Dissertations and Theses
Date of Award
2010
Document Type
Thesis
Department
Computer Science
Keywords
Cuda, Monte Carlo, Value at Risk
Abstract
"Value at Risk (VaR) is one of the most popular tools used to estimate the exposure to market risks, and it measures the worst expected loss at a given confidence level. Monte Carlo simulation is one of the best methods to calculate VaR and it is widely used in financial industry. Unfortunately, it is time consuming especially when the simulated samples and the number of assets in a portfolio are very large. The graphics processing unit (GPU) is a specialized multiprocessor which has highly parallel structure supporting more effective than general-purpose CPUs for a range of complex algorithms. In this paper, we will investigate the acceleration of Monte Carlo simulation by using GPU. Firstly, we will introduce the VaR conception and three basic method to estimate VaR. Then we will describe GPU computation and performance using matrix multiplication. At last, we will focus on the parallel algorithm of estimation VaR using Monte Carlo method, and implementation of VaR calculation using CUDA on GPU. Extensive experiments will be performed to show that GPU can achieve a much faster speed than Matlab, which demonstrates clear the advantage to use GPU in VaR estimation."
Recommended Citation
Wu, Wei, "Acceleration of Monte Carlo Value at Risk Estimation Using Graphics Processing Unit (GPU)" (2010). CUNY Academic Works.
https://academicworks.cuny.edu/cc_etds_theses/10