Model Risk and Uncertainty in the Financial World
First published:
ISBN: 978-1-78272-420-9
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Measurement of risk and estimation of uncertainty in prediction models
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
10.1 THE GROUNDWORK: FOUNDATIONAL ELEMENTS
We may at once admit that any inference from the particular to the general must be attended with some degree of uncertainty, but this is not the same as to admit that such inference cannot be absolutely rigorous, for the nature and degree of the uncertainty may itself be capable of rigorous expression.
Fisher (1966, p. 4)
10.1.1 Introduction
In the preceding chapters we discussed ways in which model risk and uncertainty could manifest or materialise during the model life cycle, whether within development, validation, implementation or production. Let us now turn to the question of how to measure this risk and estimate the uncertainty.
We often incorporate the concepts of risk and uncertainty in daily conversation. We might say “I expect it will cost me USD1,000 plus or minus USD100”. The phrase “plus or minus USD100” may seem unremarkable, yet it reflects two critical elements: an expectation (the USD1,000) and an expression or range of risk or uncertainty (the ±USD100) around this expectation. Combinations of expectations, risks and uncertainties form the basis for financial decision-making, where an understanding of not only what is likely to
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