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Deep learning with BSDE for pricing ELS
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 민찬호 | - |
| dc.contributor.author | 배우미 | - |
| dc.date.accessioned | 2022-11-29T03:01:19Z | - |
| dc.date.available | 2022-11-29T03:01:19Z | - |
| dc.date.issued | 2022-08 | - |
| dc.identifier.other | 32097 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/21008 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :금융공학과,2022. 8 | - |
| dc.description.tableofcontents | I. Introduction 1 II. Theoretical Background 1 1. Basic framework of DeepBSDE 1 2. Extension framework to solve ELS 4 3. Monte Carlo Simulation and Finite difference method 6 III. Empirical Results 8 1. Structure of Deep Neural Network 8 2. Test Results 9 IV. Conclusions 12 References 13 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Deep learning with BSDE for pricing ELS | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Catherine Woomih Bae | - |
| dc.contributor.department | 일반대학원 금융공학과 | - |
| dc.date.awarded | 2022. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1254213 | - |
| dc.identifier.uci | I804:41038-000000032097 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000032097 | - |
| dc.subject.keyword | Deep learning | - |
| dc.subject.keyword | ELS pricing | - |
| dc.subject.keyword | backward stochastic differential equation | - |
| dc.subject.keyword | barrier option | - |
| dc.subject.keyword | partial differential equation | - |
| dc.description.alternativeAbstract | Option price is solved by partial differential equations with specific terminal conditions. In this case, the PDE can be reformulated to BSDE. Recently, deep learning technology has been applied to evaluate the value of options using the BSDE approach. This technique is used as a method of learning the slope of a specific variable to solve BSDE including terminal conditions. In this paper, it proposes a method to evaluate the value of ELS through deep learning using BSDE algorithms and Brownian Bridge probability. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Financial Engineering > 3. Theses(Master)
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