Scenario tree generation and stochastic programming under heston framework = 헤스톤 모형을 활용한 시나리오 트리 생성 및 추계 계획법

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This thesis studies on the impact of appropriately modeling the fatness of left tail of the assets’ log-return distributions on the quality of scenario tree, and furthermore, on the behavior of solutions when it is applied to stochastic programming problem. To model the fat left tail, we use the Heston model. First, we introduce how to extend the single-asset Heston model to multi-asset Heston model. Moreover, we investigate the parameter calibration methods of the multi-asset Heston model. Second, we survey on the scenario tree generation methods with the focus on Moment-Matching. We also cover about the quality evaluation methods of the scenario trees. Third, we briefly introduce the formulation of Multi-Stage Stochastic Programming problem that we use in this thesis. Last, we investigate on the impact of appropriately modeling the left tail using Heston model on the solutions of stochastic programming problem, and we compare the results with the Geometric Brownian Motion model.
Advisors
Kim, Woo Changresearcher김우창researcher
Description
한국과학기술원 :산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2019.2,[iii, 50 p. :]

Keywords

Heston model▼afat-tailed distribution▼ascenario tree▼astochastic programming; 헤스톤 모형▼a두꺼운 꼬리 분포▼a시나리오 트리▼a추계적 계획법

URI
http://hdl.handle.net/10203/266232
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843204&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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