DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김동규 | - |
dc.contributor.author | Shin, Minseok | - |
dc.contributor.author | 신민석 | - |
dc.date.accessioned | 2024-08-08T19:31:58Z | - |
dc.date.available | 2024-08-08T19:31:58Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1100173&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/322258 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 경영공학부, 2024.2,[vii, 171 p. :] | - |
dc.description.abstract | The wide availability of financial big data has significantly increased our ability to understand social and economic phenomena. However, utilizing big data often introduces new challenges due to its complex structure and high-dimensionality. For instance, in working with high-frequency data, we encounter complexities caused by microstructure noise and heavy-tailed distributions. Similarly, with high-dimensional data, the curse of dimensionality becomes a critical issue to address. Therefore, it is important to develop effective and efficient estimation methods for big data. In this thesis, we develop the well-performing nonparametric estimation methods and parametric models for the financial big data. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 두꺼운 꼬리▼a최적성▼a요인 모형▼a강건 추정▼a고차원 | - |
dc.subject | Heavy-tail▼aOptimality▼aFactor model▼aRobust estimation▼aHigh-dimensionality | - |
dc.title | High-frequency financial big data analysis | - |
dc.title.alternative | 고빈도 금융 빅데이터 분석 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :경영공학부, | - |
dc.contributor.alternativeauthor | Kim, Donggyu | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.