scalar stochastic volatility model is posited for eigenvalues. We illustrate with a simple example.

Authors who are presenting talks have a * after their name.

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