DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김경국 | - |
dc.contributor.author | Yang, Joonwoo | - |
dc.contributor.author | 양준우 | - |
dc.date.accessioned | 2024-08-08T19:30:35Z | - |
dc.date.available | 2024-08-08T19:30:35Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1097700&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321872 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 경영공학부, 2024.2,[iii, 48 p. :] | - |
dc.description.abstract | In this thesis, we examine the empirical performance of rough volatility models within the S&P 500 and KOSPI 200 Index options markets. We thoroughly investigate the rough Heston and rough Bergomi models, employing a neural network-based calibration method to expedite calibration times. Our findings suggest that rough volatility models generally outperform the classical Heston model in both in-sample and out-of-sample pricing performance. Their hedging performance, however, does not consistently exhibit improvement, except for a notable enhancement in KOSPI 200 put options. This research contributes to filling the void in empirical studies on rough volatility models, robustly validating their superior performance in two prominent options markets. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 거친 변동성 모형▼a신경망 기반 보정▼a옵션 가격 결정 및 헤징 성과 | - |
dc.subject | rough volatility models▼aneural network-based calibration▼aoption pricing and hedging performance | - |
dc.title | Empirical performance of rough volatility models | - |
dc.title.alternative | 거친 변동성 모형의 성과 분석 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :경영공학부, | - |
dc.contributor.alternativeauthor | Kim, Kyoung-Kuk | - |
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