Epilogue: models and the future
Epilogue: models and the future
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
In the realm where data’s vast and models reign,
We tread the path where learning’s ardour gleams
Yet dangers lurk amidst the striving strain
As misspecification clouds truth’s streams.
Overfitting whispers sweet deceit,
Its siren song lures models far astray,
While bias and variance deftly compete,
To lead them from the true and rightful way.
In tangled web of features and of noise,
The delicate dance of learning takes its course,
But errors lurk where certainty decoys,
And shadows obscure the path to the source.
So let us wield our algorithms keen,
With caution’s hand in learning’s grand machine.
Structural modelling, ChatGPT, May 5, 2024
In realms where numbers paint the world’s design,
Where econometrics seeks to unveil,
The models crafted with meticulous line,
Can falter when assumptions start to fail.
Within data’s labyrinthine maze,
Lies misspecification’s subtle snare,
Where hidden biases subtly graze,
And truths obscured demand a careful stare.
Oh, econometrics, thy noble quest,
To unveil patterns, in the economic flow,
Yet in the midst of statistical zest,
Model errors can lead us far astray, you know.
So let us tread with caution ever wise,
In modelling worlds where truth’s disguise lies.
Algorithms
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