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Addressing the financial impact of natural disasters in the era of climate change

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  • Bufalo, Michele
  • Ceci, Claudia
  • Orlando, Giuseppe

Abstract

The objective of our study is to predict the financial losses that may result from natural disasters, along with their level of volatility, over a period of 1 to 15 years. Volatility can lead to significant fluctuations in Profit and Loss (P&L) for companies that are affected by unexpected events. To achieve this goal, we created a novel two-factor square-root model that allows us to establish a correlation between the frequency of occurrences and volatility, using correlated Brownian motions. Moreover, we utilized a Generalized Pareto Distribution (GPD) to estimate the maximum potential loss in terms of Value at Risk (VaR) for each specific type of natural disaster. To ensure the reliability of our predictions, we compared our results to those of four reference models and conducted a backtesting analysis. This approach is particularly suitable for insurance companies seeking to maintain stable reserves, but it can also be adapted for any other type of business that is vulnerable to extreme events and aims to safeguard a consistent cash flow for their stakeholders.

Suggested Citation

  • Bufalo, Michele & Ceci, Claudia & Orlando, Giuseppe, 2024. "Addressing the financial impact of natural disasters in the era of climate change," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:ecofin:v:73:y:2024:i:c:s1062940824000779
    DOI: 10.1016/j.najef.2024.102152
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    1. repec:eme:mfppss:eb018529 is not listed on IDEAS
    2. Emmanuel Bacry & Iacopo Mastromatteo & Jean-Franc{c}ois Muzy, 2015. "Hawkes processes in finance," Papers 1502.04592, arXiv.org, revised May 2015.
    3. Giuseppe Orlando & Rosa Maria Mininni & Michele Bufalo, 2020. "Forecasting interest rates through Vasicek and CIR models: A partitioning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 569-579, July.
    4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    5. Tata Subba Rao & Granville Tunnicliffe Wilson & Michael Eichler & Rainer Dahlhaus & Johannes Dueck, 2017. "Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 225-242, March.
    6. Recchioni, M.C. & Sun, Y., 2016. "An explicitly solvable Heston model with stochastic interest rate," European Journal of Operational Research, Elsevier, vol. 249(1), pages 359-377.
    7. Peter Christoffersen & Steven Heston & Kris Jacobs, 2009. "The Shape and Term Structure of the Index Option Smirk: Why Multifactor Stochastic Volatility Models Work So Well," Management Science, INFORMS, vol. 55(12), pages 1914-1932, December.
    8. Julio Guerrero & Maria del Carmen Galiano & Giuseppe Orlando, 2023. "Modeling COVID-19 pandemic with financial markets models: The case of Ja\'en (Spain)," Papers 2301.08803, arXiv.org.
    9. Wulan Anggraeni & Sudradjat Supian & Sukono & Nurfadhlina Binti Abdul Halim, 2022. "Earthquake Catastrophe Bond Pricing Using Extreme Value Theory: A Mini-Review Approach," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    10. Rehez Ahlip & Laurence A. F. Park & Ante Prodan, 2017. "Pricing currency options in the Heston/CIR double exponential jump-diffusion model," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-30, March.
    11. Patricia Born & W. Viscusi, 2006. "The catastrophic effects of natural disasters on insurance markets," Journal of Risk and Uncertainty, Springer, vol. 33(1), pages 55-72, September.
    12. Christian-Oliver Ewald & Aihua Zhang & Zhe Zong, 2019. "On the calibration of the Schwartz two-factor model to WTI crude oil options and the extended Kalman Filter," Annals of Operations Research, Springer, vol. 282(1), pages 119-130, November.
    13. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo & Fernandez-Anaya, Guillermo, 2008. "Time-varying Hurst exponent for US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6159-6169.
    14. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    15. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, January.
    16. Ju-Liang Jin & Jian Cheng & Yi-Ming Wei, 2008. "Forecasting flood disasters using an accelerated genetic algorithm: Examples of two case studies for China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 44(1), pages 85-92, January.
    17. Robert Faff & Sirimon Treepongkaruna, 2013. "A re-examination of the empirical performance of the Longstaff and Schwartz two-factor term structure model using real yield data," Australian Journal of Management, Australian School of Business, vol. 38(2), pages 333-352, August.
    18. Umar Farooq & Adeel Nasir & Bilal & Muhammad Umer Quddoos, 2021. "The impact of COVID-19 pandemic on abnormal returns of insurance firms: a cross-country evidence," Applied Economics, Taylor & Francis Journals, vol. 53(31), pages 3658-3678, July.
    19. Laudagé, Christian & Desmettre, Sascha & Wenzel, Jörg, 2019. "Severity modeling of extreme insurance claims for tariffication," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 77-92.
    20. Benali, Nadia & Feki, Rochdi, 2017. "The impact of natural disasters on insurers’ profitability: Evidence from Property/Casualty Insurance company in United States," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1394-1400.
    21. Rypdal, Martin & Løvsletten, Ola, 2013. "Modeling electricity spot prices using mean-reverting multifractal processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 194-207.
    22. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    23. Hendrik Kohrs & Hermann Mühlichen & Benjamin R. Auer & Frank Schuhmacher, 2019. "Pricing and risk of swing contracts in natural gas markets," Review of Derivatives Research, Springer, vol. 22(1), pages 77-167, April.
    24. Marc Gürtler & Martin Hibbeln & Christine Winkelvos, 2016. "The Impact of the Financial Crisis and Natural Catastrophes on CAT Bonds," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 579-612, September.
    25. Apostolos Kiohos & Maria Paspati, 2021. "Alternative to Insurance Risk Transfer: Creating a catastrophe bond for Romanian earthquakes," Bulletin of Applied Economics, Risk Market Journals, vol. 8(1), pages 1-17.
    26. Alan G. Hawkes, 2018. "Hawkes processes and their applications to finance: a review," Quantitative Finance, Taylor & Francis Journals, vol. 18(2), pages 193-198, February.
    27. Cox, Samuel H. & Lin, Yijia & Pedersen, Hal, 2010. "Mortality risk modeling: Applications to insurance securitization," Insurance: Mathematics and Economics, Elsevier, vol. 46(1), pages 242-253, February.
    28. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
    29. Jiangpeng Chen & Xun Lei & Li Zhang & Bin Peng, 2015. "Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang, China," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-10, February.
    30. Pius Babuna & Xiaohua Yang & Amatus Gyilbag & Doris Abra Awudi & David Ngmenbelle & Dehui Bian, 2020. "The Impact of COVID-19 on the Insurance Industry," IJERPH, MDPI, vol. 17(16), pages 1-14, August.
    31. Longstaff, Francis A & Schwartz, Eduardo S, 1992. "Interest Rate Volatility and the Term Structure: A Two-Factor General Equilibrium Model," Journal of Finance, American Finance Association, vol. 47(4), pages 1259-1282, September.
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    More about this item

    Keywords

    Forecasting; Natural disasters; Two-factor square-root model; Generalized Pareto distribution; Model validation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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