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

Mark Cummins (), Fabian Gogolin (), Fearghal Kearney (), Greg Kiely () and Bernard Murphy ()
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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

Annals of Operations Research, 2023, vol. 330, issue 1, No 16, 455 pages

Abstract: 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.

Keywords: Risk management; Model validation; Parameter risk; Distributional analysis; Natural gas storage modelling (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04574-x

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