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Machine Learning for Financial Risk Management with Python

Algorithms for Modeling Risk

Paperback Engels 2021 9781492085256
Verkooppositie 2129Hoogste positie: 2129
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Samenvatting

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models.

Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python.

With this book, you will:
- Review classical time series applications and compare them with deep learning models
- Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning
- Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension
- Develop a credit risk analysis using clustering and Bayesian approaches
- Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model
- Use machine learning models for fraud detection
- Predict stock price crash and identify its determinants using machine learning models

Specificaties

ISBN13:9781492085256
Taal:Engels
Bindwijze:paperback
Aantal pagina's:350
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:22-12-2021
ISSN:

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Inhoudsopgave

Preface
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgements

Part I. Risk Management Foundations
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

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

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
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

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

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

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

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
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

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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        Machine Learning for Financial Risk Management with Python