POT Model On The Application Of Catastrophe Insurance

Catastrophe Risk, literally understood, is the risk which may cause a large number of subject-matter insured in a certain geographical area suffer losses at the same time, trigger large insurance claims, and thus bring huge impact on the stability of the insurance industry. In recent years, natural disasters and man-made disasters around the globe have been increasing in frequency and intensity, which made the insurance industry be badly hit constantly. If we can adopt a variety of methods and models to quantitatively analyze and forecast catastrophe risk, the insurance industry will get great help.POT model based on extreme value theory is, therefor, used to quantitatively analyze and forecast huge claims due to catastrophe risk in this paper, where the tail estimation of population distribution of claims is a measure of insurance firm's catastrophe risk; the estimation of big fractile provides a quantitative reference for insurers to set reserves and help insurers stabilize their business; the timing and size forecast of catastrophe risk provides a reference for insurers to improve reserves at the appropriate time in order to prevent bankruptcy caused by large claims; and the surplus estimation provides a reference for insurers to set premium rate after accepting catastrophe insurance.The article is divided into four chapters. Chapter 1 is the background to this choice; Chapter 2 introduces the POT model of independent and identically-distributed time series in detail; Chapter 3 focuses on the applications of POT model in Catastrophe insurance; Chapter 4 gives the POT models of stationary seuence and first-order stationary Markov chain, and describes the applications of them in Catastrophe insurance.