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Simple Compound Risk Model with Dependant Structure
의존구조를 가진 단순복합위험모형

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dc.contributor.advisorMyunghee Cho Paik-
dc.contributor.authorHimchan Jeong-
dc.date.accessioned2017-07-19T08:46:37Z-
dc.date.available2017-07-19T08:46:37Z-
dc.date.issued2016-08-
dc.identifier.other000000136214-
dc.identifier.urihttps://hdl.handle.net/10371/131314-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 8. Myunghee Cho Paik.-
dc.description.abstractThere have been fewer trials to address the claim severity in the development of optimal bonus-malus system (BMS), while the claim frequency has been dealt with a lot. In this article, the generalized linear mixed model (GLMM) was incorporated to address the severity, frequency, and their dependency simultaneously with 5 years insurance panel data. Also, estimated individual random effect coefficient from training set and past claim was utilized as a predictor of future loss. From the result of analysis, it was revealed that GLMM had the better fit than its alternatives including simple generalized linear model, dependency between the frequency and severity was significant, and estimated random effect coefficient predicted the future loss better as the length of training set increased. These results provide the rationale to reflect both the past frequency and past severity to construct the optimal BMS, and considering dependence between frequency and severity in the derivation of motor insurance premium.-
dc.description.tableofcontents1.Introduction 1

2.Literature review 3
2.1.The Rationale of Bonus-Malus System in Auto Insurance 3
2.2.Designing optimal BMS with past frequency and severity 5
2.3.Individual effects and dependency between frequency and severity 6

3.Data and Methodology 8
3.1.Data Description 8
3.2.Proposed Model 9

4.Model Comparison and Empirical Analysis 11
4.1.Frequency 11
4.2.Severity 14

5.Prediction 15
5.1.Frequency 16
5.2.Severity 19

6.Dependency between the Frequency and Severity 21

7.Conclusion 23

Appendix 24

References 33

국문 초록 35
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dc.formatapplication/pdf-
dc.format.extent759259 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectBMS-
dc.subjectRandom Effects Model-
dc.subjectSeverity-
dc.subjectPrediction-
dc.subjectDependence-
dc.subjectCompound Risk Model in Motor Insurance-
dc.subjectGLMM-
dc.subject.ddc519-
dc.titleSimple Compound Risk Model with Dependant Structure-
dc.title.alternative의존구조를 가진 단순복합위험모형-
dc.typeThesis-
dc.contributor.AlternativeAuthor정힘찬-
dc.description.degreeMaster-
dc.citation.pages23-
dc.contributor.affiliation자연과학대학 통계학과-
dc.date.awarded2016-08-
Appears in Collections:
College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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