IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1810.10970.html
   My bibliography  Save this paper

Defining and estimating stochastic rate change in a dynamic general insurance portfolio

Author

Listed:
  • Roland R. Ramsahai

Abstract

Rate change calculations in the literature involve deterministic methods that measure the change in premium for a given policy. The definition of rate change as a statistical parameter is proposed to address the stochastic nature of the premium charged for a policy. It promotes the idea that rate change is a property of an asymptotic population to be estimated, not just a property to measure or monitor in the sample of observed policies that are written. Various models and techniques are given for estimating this stochastic rate change and quantifying the uncertainty in the estimates. The use of matched sampling is emphasized for rate change estimation, as it adjusts for changes in policy characteristics by directly searching for similar policies across policy years. This avoids any of the assumptions and recipes that are required to re-rate policies in years where they were not written, as is common with deterministic methods. Such procedures can be subjective or implausible if the structure of rating algorithms change or there are complex and heterogeneous exposure bases and coverages. The methods discussed are applied to a motor premium database. The application includes the use of a genetic algorithm with parallel computations to automatically optimize the matched sampling.

Suggested Citation

  • Roland R. Ramsahai, 2018. "Defining and estimating stochastic rate change in a dynamic general insurance portfolio," Papers 1810.10970, arXiv.org.
  • Handle: RePEc:arx:papers:1810.10970
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1810.10970
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. England, P.D. & Verrall, R.J., 2002. "Stochastic Claims Reserving in General Insurance," British Actuarial Journal, Cambridge University Press, vol. 8(3), pages 443-518, August.
    2. Alberto Abadie & Guido W. Imbens, 2016. "Matching on the Estimated Propensity Score," Econometrica, Econometric Society, vol. 84, pages 781-807, March.
    3. Ben B. Hansen, 2008. "The prognostic analogue of the propensity score," Biometrika, Biometrika Trust, vol. 95(2), pages 481-488.
    4. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    5. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    6. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    7. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    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. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    2. Fredrik Savje, 2019. "On the inconsistency of matching without replacement," Papers 1907.07288, arXiv.org, revised Jun 2021.
    3. Adam C. Sales & Ben B. Hansen & Brian Rowan, 2018. "Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates," Journal of Educational and Behavioral Statistics, , vol. 43(1), pages 3-31, February.
    4. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    5. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    6. Hünermund, Paul & Czarnitzki, Dirk, 2019. "Estimating the causal effect of R&D subsidies in a pan-European program," Research Policy, Elsevier, vol. 48(1), pages 115-124.
    7. Jasjeet Singh Sekhon & Richard D. Grieve, 2012. "A matching method for improving covariate balance in cost‐effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 21(6), pages 695-714, June.
    8. Paul Hünermund & Dirk Czarnitzki, 2016. "Estimating the local average treatment effect of R&D subsidies in a pan-European program," Working Papers of Department of Management, Strategy and Innovation, Leuven 541177, KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven.
    9. Blackman, Allen, 2015. "Strict versus mixed-use protected areas: Guatemala's Maya Biosphere Reserve," Ecological Economics, Elsevier, vol. 112(C), pages 14-24.
    10. Robert J. Johnston & Klaus Moeltner, 2019. "Special Flood Hazard Effects on Coastal and Interior Home Values: One Size Does Not Fit All," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(1), pages 181-210, September.
    11. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Papers 2206.08503, arXiv.org, revised Apr 2024.
    12. Jan-Hinrik Meyer-Sahling & Will Lowe & Christian van Stolk, 2016. "Silent professionalization: EU integration and the professional socialization of public officials in Central and Eastern Europe," European Union Politics, , vol. 17(1), pages 162-183, March.
    13. Wendimu, Mengistu Assefa & Henningsen, Arne & Gibbon, Peter, 2016. "Sugarcane Outgrowers in Ethiopia: “Forced” to Remain Poor?," World Development, Elsevier, vol. 83(C), pages 84-97.
    14. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    15. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
    16. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    17. Zeqin Liu & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201904, University of Kansas, Department of Economics, revised Mar 2019.
    18. Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    19. Gary King & Christopher Lucas & Richard A. Nielsen, 2017. "The Balance‐Sample Size Frontier in Matching Methods for Causal Inference," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 473-489, April.
    20. Seonho Shin, 2022. "Evaluating the Effect of the Matching Grant Program for Refugees: An Observational Study Using Matching, Weighting, and the Mantel-Haenszel Test," Journal of Labor Research, Springer, vol. 43(1), pages 103-133, March.

    More about this item

    Statistics

    Access and download statistics

    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:arx:papers:1810.10970. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.