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

Cover
Copyright
Table of Contents
Preface
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgements
Part I. Risk Management Foundations
	Chapter 1. Fundamentals of Risk Management
		Risk
		Return
		Risk Management
			Main Financial Risks
			Big Financial Collapse
		Information Asymmetry in Financial Risk Management
			Adverse Selection
			Moral Hazard
		Conclusion
		References
	Chapter 2. Introduction to Time Series Modeling
		Time Series Components
			Trend
			Seasonality
			Cyclicality
			Residual
		Time Series Models
		White Noise
			Moving Average Model
			Autoregressive Model
			Autoregressive Integrated Moving Average Model
		Conclusion
		References
	Chapter 3. Deep Learning for Time Series Modeling
		Recurrent Neural Networks
		Long-Short Term Memory
		Conclusion
		References
Part II. Machine Learning for Market, Credit, Liquidity, and Operational Risks
	Chapter 4. Machine Learning-Based Volatility Prediction
		ARCH Model
		GARCH Model
		GJR-GARCH
		EGARCH
		Support Vector Regression: GARCH
		Neural Networks
		The Bayesian Approach
			Markov Chain Monte Carlo
			Metropolis–Hastings
		Conclusion
		References
	Chapter 5. Modeling Market Risk
		Value at Risk (VaR)
			Variance-Covariance Method
			The Historical Simulation Method
			The Monte Carlo Simulation VaR
		Denoising
		Expected Shortfall
		Liquidity-Augmented Expected Shortfall
		Effective Cost
		Conclusion
		References
	Chapter 6. Credit Risk Estimation
		Estimating the Credit Risk
		Risk Bucketing
		Probability of Default Estimation with Logistic Regression
			Probability of Default Estimation with the Bayesian Model
			Probability of Default Estimation with Support Vector Machines
			Probability of Default Estimation with Random Forest
			Probability of Default Estimation with Neural Network
			Probability of Default Estimation with Deep Learning
		Conclusion
		References
	Chapter 7. Liquidity Modeling
		Liquidity Measures
			Volume-Based Liquidity Measures
			Transaction Cost–Based Liquidity Measures
			Price Impact–Based Liquidity Measures
			Market Impact-Based Liquidity Measures
		Gaussian Mixture Model
		Gaussian Mixture Copula Model
		Conclusion
		References
	Chapter 8. Modeling Operational Risk
		Getting Familiar with Fraud Data
		Supervised Learning Modeling for Fraud Examination
			Cost-Based Fraud Examination
			Saving Score
			Cost-Sensitive Modeling
			Bayesian Minimum Risk
		Unsupervised Learning Modeling for Fraud Examination
			Self-Organizing Map
			Autoencoders
		Conclusion
		References
Part III. Modeling Other Financial Risk Sources
	Chapter 9. A Corporate Governance Risk Measure: Stock Price Crash
		Stock Price Crash Measures
		Minimum Covariance Determinant
		Application of Minimum Covariance Determinant
		Logistic Panel Application
		Conclusion
		References
	Chapter 10. Synthetic Data Generation and The Hidden Markov Model in Finance
		Synthetic Data Generation
		Evaluation of the Synthetic Data
		Generating Synthetic Data
		A Brief Introduction to the Hidden Markov Model
		Fama-French Three-Factor Model Versus HMM
		Conclusion
		References
Afterword
Index
About the Author
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