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Risk management with Local Least Squares Monte-Carlo

Author

Listed:
  • Hainaut, Donatien

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Akbaraly, Adnane

    (Detralytics)

Abstract

The method of least squares Monte-Carlo (LSMC) has become a standard in the insurance and financial sectors for computing the exposure of a company to market risk. The sensitive point of this procedure is the non-linear regression of simulated responses on risk factors. This article proposes a novel approach for this step, based on an a-priori segmentation of responses. Using a K-means algorithm, we identify clusters of responses that are next locally regressed on corresponding risk factors. A global function of regression is obtained by combining local models and a logistic regression. The efficiency of the Local Least squares Monte-Carlo (LLSMC) is checked in two illustrations. The first one focuses on butterfly and bull trap options in a Heston stochastic volatility model. The second illustration analyzes the exposure to risks of a participating life insurance.

Suggested Citation

  • Hainaut, Donatien & Akbaraly, Adnane, 2023. "Risk management with Local Least Squares Monte-Carlo," LIDAM Discussion Papers ISBA 2023003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2023003
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Least square Monte-Carlo ; risk management ; option valuation;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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