{"id":{"repo_id":"cuny","oai_identifier":"oai:academicworks.cuny.edu:cc_etds_theses-1009"},"canonical_url":"https://search.dev.ndltd.org/etd/cuny/oai:academicworks.cuny.edu:cc_etds_theses-1009","repository":{"repo_id":"cuny","name":"City University of New York - City College","base_url":"https://academicworks.cuny.edu/do/oai/"},"display":{"title":"Acceleration of Monte Carlo Value at Risk Estimation Using Graphics Processing Unit (GPU)","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.\"","abstract_html":""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."","abstract_has_math":false,"creators":["Wu, Wei"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T01:56:34Z","subjects":["Cuda","Monte Carlo","Value at Risk","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/cc_etds_theses/10","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Wu, Wei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cuda","Monte Carlo","Value at Risk","Computer Sciences","Physical Sciences and Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/cc_etds_theses/10"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"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.\""]},{"key":"dc:title","label":"Title","values":["Acceleration of Monte Carlo Value at Risk Estimation Using Graphics Processing Unit (GPU)"]}]}],"canonical_facts":{"dc:creator":["Wu, Wei"],"dc:description.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.\""],"dc:identifier":["https://academicworks.cuny.edu/cc_etds_theses/10"],"dc:subject":["Cuda","Monte Carlo","Value at Risk","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["Acceleration of Monte Carlo Value at Risk Estimation Using Graphics Processing Unit (GPU)"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T01:56:34Z"}