Academic journal article Journal of Risk and Insurance


Academic journal article Journal of Risk and Insurance


Article excerpt


Catastrophes (CAT) occur infrequently, but often lead to severe losses. A report from the Insurance Information Institute indicates that U.S. hurricanes and tropical storms were responsible for 44 percent of the total losses associated with CAT events from 1991 to 2010. The most costly insured CAT losses in the United States result from weather-related CAT, in particular, hurricanes (see Table 1). For more accurate valuations of CAT insurance products and better risk management of hurricane events, it is thus important to have reliable forecasts of a hurricane frequency parameter.

Based only on CAT loss data, insurance economists employ a pure Poisson process to describe the frequency of CAT events to price CAT insurance products (e.g., Chang, Chang, and Yu, 1996; Lee and Yu, 2002,2007; Lo, Lee, and Yu, 2013). Lin, Chang, and Powers (2009) point out that the deterministic frequency parameter of the Poisson process is inadequate for CAT events and propose a doubly stochastic Poisson process for a frequency parameter of CAT events. Wu and Chung (2010) adopt a mean-reverting stochastic process as a frequency parameter of CAT events. After observing the patterns of U.S. hurricane events from 1960 to 2007, Chang, Lin, and Yu (2011) assume these events follow a two-state Markov-modulated regime-switching (known as Markov-modulated or regime-switching) Poisson process. However, all these valuation models of CAT insurance products fail to incorporate important climate and environmental variables for hurricane risk and are generally incapable of forecasting future hurricane frequency.1 Therefore, the main objectives of this article are to fill this gap in the literature by using regime-switching Poisson regressions to link the relation between the climate variables and hurricane frequency parameter and to further examine the impacts of the climate variables on the reinsurance premiums and tail value at risk (TVaR).

Questions remain about the factors that cause increased hurricane activities. Recent meteorological studies (e.g., Elsner, Jagger, and Niu, 2000; Maloney and Hartmann, 2000; Goldenberg et al., 2001; Landsea, 2005; Sutton and Hodson, 2005) attribute increases in Atlantic hurricane activity to six climate indices, including the Atlantic meridional mode (AMM), Atlantic multidecadal oscillation (AMO), El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Tropical North Atlantic (TNA), and Caribbean SST index (CAR).2 Scientists also broadly designate these climate indices as warm (positive) phase and cool (negative) phase. Warm phases in climate indices may lead to higher Atlantic hurricane activities, whereas cool phases in climate indices may lead to lower Atlantic hurricane activities.

There is also a perspective that global climate change--rather than natural climate cycles--is playing a dominant role in generating higher levels of hurricane activity (Knutson and Tuleya, 2004; Barnett et al., 2005; Emanuel, 2005; Webster et al., 2005; Webster et al., 2006). The report by the Intergovernmental Panel on Climate Change (2014, chap. 26) indicates that North America's climate has changed and some relevant societally changes have been attributed to anthropogenic causes. North American ecosystems are under increasing stress from rising temperatures, carbon dioxide ([CO.sub.2]) concentrations, and sea levels and are particularly vulnerable to climate extremes. Studies have shown that the most robust trends in extratropical cyclones over North America lean toward being more frequent. Increased [CO.sub.2] levels are found to be related to the high frequency of the most intense hurricane events (e.g., Knutson and Tuleya, 2004).

Gray (1984) generates a complex methodological and statistical model of global weather patterns to forecast hurricane frequency over the Atlantic Basin. His model is one of the most publicly discussed models on hurricane frequency. …

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