| Wasserstein Distributionally Robust Option Pricing |
| Received:January 20, 2020 Revised:October 24, 2020 |
| Key Words:
option pricing Wasserstein distance distributionally robust optimization
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| Fund Project:Supported by the National Natural Science Foundation of China (Grant Nos.11571061; 11401075; 11971092) and the Fundamental Research Funds for the Central Universities (Grant No.DUT17RC(4)38). |
| Author Name | Affiliation | | Wei LIU | School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China
School of Applied Mathematics, Beijing Normal University, Zhuhai, Guangdong 519087, P. R. China | | Li YANG | School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China | | Bo YU | School of Mathematical Sciences, Dalian University of Technology, Liaoning 116024, P. R. China |
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| Abstract: |
| In this paper, the option pricing problem is formulated as a distributionally robust optimization problem, which seeks to minimize the worst case replication error for a given distributional uncertainty set (DUS) of the random underlying asset returns. The DUS is defined as a Wasserstein ball centred the empirical distribution of the underlying asset returns. It is proved that the proposed model can be reformulated as a computational tractable linear programming problem. Finally, the results of the empirical tests are presented to show the significance of the proposed approach. |
| Citation: |
| DOI:10.3770/j.issn:2095-2651.2021.01.010 |
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