USING VALUE AT RISK TO ANALYZE PROBABLE MAXIMUM LOSS OF CIVIL ENGINEERING PROJECTS IN TAIWAN AREA

碩士 === 銘傳大學 === 風險管理與保險學系碩士在職專班 === 94 === Insurer or insurance superintendent couldn''t recognize the degree of risk at any moment due to that the risks of civil engineering projects are intricate and the Contractor’s All Risk Insurance (CAR) policy is non-renewable. It results in insuffi...

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Bibliographic Details
Main Authors: Wan-Tsung Yu, 游萬聰
Other Authors: Cheng-Ming Lin
Format: Others
Language:zh-TW
Published: 2006
Online Access:http://ndltd.ncl.edu.tw/handle/25a4cx
Description
Summary:碩士 === 銘傳大學 === 風險管理與保險學系碩士在職專班 === 94 === Insurer or insurance superintendent couldn''t recognize the degree of risk at any moment due to that the risks of civil engineering projects are intricate and the Contractor’s All Risk Insurance (CAR) policy is non-renewable. It results in insufficient capacity of the CAR market or insurer’s accumulation risk is at very high level without being noticed. Furthermore, it would affect the company’s performance and operation. This study is trying to look for a method to estimate Probable Maximum Loss (PML) of civil engineering projects, which combines with theory and real practice. The method could calculate and check PML of CAR in order to provide reference for every relevant person. This study is based on the concept of Risk-at-Value (VAR) that often operates in financial risk management, and integrates with the characteristic that the value of construction work is often accumulated during project period. Based on the current project classification of CAR project, the study intends to develop a brand-new method to estimate PML and to build the PML model of every project. The result of this study is contributive to every relevant person operating CAR, such as the underwriters could refer it to avoid making wrong decisions, and the insurers and contractors could also apply it to develop the risk management policy of company. The insurance superintendent could clear the degree of every risk at any moment by using the PML model.