| Summary: | 碩士 === 銘傳大學 === 風險管理與保險學系碩士班 === 105 === Abstract: Climate change has increased the likelihood and severity of natural disasters in recent years. Wildfire is a critical issue in natural disaster, such as the wildfire in Indonesia; California, U.S.; Yunnan, China; Nantou, Taiwan …etc. This study explored the feasibility to diverse risk for wildfire with pure risk premium in U.S. The pure risk premium utilized VaR and CVaR value to estimate probable yearly aggregate loss (MPY), and applied Monde Carlo method and historical manner to simulate VaR and CVaR values, and utilized loss data and total output value for wildfire in U.S.
The study used @risk simulation software to simulate VaR and CVaR values. The @Risk have more functions and test methods, which could be utilized to fit and test distribution structure of wildfire loss data. In order to make the simulation value close to the actual value, we use the Monte Carlo simulation method to simulate the risk value (95% and 99% confidence level) and simulate the rate, insurance amount and premium.
Finally, compare with the Chinese examples to determine whether the analog values are sufficient.
In this study, BetaGeneral function fitted U.S. forest fire rates and Monte Carlo 99% CVaR was close to actual rate of 0.892 ‰, and InvGauss function fitted U.S. forest fire premium and 99% CVaR was close to actual rate 2.27 ‰. Taiwan's forest fire loss is too small to be a single risk and this study suggests to formulate a comprehensive insurance policy for the market.
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