There will be an invited talk.<\/p>\n
Title: Reinforcement Hedging<\/strong><\/span> There will be an invited talk. Title: Reinforcement Hedging Speaker: Kiseop Lee (Professor, Purdue University) Time: 2022 December 2ed, 16:00 ~ 16:50 Place: 27-325 Abstract: We investigate option hedging in an incomplete market with a reinforcement learning algorithm called double deep Q-network (DDQN). The agent of DDQN learns the optimal policy that generates replicating portfolios […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"_links":{"self":[{"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=\/wp\/v2\/posts\/1485"}],"collection":[{"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1485"}],"version-history":[{"count":2,"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=\/wp\/v2\/posts\/1485\/revisions"}],"predecessor-version":[{"id":1490,"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=\/wp\/v2\/posts\/1485\/revisions\/1490"}],"wp:attachment":[{"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1485"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1485"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/hyke.snu.ac.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1485"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Speaker: Kiseop Lee<\/strong> (Professor, Purdue University)<\/span>
Time: 2022 December 2ed, 16:00 ~ 16:50<\/strong><\/span>
Place: 27-325<\/strong><\/span>
Abstract:<\/span>
We investigate option hedging in an incomplete market with a reinforcement learning algorithm called double deep Q-network (DDQN). The agent of DDQN learns the optimal policy that generates replicating portfolios without prior knowledge of the stochastic representation of an underlying asset price process. First, we interpret a mean-variance approach in quadratic hedging in a reinforcement learning framework. This study includes three simulation studies for different underlying asset price processes: geometric Brownian motion (GBM), Heston, and GBM with compound Poisson jumps. For each study, a DDQN agent learns the optimal policy, and we compare the algorithm performance with delta hedging. Second, we discuss limitations that stem from the structure of reinforcement learning in finance.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"