Calculating Premiums for Extreme-Tail Risks with Liability Claims and Nuclear Verdicts

Authors

  • Ahmet Bidav Probability in Insurance

DOI:

https://doi.org/10.47672/ajsas.2283

Keywords:

Extreme-Tail Risks, Liability Claims, Nuclear Verdicts, Fat-Tailed Distributions, Tail Index Uncertainty, Premium Principle, Generalized Pareto Distributions , Risk Management Strategies

Abstract

Purpose: When insurance claims, particularly liability claims and nuclear verdicts, are governed by fat-tailed distributions, considerable uncertainty exists about the value of the tail index.

Material and Methods: Using the theory of risk aversion, this paper establishes a new premium principle (the power principle – analogous to the exponential principle for thin-tailed claims) and investigates its properties.

Findings: Applied to claims arising from generalized Pareto distributions, the resultant premium is shown to be the ratio of the two largest expected claims. This structure provides a natural model for incorporating tail-index uncertainty into premiums. The theory is illustrated through possible ‘premiums’ for liability claims and nuclear verdicts, utilizing the consistent pattern of observed extremes.

Implications to Theory, Practice and Policy: By integrating statistical methods for tail index estimation and addressing the inherent uncertainty, the power principle offers a robust framework for determining premiums in high-risk environments. The paper concludes with practical implications for insurers, highlighting the need for advanced risk management strategies and regulatory considerations in dealing with extreme liability claims.

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References

Arrow, K.J. (1971). Essays in the Theory of Risk Bearing. Markham Publishing Company.

Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer-Verlag.

Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Vol. 2. Wiley.

Gay, R. (2004a). Pricing risk when the distributions are fat-tailed. Journal of Applied Probability, 41A, 157-175.

Hill, B.M. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics, 3, 1163-1174.

Pickands, J. (1975). Statistical inference using extreme order statistics. Annals of Statistics, 3, 119-131.

Rolski, T., Schmidli, H., Schmidt, V., & Teugels, J. (1999). Stochastic Processes for Insurance and Finance. Wiley.

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Published

2024-08-14

How to Cite

Bidav, A. (2024). Calculating Premiums for Extreme-Tail Risks with Liability Claims and Nuclear Verdicts. American Journal of Statistics and Actuarial Sciences, 5(2), 13–25. https://doi.org/10.47672/ajsas.2283

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