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Measuring U.S. Hurricane Risk Associated with Natural Climate Cycle and Global Warming Effects

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Published/Copyright: July 5, 2013
Asia-Pacific Journal of Risk and Insurance
From the journal Asia-Pacific Journal of Risk and Insurance Volume 7 Issue 2

Abstract

This paper develops a threshold function of the Atlantic Multidecadal Oscillation (AMO) index and allows the effect of AMO index changes over time due to global warming to yield improvements in the forecasts of U.S. hurricane activity. The influence of the threshold effect, the AMO effect, and the global warming effect on Value at Risk and expected shortfall of hurricane risk is also examined. The empirical results of a time-variant threshold Poisson regression model provide an excellent projection for which the forecasting error of average annual U.S. hurricane activity is less than one. We find that the threshold effect and global warming effect dominate the AMO effect. In particular, the global warming effect dominates the AMO effect in extreme hurricane events, and this domination increases over time.

Keywords: hurricane activity; AMO index; global warming; threshold poisson regression model; hurricane risk measurement

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  1. 1

    For example, Bayesian models have been applied in climate studies: Slow (1998), LeRoy (1998), Berliner et al. (2000), Wikle (2000), Katz (2002), Wikle and Anderson (2003), Chu and Zhao (2004), and Elsner and Jagger (2004, 2006). Daneshvaran and Haji (2012a) provide a prediction method for U.S. landfalling hurricanes and identify the differences between long term and warm phase of the atmosphere in terms of U.S. landfall occurrence by using hurricane origination points. Daneshvaran and Haji (2012b) further calculate the hurricane risk from loss point of view in the USA for both long-term and warm phase conditions using a simulation-based stochastic model. Daneshvaran and Haji (2013) use principal components analysis (PCA) to identify possible patterns in historical data based on six climate variables to discuss the relationship between the climate signals and Atlantic hurricane activity. The empirical results show that PCA-based approach provides better estimates compared to ARIMA model and Colorado State University’s forecast.

  2. 2

    This regressions model is based on the Poisson distribution, which is a discrete distribution that is defined based on nonnegative integers. The Poisson regression, a variant of the linear regression, is appropriate for modeling the influence of some set of independent variables (covariates) on the expected rate of a Poisson distributed random process.

  3. 3

    These particular start and stop dates for the averaging points do not appear to be especially important to the following analysis (we also attempted to use several other monthly averages, all of which yielded nearly identical results).

  4. 4

    At least for the Atlantic, the year-to-year variation in hurricane activity is statistically linked (positively correlated) with the AMO index (Saunders and Harris 1997; Kimberlain and Elsner 1998; Goldenberg et al. 2001; Landsea 2005; Elsner et al. 2008).

  5. 5

    For convenience, some studies (e.g. Goldenberg et al. 2001; Landsea 2005; Sutton and Hodson 2005) claim that there are different situations in which the AMO index is positive or negative (i.e. the threshold value is 0). We also consider this assumption, and its corresponding F-statistic of the Chow breakpoint test is 3.1657 with a p-value of 0.0517. Clearly, our objective estimating result is superior to such a convenient assumption.

Published Online: 2013-07-05

© 2013 by Walter de Gruyter Berlin / Boston

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  1. Masthead
  2. Featured Article
  3. Measuring U.S. Hurricane Risk Associated with Natural Climate Cycle and Global Warming Effects
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https://doi.org/10.1515/apjri-2013-0002
Keywords for this article
hurricane activity; AMO index; global warming; threshold poisson regression model; hurricane risk measurement

Articles in the same Issue

  1. Masthead
  2. Featured Article
  3. Measuring U.S. Hurricane Risk Associated with Natural Climate Cycle and Global Warming Effects
  4. Insurance Group Risk Management Model for the Next-Generation Solvency Framework
  5. Analysis of the Residual Structure of the Lee–Carter Model: The Case of Japanese Mortality
  6. The Impact of Mortality Risk on the Asset and Liability Management of Insurance Companies
  7. Pricing Equity Index Annuities with Surrender Options in Four Models
  8. The Asymptotic Ruin Problem in Health Care Insurance with Interest
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