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Generative Diffusion Models for Financial Engineering
Synthetic Data Generation, Tail Risk Management, and Stress Testing with Python
Inglese
99 b
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Reactive Publishing
Discover how generative diffusion models are transforming financial engineering. This technical guide explores the application of advanced diffusion-based techniques to create high-quality synthetic financial scenarios, strengthen tail-risk analysis, and improve stress testing and backtesting workflows.
Written for quantitative analysts, risk managers, and Python practitioners, the book provides practical implementations for generating realistic synthetic data that captures complex market dynamics, volatility clustering, and extreme events. Learn to build more robust models that better withstand market shocks and enhance decision-making under uncertainty.
Clear explanations, code examples, and real-world considerations make this a valuable resource for professionals applying cutting-edge generative AI methods to quantitative finance challenges.
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