A novel GPU implementation of eigenanalysis for risk management
Document Type
Conference Proceeding
Publication Date
11-2-2012
Abstract
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. © 2012 IEEE.
Identifier
84868014756 (Scopus)
ISBN
[9781467309714]
Publication Title
IEEE Workshop on Signal Processing Advances in Wireless Communications Spawc
External Full Text Location
https://doi.org/10.1109/SPAWC.2012.6292956
First Page
490
Last Page
494
Recommended Citation
Torun, Mustafa U. and Akansu, Ali N., "A novel GPU implementation of eigenanalysis for risk management" (2012). Faculty Publications. 18036.
https://digitalcommons.njit.edu/fac_pubs/18036
