Model operation risk and uncertainty

Devajyoti Ghose and George Soulellis

Contents

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

10.

Measurement of risk and estimation of uncertainty in prediction models

11.

Using models under risk and uncertainty

12.

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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