The Quantization Tree algorithm has proven to be quite an efficient tool for the evaluation of financial derivatives with non-vanilla exercise rights as American-, Bermudan-or Swing options. Nevertheless, it relies heavily on a fast computation of the transition probabilities in the underlying Quantization Tree. Since this estimation is typically done by Monte-Carlo simulations, it is appealing to take advantage of the massive parallel computing capabilities of modern GPGPU-devices. We present in this article a parallel implementation of the transition probability estimation for a Gaussian 2-factor model in CUDA. Since we have to deal in this case with a huge amount of data and quite long MC-paths, it turned out that the naive pathwise parallel implementation is not optimal. We therefore present a time-layer wise parallelization which can better exploit the parallel computing power of GPGPU-devices by using faster memory structures.<\/p>\n","protected":false},"excerpt":{"rendered":"
The Quantization Tree algorithm has proven to be quite an efficient tool for the evaluation of financial derivatives with non-vanilla exercise rights as American-, Bermudan-or Swing options. Nevertheless, it relies heavily on a fast computation of the transition probabilities in the underlying Quantization Tree. Since this estimation is typically done by Monte-Carlo simulations, it is […]<\/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":[14,1804,20],"class_list":["post-3605","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-finance","category-paper","tag-cuda","tag-finance","tag-nvidia"],"views":1786,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3605","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=3605"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3605\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3605"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3605"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3605"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}