IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-03791154.html
   My bibliography  Save this paper

Subsidence and household insurances in France : geolocated data and insurability

Author

Listed:
  • Pierre Chatelain

    (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Stéphane Loisel

    (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

Abstract

The insurability of natural disasters has always been an issue faced by the insurers, states, and insured persons. In France, the insurer and the legislator are concerned about the subsidence risks due to several consecutive dry years. More and more open data are provided in France, which allows insurers by geolocating their portfolio to have better knowledge. This knowledge plus the increase in subsidence risks query the insurability of the subsidence risk. Using mostly GLMs, the most common models used in France, this paper shows the improvement of the knowledge subsidence risks. The results bring to the fore the importance of legislative control and the recently enforced new CatNat program, leading authors to question the CatNat fee stagnation.

Suggested Citation

  • Pierre Chatelain & Stéphane Loisel, 2021. "Subsidence and household insurances in France : geolocated data and insurability," Working Papers hal-03791154, HAL.
    • Handle: RePEc:hal:wpaper:hal-03791154
    Note: View the original document on HAL open archive server: https://hal.science/hal-03791154
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03791154/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Mack, Thomas, 1991. "A Simple Parametric Model for Rating Automobile Insurance or Estimating IBNR Claims Reserves," ASTIN Bulletin, Cambridge University Press, vol. 21(1), pages 93-109, April.
    3. Renshaw, A.E. & Verrall, R.J., 1998. "A Stochastic Model Underlying the Chain-Ladder Technique," British Actuarial Journal, Cambridge University Press, vol. 4(4), pages 903-923, October.
    4. Arthur Charpentier & Molly James & Hani Ali, 2021. "Predicting Drought and Subsidence Risks in France," Papers 2107.07668, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Benjamin Avanzi & Yanfeng Li & Bernard Wong & Alan Xian, 2022. "Ensemble distributional forecasting for insurance loss reserving," Papers 2206.08541, arXiv.org, revised Jun 2024.
    2. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
    3. Liivika Tee & Meelis Käärik & Rauno Viin, 2017. "On Comparison of Stochastic Reserving Methods with Bootstrapping," Risks, MDPI, vol. 5(1), pages 1-21, January.
    4. Wahl, Felix & Lindholm, Mathias & Verrall, Richard, 2019. "The collective reserving model," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 34-50.
    5. Alicja Wolny-Dominiak, 2016. "The hierarchical generalized linear model and the bootstrap estimator of the error of prediction of loss reserves in a non-life insurance company," Papers 1612.04126, arXiv.org.
    6. Kunkler, Michael, 2006. "Modelling negatives in stochastic reserving models," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 540-555, June.
    7. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2022. "Mack-Net model: Blending Mack's model with Recurrent Neural Networks," Papers 2205.07334, arXiv.org.
    8. England, P.D. & Verrall, R.J. & Wüthrich, M.V., 2019. "On the lifetime and one-year views of reserve risk, with application to IFRS 17 and Solvency II risk margins," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 74-88.
    9. Yixing Zhao & Rogemar Mamon & Heng Xiong, 2021. "Claim reserving for insurance contracts in line with the International Financial Reporting Standards 17: a new paid-incurred chain approach to risk adjustments," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    10. Valandis Elpidorou & Carolin Margraf & María Dolores Martínez-Miranda & Bent Nielsen, 2019. "A Likelihood Approach to Bornhuetter–Ferguson Analysis," Risks, MDPI, vol. 7(4), pages 1-20, December.
    11. Qi Guo & Bruno Remillard & Anatoliy Swishchuk, 2020. "Multivariate General Compound Point Processes in Limit Order Books," Risks, MDPI, vol. 8(3), pages 1-20, September.
    12. England, Peter, 2002. "Addendum to "Analytic and bootstrap estimates of prediction errors in claims reserving"," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 461-466, December.
    13. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    14. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    15. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    16. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    17. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," Working Papers halshs-03626503, HAL.
    18. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    19. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    20. Gian Paolo Clemente & Nino Savelli & Diego Zappa, 2019. "Modelling Outstanding Claims with Mixed Compound Processes in Insurance," International Business Research, Canadian Center of Science and Education, vol. 12(3), pages 123-138, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-03791154. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.