The evolution of models
The evolution of 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
The laws of nature are but the mathematical thoughts of God.
Attributed to Euclid
1.1 INTRODUCTION
In the heady days leading up to the 2007–9 global financial crisis (GFC) a young analyst addressed institutional clients at a fixed-income conference at Laguna Beach in Southern California. The weather, as always, was fine, good wine had been served at the cocktail party the previous evening and the outlook of the audience – sophisticated institutional clients – was a mixture of confidence and, perhaps, some apprehension. The Lehman Brothers analyst warned “don’t lend a million bucks to someone who can’t put down a buck”. He was referring to the subprime equity issue, where a trillion US dollars of subprime US home mortgages had been originated, securitised in various forms and sold to investors across the world. It was a looming risk but not yet a crisis. In May 2007, Chairman Bernanke of the Federal Reserve had said, “We believe the effect of the troubles in the subprime sector on the broader housing market will be limited and we do not expect significant spillovers from the subprime market to the rest of the economy or to the financial system” (CNBC 2007).
The Lehman Brothers analyst
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