Model Risk and Uncertainty in the Financial World
First published:
ISBN: 978-1-78272-420-9
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Acknowledgements
Acknowledgements
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
We would like to express our thanks and gratitude to Lewis O’Sullivan, Sarah Hastings, Victoria Nightingale and everyone at Risk Books who contributed to this effort, as well as Emma Dain and Sam Clark at T&T Productions Ltd for copy-editing and typesetting: their diligence and patience was immeasurable. We are indebted to Mihail Razhko for his assistance with the proof – thank you Misha. We are grateful for the support and encouragement of family, friends and colleagues, who have meant so much to us along the way.
Devajyoti Ghose
In the course of writing this book, I have subjected my friends and family to elaborate discussions on topics ranging from conditional probability to Ellsberg urns and marbles during Thanksgiving dinners over the years (a memorable insight from the Thanksgiving table: instead of marbles use M&Ms to engage younger audiences in Ellsberg experiments!). I greatly appreciate their indulgence and patience. I owe particular thanks to my family – Margaret Ghose, Nishant Ghose, Tara Rangat, Kira Mauro, Ravi Ghose, Vir Ghose and Soraya Mauro – many of them for participating in discussions on probably puzzles and much else.
Over the years, I have also benefited from
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