@inproceedings{0ccc80f6456d4cf89f3c7b9e8bc7774b,
title = "A novel GPU implementation of eigenanalysis for risk management",
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.",
keywords = "CUDA, Eigen Decomposition, GPU, Jacobi Algorithm, Portfolio Risk",
author = "Torun, {Mustafa U.} and Akansu, {Ali N.}",
year = "2012",
doi = "10.1109/SPAWC.2012.6292956",
language = "English (US)",
isbn = "9781467309714",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
pages = "490--494",
booktitle = "2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2012",
note = "2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2012 ; Conference date: 17-06-2012 Through 20-06-2012",
}