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Smooth Extremal Models in Finance and Insurance

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  • V. Chavez‐Demoulin
  • P. Embrechts

Abstract

This article describes smooth nonstationary generalized additive modeling for sample extremes, in which spline smoothers are incorporated into models for exceedances over high thresholds. We summarize the smoothing methodology as a new tool for practical extreme value exploration in finance and insurance.

Suggested Citation

  • V. Chavez‐Demoulin & P. Embrechts, 2004. "Smooth Extremal Models in Finance and Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 71(2), pages 183-199, June.
    • Handle: RePEc:bla:jrinsu:v:71:y:2004:i:2:p:183-199
    DOI: 10.1111/j.0022-4367.2004.00085.x
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    Cited by:

    1. Dembińska, Anna & Buraczyńska, Aneta, 2019. "The long-term behavior of number of near-maximum insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 226-237.
    2. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    3. Eryilmaz, Serkan & Gebizlioglu, Omer L. & Tank, Fatih, 2011. "Modeling of claim exceedances over random thresholds for related insurance portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 496-500.
    4. Albrecht, Peter & Schwake, Edmund & Winter, Peter, 2007. "Quantifizierung operationeller Risiken: Der Loss Distribution Approach," German Risk and Insurance Review (GRIR), University of Cologne, Department of Risk Management and Insurance, vol. 3(1), pages 1-45.
    5. Valérie Chavez-Demoulin & Paul Embrechts & Marius Hofert, 2016. "An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 735-776, September.
    6. Daniele Massacci, 2017. "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness," Management Science, INFORMS, vol. 63(9), pages 3072-3089, September.
    7. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    8. Sonia Benito & Carmen López-Martín & Mª Ángeles Navarro, 2023. "Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT)," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-31, March.
    9. Jose PINEDA & Rodolfo MÉNDEZ, 2009. "Fiscal Sustainability and Economic Growth in Bolivia," EcoMod2009 21500075, EcoMod.
    10. Herrera, Rodrigo & González, Sergio & Clements, Adam, 2018. "Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 70-88.
    11. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
    12. Chavez-Demoulin, V. & Embrechts, P. & Sardy, S., 2014. "Extreme-quantile tracking for financial time series," Journal of Econometrics, Elsevier, vol. 181(1), pages 44-52.
    13. Laurini, Fabrizio & Pauli, Francesco, 2009. "Smoothing sample extremes: The mixed model approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3842-3854, September.

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