Deep Learning in Quantitative Finance - Andrew Green

. .

Résumé :

Acknowledgments xix

1 Introduction 3
1.1 What this book is about 3
1.2 The Rise of AI 5
1.3 The Promise of AI in Quantitative Finance 7
1.4 Practicalities 7
1.5 Reading this book 10

2 Feed Forward Neural Networks 13
2.1 Introducing Neural Networks 13
2.2 Regression and Classification 18
2.3 Activation Functions 27
2.4 The Universal Function Approximation Theorem 45
2.5 Conclusions 48

3 Training Neural Networks 49
3.1 Backpropagation and Adjoint Algorithmic Differentiation 50
3.2 Data Preparation and Scaling 53
3.3 Weight Initialization 57
3.4 The Choice of Loss Function 68
3.5 Optimization Algorithms 82
3.6 Common Training Problems 97
3.7 Batch Normalization 104
3.8 Evaluation and Validation 110
3.9 Sobolev Training Using Function Derivatives 124
3.10 Conclusions 131

4 Regularisation 133
4.1 Introduction Regularisation and Generalisation 133
4.2 Weight Decay 134
4.3 Early Stopping 137
4.4 Ensemble Methods and Dropout 138
4.5 Data Augmentation 146
4.6 Other Regularisation Methods 147
4.7 Conclusions Regularisation Strategy 149

5 Hyperparameter Optimization 151
5.1 Introduction 151
5.2 Manual 155
5.3 Grid Search 155
5.4 Random Search 158
5.5 Bayesian Optimization 159
5.6 Bandit-based 165
5.7 Population Based Training (PBT) 181
5.8 Conclusions 184

6 Convolutional Neural Networks 187
6.1 Introduction 187
6.2 Convolutions 188
6.3 Downsampling 203
6.4 Data Augmentation 206
6.5 Transfer Learning Using Pre-trained Networks 211
6.6 Visualising Features 213
6.7 Famous CNNs 223
6.8 Conclusions on CNNs 252

7 Sequence Models 255
7.1 Introducing Sequence Models 255
7.2 Recurrent Neural Networks 257
7.3 Neural Natural Language Processing 276
7.4 Conclusions on Sequence Models 322

8 Autoencoders 323
8.1 Introduction 323
8.2 Autoencoders and Singular-Valued Decomposition 325
8.3 Shallow and Deep Autoencoders 332
8.4 Regularized and Sparse Autoencoders 336
8.5 Denoising Autoencoders 339
8.6 Autoencoders and Generative Models 341
8.7 Conclusion 342

9 Generative Models 343
9.1 Introduction 343
9.2 Evaluating Generative Model Performance 345
9.3 Energy-based Models (EBMs) 348
9.4 Variational Autoencoders (VAEs) 383
9.5 Generative Adversarial Networks (GANs) 396
9.6 Latent Diffusion Models (LDMs) 491
9.7 Conclusions on Generative Models 493

10 Deep Reinforcement Learning 495
10.1 Introduction 495
10.2 Key Concepts in Reinforcement Learning 496
10.3 Markov Decision Processes (MDPs) and the Bellman Equations 506
10.4 Dynamic Programming and Policy Search 509
10.5 Monte Carlo Methods for RL 516
10.6 TD Learning 535
10.7 Deep Q Networks (DQNs) 546
10.8 Policy Gradient 561
10.9 Actor-Critic Methods 567
10.10 Conclusions 568

11 Derivative Valuation using Neural Networks 571
11.1 Introduction 571
11.2 Derivative Valuation using Neural Networks trained as Non-parametric Models 572
11.3 Derivative Valuation Function Approximation 584

12 High Dimensional PDE and BSDE Solvers 603
12.1 Introduction 603
12.2 Deep Galerkin Method (DGM) 604
12.3 Deep BSDE Solvers 619
12.4 Projection and Martingale Solvers 641
12.5 Deep Path Dependent PDEs (DPPDE) 642
12.6 Physics Informed Neural Networks (PINNs) 644
12.7 Deep Backward Dynamic Programming (DBDP) 646
12.8 Deep Splitting (DS) 647
12.9 Conclusions 649

13 Deep Monte Carlo and Optimal Stopping 651
13.1 Introduct...

Biographie:
ANDREW GREEN FIMA MINSTP BA MA MAST DPHIL is a Managing Director, and Lead Rates and XVA Quant at Scotiabank with over twenty-five years of experience in quantitative finance. He has previously held leadership roles in XVA modelling at Lloyds Banking Group and Barclays Capital. He is also the author of XVA: Credit, Funding and Capital Valuation Adjustments (Wiley, 2015). Andrew has worked on interest rate, credit, and equity derivative model development and implementation during his career....

Sommaire:
Contents