On Modeling U.S. Product Liability Risk : An Empirical Analysis

Material type: 文書・図像類

Author: Maeda, Yujiほか

Publisher: Center for Risk Research (CRR), Shiga University

Publication date: 2005-07

Material Format: Digital

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Note (General)：: type:Technical ReportThe purpose of this paper is to seek a model that adequately describes a company’s U.S.product liability risk based on a company’...

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Material Type: 文書・図像類
Title: On Modeling U.S. Product Liability Risk : An Empirical Analysis
Author/Editor: Maeda, Yuji
Moriwaki, Naruhiko
Miyahara, Yoshio
Author Heading: Maeda, Yuji
Moriwaki, Naruhiko
Miyahara, Yoshio
Publication, Distribution, etc.: Center for Risk Research (CRR), Shiga University
Publication Date: 2005-07
Publication Date (W3CDTF): 2005-07
Periodical title: CRR Working Paper, Series B
No. or year of volume/issue: B-5
Volume: B-5
Pages: 1-20
Text Language Code: eng
Subject Heading: U.S. Product Liability
Risk Management
Risk Modeling
Compound Poisson Process
Target Audience: 一般
Note (General): type:Technical Report
The purpose of this paper is to seek a model that adequately describes a company’s U.S.product liability risk based on a company’s loss data. U.S. product liability risks are real threatsto Japanese multinational corporations which sell their products to the U.S. market since theliability risks might cost a tremendous amount of money to them not only for the liabilityclaims but also for punitive damages, legal costs, reputational costs, business opportunity costsand risk control costs. Thus, it is important for the corporations to manage the U.S. productliability risk e ectively and e ciently. The models can be used by the corporate risk managersfor the insurance cost and benefit analysis, the captive feasibility study, the risk managemente ciency analysis, among others.Based on the actual U.S. product liability loss data of a particular manufacturer 1, fivedi erent compound Poisson process models, which are Poisson processes compounding witha jump following Inverse Gaussian Distribution, Lognormal Distribution, Gamma Distribution,Pareto Distribution or Weibull Distribution, are analyzed for their goodness-of-fit to the risk.The parameters for each model are estimated by the maximum likelihood estimation method.The best fitted model is determined based on AIC criterion.As a result, the Lognormal type compound Poisson process is the best among the modelsto describe the risk. Kolmogorov-Smirnov test confirms the result that the model is “ notsignificantly ”di erent from the actual process.
identifier:CRR Working Paper, Series B, No. B-5, pp. 1-20
Access Restrictions: 限定公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 滋賀大学 : 滋賀大学学術情報リポジトリ