Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk

Cover....1 Copyright....6 Table of Contents....7 Preface....11 Conventions Used in This Book....14 Using Code Examples....15 O’Reilly Online Learning....16 How to Contact Us....16 Acknowledgements....17 Part I. Risk Management Foundations....19 Chapter 1. Fundamentals of Risk Management....21 Risk....22 Return....22 Risk Management....25 Main Financial Risks....26 Big Financial Collapse....27 Information Asymmetry in Financial Risk Management....29 Adverse Selection....29 Moral Hazard....32 Conclusion....33 References....33 Chapter 2. Introduction to Time Series Modeling....35 Time Series Components....38 Trend....39 Seasonality....43 Cyclicality....45 Residual....46 Time Series Models....52 White Noise....53 Moving Average Model....55 Autoregressive Model....60 Autoregressive Integrated Moving Average Model....66 Conclusion....72 References....73 Chapter 3. Deep Learning for Time Series Modeling....75 Recurrent Neural Networks....76 Long-Short Term Memory....83 Conclusion....89 References....90 Part II. Machine Learning for Market, Credit, Liquidity, and Operational Risks....91 Chapter 4. Machine Learning-Based Volatility Prediction....93 ARCH Model....96 GARCH Model....102 GJR-GARCH....108 EGARCH....110 Support Vector Regression: GARCH....113 Neural Networks....119 The Bayesian Approach....124 Markov Chain Monte Carlo....126 Metropolis–Hastings....128 Conclusion....133 References....134 Chapter 5. Modeling Market Risk....137 Value at Risk (VaR)....139 Variance-Covariance Method....140 The Historical Simulation Method....146 The Monte Carlo Simulation VaR....147 Denoising....151 Expected Shortfall....159 Liquidity-Augmented Expected Shortfall....161 Effective Cost....163 Conclusion....171 References....172 Chapter 6. Credit Risk Estimation....173 Estimating the Credit Risk....174 Risk Bucketing....176 Probability of Default Estimation with Logistic Regression....188 Probability of Default Estimation with the Bayesian Model....197 Probability of Default Estimation with Support Vector Machines....203 Probability of Default Estimation with Random Forest....205 Probability of Default Estimation with Neural Network....206 Probability of Default Estimation with Deep Learning....207 Conclusion....210 References....210 Chapter 7. Liquidity Modeling....211 Liquidity Measures....213 Volume-Based Liquidity Measures....213 Transaction Cost–Based Liquidity Measures....217 Price Impact–Based Liquidity Measures....221 Market Impact-Based Liquidity Measures....224 Gaussian Mixture Model....228 Gaussian Mixture Copula Model....234 Conclusion....237 References....237 Chapter 8. Modeling Operational Risk....239 Getting Familiar with Fraud Data....242 Supervised Learning Modeling for Fraud Examination....247 Cost-Based Fraud Examination....252 Saving Score....254 Cost-Sensitive Modeling....256 Bayesian Minimum Risk....258 Unsupervised Learning Modeling for Fraud Examination....261 Self-Organizing Map....262 Autoencoders....265 Conclusion....269 References....270 Part III. Modeling Other Financial Risk Sources....271 Chapter 9. A Corporate Governance Risk Measure: Stock Price Crash....273 Stock Price Crash Measures....275 Minimum Covariance Determinant....276 Application of Minimum Covariance Determinant....278 Logistic Panel Application....288 Conclusion....296 References....297 Chapter 10. Synthetic Data Generation and The Hidden Markov Model in Finance....299 Synthetic Data Generation....299 Evaluation of the Synthetic Data....301 Generating Synthetic Data....302 A Brief Introduction to the Hidden Markov Model....310 Fama-French Three-Factor Model Versus HMM....311 Conclusion....322 References....322 Afterword....323 Index....325 About the Author....333 Colophon....333