Sample average approximation of CVaR-based hedging problem with a deep-learning solution
文献类型：期刊论文
| 作者 | Peng, Cheng1,2; Li, Shuang1,2; Zhao, Yanlong1,2; Bao, Ying3 |
| 刊名 | NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE
 |
| 出版日期 | 2021-04-01 |
| 卷号 | 56页码:14 |
| 关键词 | Conditional Value-at-Risk Hedging strategies Deep learning Theoretical guarantee Sample average approximation Uniform convergence |
| ISSN号 | 1062-9408 |
| DOI | 10.1016/j.najef.2020.101325 |
| 英文摘要 | Conditional Value-at-Risk (CVaR) is an extremely popular risk measure in finance and is usually optimized to reduce the risk of large losses. This paper considers the CVaR optimization problem for hedging a portfolio of derivatives with bounded constraints. We focus on minimizing the CVaR of the loss of the hedging portfolio by a deep learning solution because of its promising application to classic portfolio optimization. As the cost objective function in the deep learning framework, the CVaR does not have a closed-form expression, but it can be estimated by using the i.i.d samples average approximation method. While many works have adopted minimizing the estimated CVaR to obtain the optimal solution, they lack theoretical performance guarantees for sample-based solutions. This paper attempts to bridge this gap. On the one hand, we introduce a typical deep neural network architecture for training the optimal hedging strategies, which helps us to analyze the properties of function set for this neural network. On the other hand, we offer a sufficient condition to guarantee that the optimal strategies obtained by using the estimated CVaR can be assured in practical applications. In particular, we prove that the uniform convergence in probability of the estimated CVaR to CVaR over a set of functions, which are generated by the proposed deep neural network. Numerical experiments verify the proposed sufficient condition and demonstrate the feasibility and superiority of this approach. |
| WOS研究方向 | Business & Economics |
| 语种 | 英语 |
| WOS记录号 | WOS:000631532300003 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/58284]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Zhao, Yanlong |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, KLSC, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 3.Ind & Commercial Bank China, Beijing 100032, Peoples R China |
| 推荐引用方式 GB/T 7714 | Peng, Cheng,Li, Shuang,Zhao, Yanlong,et al. Sample average approximation of CVaR-based hedging problem with a deep-learning solution[J]. NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE,2021,56:14. |
| APA | Peng, Cheng,Li, Shuang,Zhao, Yanlong,&Bao, Ying.(2021).Sample average approximation of CVaR-based hedging problem with a deep-learning solution.NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE,56,14. |
| MLA | Peng, Cheng,et al."Sample average approximation of CVaR-based hedging problem with a deep-learning solution".NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE 56(2021):14. |
入库方式： OAI收割
来源：数学与系统科学研究院
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