Model specification 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

Model uncertainty is fear of model misspecification.

Lars Hansen and Thomas Sargent

5.1 INTRODUCTION

In this chapter we address risk and uncertainty arising from model misspecification: what it is; what its consequences are; how it generates risk and uncertainty in the model life cycle; and how it may be mitigated.

In the quote above, Hansen and Sargent were concerned with the behaviour of economic agents who form expectations based on the best model that is available to them at the time: the “rational” part of rational expectations. Whether or not we believe in the strict version of rational expectations espoused in the 1980s (and plenty of reasons to be sceptical have emerged since), the uncertainty associated with models must lie at the centre of decisions made by financial institutions as economic agents.¹¹ In an extreme form of the theory, rational economic agents are able to forecast the effects of government policy (Hansen and Sargent were thinking of fiscal and monetary policy) with the same model as the Federal Reserve or the Bank of England (if that is the best available model) and incorporate these forecasts, such as a forecast of future inflation as a consequence of

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