{"id":5819,"date":"2011-10-08T17:31:29","date_gmt":"2011-10-08T14:31:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=5819"},"modified":"2011-10-08T17:40:45","modified_gmt":"2011-10-08T14:40:45","slug":"efficient-reconfigurable-design-for-pricing-asian-options","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5819","title":{"rendered":"Efficient reconfigurable design for pricing asian options"},"content":{"rendered":"<p>Arithmetic Asian options are financial derivatives which have the feature of path-dependency: they depend on the entire price path of the underlying asset, rather than just the instantaneous price. This path-dependency makes them difficult to price, as only computationally intensive Monte-Carlo methods can provide accurate prices. This paper proposes an FPGA-accelerated Asian option pricing solution, using a highly-optimised parallel Monte-Carlo architecture. The proposed pipelined design is described parametrically, facilitating its re-use for different technologies. An implementation of this architecture in a Virtex-5 xc5vlx330t FPGA at 200MHz is 313 times faster than a multi-threaded software implementation running on a Intel Xeon E5420 quad-core CPU at 2.5GHz; it is also 2.2 times faster than the Tesla C1060 GPU at 1.3 GHz.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Arithmetic Asian options are financial derivatives which have the feature of path-dependency: they depend on the entire price path of the underlying asset, rather than just the instantaneous price. This path-dependency makes them difficult to price, as only computationally intensive Monte-Carlo methods can provide accurate prices. This paper proposes an FPGA-accelerated Asian option pricing solution, [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,576,3],"tags":[592,14,1804,377,1803],"class_list":["post-5819","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-finance","category-paper","tag-computational-finance","tag-cuda","tag-finance","tag-fpga","tag-tesla"],"views":2210,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5819","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5819"}],"version-history":[{"count":2,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5819\/revisions"}],"predecessor-version":[{"id":5821,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5819\/revisions\/5821"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5819"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5819"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5819"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}