Abstract
Background: With the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. The credit crisis of 2008–2009 provoked added concern about the use of models in finance. Measuring and managing model risk has subsequently come under scrutiny from regulators, supervisors, banks and other financial institutions. Regulatory guidance indicates that meticulous monitoring of all phases of model development and implementation is required to mitigate this risk. Considerable resources must be mobilised for this purpose. The exercise must embrace model development, assembly, implementation, validation and effective governance.Setting: Model validation practices are generally patchy, disparate and sometimes contradictory, and although the Basel Accord and some regulatory authorities have attempted to establish guiding principles, no definite set of global standards exists.Aim: Assessing the available literature for the best validation practices.Methods: This comprehensive literature study provided a background to the complexities of effective model management and focussed on model validation as a component of model risk management.Results: We propose a coherent ‘best practice’ framework for model validation. Scorecard tools are also presented to evaluate if the proposed best practice model validation framework has been adequately assembled and implemented.Conclusion: The proposed best practice model validation framework is designed to assist firms in the construction of an effective, robust and fully compliant model validation programme and comprises three principal elements: model validation governance, policy and process.
Highlights
Model validation is concerned with mitigating model risk and, as such, is a component of model risk management
Since the objective of this article is to provide a framework for model validation, it is important to distinguish between model risk management and model validation
According to McGuire (2007), model risk is ‘defined from a SOX (USA’s Sarbanes-Oxley Act) Section 404 perspective as the exposure arising from management and the board of directors reporting incorrect information derived from inaccurate model outputs’
Summary
With the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. The credit crisis of 2008–2009 provoked added concern about the use of models in finance. Measuring and managing model risk has subsequently come under scrutiny from regulators, supervisors, banks and other financial institutions. Regulatory guidance indicates that meticulous monitoring of all phases of model development and implementation is required to mitigate this risk. Considerable resources must be mobilised for this purpose. The exercise must embrace model development, assembly, implementation, validation and effective governance. Setting: Model validation practices are generally patchy, disparate and sometimes contradictory, and the Basel Accord and some regulatory authorities have attempted to establish guiding principles, no definite set of global standards exists. Aim: Assessing the available literature for the best validation practices
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