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