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Practice-relevant model validation: distributional parameter risk analysis in financial model risk management

Author

Listed:
  • Mark Cummins

    (Dublin City University)

  • Fabian Gogolin

    (Leeds University Business School, University of Leeds)

  • Fearghal Kearney

    (Queen’s University Belfast)

  • Greg Kiely

    (Gazprom Marketing and Trading Limited)

  • Bernard Murphy

    (University of Limerick)

Abstract

An objective of model validation within organisations is to provide guidance on model selection decisions that balance the operational effectiveness and structural complexity of competing models. We consider a practice-relevant model validation scenario where a financial quantitative analysis team seeks to decide between incumbent and alternative models on the basis of parameter risk. We devise a model risk management methodology that gives a meaningful distributional assessment of parameter risk in a setting where market calibration and historical estimation procedures must be jointly applied. Such a scenario is typically driven by data constraints that preclude market calibration only. We demonstrate our proposed methodology in a natural gas storage modelling context, where model usage is necessary to support profit and loss reporting, and to inform trading and hedging strategy. We leverage our distributional parameter risk approach to devise an accessible technique to support model selection decisions.

Suggested Citation

  • Mark Cummins & Fabian Gogolin & Fearghal Kearney & Greg Kiely & Bernard Murphy, 2023. "Practice-relevant model validation: distributional parameter risk analysis in financial model risk management," Annals of Operations Research, Springer, vol. 330(1), pages 431-455, November.
    • Handle: RePEc:spr:annopr:v:330:y:2023:i:1:d:10.1007_s10479-022-04574-x
    DOI: 10.1007/s10479-022-04574-x
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    References listed on IDEAS

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