Microinsurance markets have exhibited strong growth rates in recent years. Great parts of the industry are, however, challenged by fundamental issues of providing insurance products, one of the most significant of which is pricing risk. In this paper, we provide a non-technical analysis of insurance pricing problems and a review of the set of opportunities that can address some of the specific pricing constraints in microinsurance markets. A key contribution of this paper is the investigation of conventional techniques as potential solutions for improving the pricing of insurance risk in microinsurance markets.
Insurance companies are important financial institutions exposed to natural and man-made disasters. We conduct a comprehensive examination of existing asset pricing models in the US insurance universe (1988-2013) and propose an insurance-specific asset pricing model. We find that extant asset pricing models fail to explain the cross-section of insurance stock returns. Instead, we provide evidence that the factors of the insurance-specific model (book-to-market ratio, short-term reversal, illiquidity, and cashflow volatility) are priced in the cross-section of property/liability insurance stocks. Our model takes into account both insurance-specific anomalies primarily related to the insurance business cycle and externalities imposed by catastrophe risk.
The risk of infrastructure investments is driven by unique factors that cannot be well described by standard asset class factor models. We thus create a nine-factor model based on infrastructure-specific risk exposure, i.e., market risk, size, value, momentum, cash flow volatility, leverage, investment growth, term risk, and default risk. We empirically test our model on a large dataset of U.S. infrastructure stocks in different subsectors (utility, telecommunication, and transportation) and over a long period of time (1983 to 2011). The new factor model is able to capture the variation of infrastructure returns better than the Fama/French three-factor, the Carhart four-factor or the extended Fung/Hsieh eight-factor models. Thus, our model helps to improve the evaluation of infrastructure funds and to better determine the cost of capital of infrastructure firms, something that is increasingly relevant in light of the growing need for privately financed infrastructure projects.
Property-casualty (P&C) insurers are exposed to rare but severe natural disasters. This paper analyzes the relation between catastrophe risk and the implied volatility smile of insurance stock options. We find that the slope is significantly steeper compared to non-financials and other financial institutions. We show that this effect has increased over time, suggesting a higher risk compensation for catastrophic events. We are able to link the insurance-specific tail risk component derived from options with the risk spread from catastrophe bonds. Our results provide an accurate, high-frequency calculation for catastrophe risk linking the traditional derivatives market with insurance-linked securities (ILS).
