Foreword

Devajyoti Ghose and George Soulellis

Foreword

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

Foreword

Risk-taking lies at the heart of any investment business. Risk-free investments produce a risk-free rate of return – something that clients can access freely without assistance. The role of the professional investor is therefore to understand and quantify risk, ensuring it correlates with the expected return, and to deliver the resulting return to their clients. In doing so, investors allocate capital to the places that can use it most productively, transforming risk into returns and catalysing economic growth in the process. This is the power of capital markets.

Given this model, it is no wonder that finance has sought, with increasing speed and sophistication, to better quantify risk. It is a continual journey to find new sources of data, process it faster and create models more capable of reflecting it. We do all this with the understanding that these more dynamic models will help us deliver greater and higher-quality returns to our clients. For example, this philosophy is the DNA of Aladdin, BlackRock’s end-to-end investment management and risk management system, which was first sold commercially in 1999 and has been used by leading financial institutions around the

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