A Novel GPU Implementation of Eigen Analysis for Risk Management
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102 USA
13th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2012
@inproceedings{torun2012novel,
title={A Novel GPU Implementation of Eigen Analysis for Risk Management},
author={Torun, M.U. and Akansu, A.N.},
booktitle={IEEE SPAWC},
year={2012}
}
Portfolio risk is commonly defined as the standard deviation of its return. The empirical correlation matrix of asset returns in a portfolio has its intrinsic noise component. This noise is filtered for more robust performance. Eigendecomposition is a widely used method for noise filtering. Jacobi algorithm has been a popular eigensolver technique due to its stability. We present an efficient GPU implementation of parallel Jacobi eigensolver for noise filtering of empirical correlation matrix of asset returns for portfolio risk management. The computational efficiency of the proposed implementation is about 34% better than our most recent study for an investment portfolio of 1024 assets.
July 29, 2012 by hgpu
