IMPACT OF UNCERTAINTY IN CATASTROPHE LOSSES ON INSURANCE DERIVATIVES

Abstract eng:
The amount of economical losses associated with natural disasters has significantly increased in recent years reflecting such factors as the increased frequency and severity of the hazards as well as the increase in property wealth. The US insurance industry has a capital of 350 billion dollars to cover property valued at about 30 trillion dollars. This has prompted insurance companies to turn to the financial market to provide them with the capability of handling CAT exposure. Insurance products are valuated based on stochastic models of losses. In loss modeling, two sources of uncertainty are noted: the choice of the stochastic model itself and the estimated values of the parameters used in these models. Stochastic models typically consist of a combination of Poisson processes modeling the number of catastrophe occurrences along with probabilistic distributions for claims sizes during each event. This paper aims at quantifying the uncertainty in assessing catastrophe losses and its impact on the pricing of insurance derivatives. Simulations of several models is carried out with an emphasize on quantifying the significance of the uncertainty in the parameters on the pricing of financial securities. Prices of call-spreads are evaluated in each case and compared with real prices. The catastrophe derivatives considered include call spreads traded on the CBOT (Chicago Board Of Trade) and based on the PCS (Property Claim Service) loss index. The objective of the present research is to identify and so quantify the impact of uncertainty management on the perceived exposure, risk and hence pricing of financial derivatives associated with CAT insurance. This uncertainty can be reduced through better information management. As such, this paper can be viewed as providing a motivation to some of the recent work in applying advanced technologies to catastrophe loss estimation.

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