DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Byun, Sukjoon | - |
dc.contributor.advisor | 변석준 | - |
dc.contributor.advisor | Yoann Potiron | - |
dc.contributor.advisor | 요안 포티롱 | - |
dc.contributor.author | Yu, Seunghyeon | - |
dc.date.accessioned | 2023-06-21T19:33:01Z | - |
dc.date.available | 2023-06-21T19:33:01Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032128&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/307801 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 경영공학부, 2023.2,[vi, 144 p. :] | - |
dc.description.abstract | We develop a new nonpararmetric estimator of branching ratio matrix for high-dimensional Hawkes processes with time-varying baseline intensity. In order to incorporate high-dimensionality, we first consider a PCA analysis in the first paper, and derive its theoretical properties. Then in the second paper, we develop a nonparametric branching ratio estimator which is robust to the time-varying baseline intensity under univariate setting. In the last paper, we develop a multi-variate version of nonparametric branching ratio estimator, and apply it to explaining the micro-foundation of Heston-type volatility model. We expect that our estimator would be used to causal analysis of the point processes under time-varying external intensity. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Point processes▼aHigh-dimensional statistics▼aHigh-frequency finance▼aHawkes processes▼aVolatility | - |
dc.subject | 점과정▼a고차원 통계▼a고빈도 금융▼a호크스 과정▼a변동성 | - |
dc.title | Nonparametric inference on high-dimensional Hawkes processes | - |
dc.title.alternative | 고차원 호크스 과정의 비모수적 추정 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :경영공학부, | - |
dc.contributor.alternativeauthor | 유승현 | - |
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