Model operation risk and uncertainty
Foreword
Preface
Acknowledgements
The evolution of models
The foundations of risk and uncertainty
Uncertainty: a taxonomy
Model risk and uncertainty: a survey of the institutional landscape
Model specification risk and uncertainty
Model operation risk and uncertainty
Data, models and their purpose
Artificial intelligence in finance: a synthesis of human and machine
A deeper dive into machine learning methods: their opportunities, limitations, risks and uncertainties
Measurement of risk and estimation of uncertainty in prediction models
Using models under risk and uncertainty
When models fail
Epilogue: models and the future
Vision without action is a daydream. Action without vision is a nightmare.
Japanese proverb
6.1 INTRODUCTION
The famous Japanese proverb suggests that vision, by itself, is necessary but not sufficient for achieving our objectives. Analogously, a well-specified model is a necessary but not sufficient condition for effective model risk management. It will be seen that model operations,11 We shall use the terms ‘model operation’ and ‘model operations’ interchangeably; as such, we deem them synonymous and to signify activities associated with the model life cycle post model development/specification which span implementation, use, monitoring and feedback as well as decommissioning, play an integral role in ensuring model risk and uncertainty are appropriately managed and mitigated.
Recalling Chapter 4, model operation uncertainty arises when a model’s output and use in decision-making is inconsistent with its intended purpose. Regardless of the appropriateness of a model’s specification, a weak operating environment can compromise a model’s effectiveness, presenting its own set of serious risks and increasing the likelihood of model failure.
Historical experience with models suggests that
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