ISBN: 9798198945449 - python for rough volatility: rough bergomi and stochastic volatility models (6 risultati)

Taschenbuch. Condizione: Neu. Neuware - Reactive PublishingPython for Rough Volatility introduces readers to the practical implementation of rough volatility models in Python, with a focused treatment of the Rough Bergomi model and related stochastic volatility frameworks used in quantitative finance.This book bridges the gap between advanced mathematical theory and working code. It demonstrates how to build, simulate, calibrate, and apply rough volatility models using Python, with emphasis on numerical methods and computational efficiency suitable for real-world quantitative workflows.What the book covers: - Core concepts of rough volatility and the Rough Bergomi model- Implementation of stochastic volatility models in Python- High-performance calibration techniques- Simulation methods for rough paths- Practical considerations for model application in quantitative financeWritten for quantitative analysts, developers, and researchers working in options pricing, volatility modeling, and financial engineering, the book assumes familiarity with Python programming and basic stochastic calculus. All examples are provided with complete, runnable code that can be adapted for personal or professional use.The material is presented in a clear, technical style focused on implementation details rather than theoretical proofs, making it a practical resource for those looking to incorporate rough volatility models into their quantitative toolkit.…

Paperback. Condizione: new. Paperback. Reactive PublishingPython for Rough Volatility introduces readers to the practical implementation of rough volatility models in Python, with a focused treatment of the Rough Bergomi model and related stochastic volatility frameworks used in quantitative finance.This book bridges the gap between advanced mathematical theory and working code. It demonstrates how to build, simulate, calibrate, and apply rough volatility models using Python, with emphasis on numerical methods and computational efficiency suitable for real-world quantitative workflows.What the book covers: Core concepts of rough volatility and the Rough Bergomi modelImplementation of stochastic volatility models in PythonHigh-performance calibration techniquesSimulation methods for rough pathsPractical considerations for model application in quantitative financeWritten for quantitative analysts, developers, and researchers working in options pricing, volatility modeling, and financial engineering, the book assumes familiarity with Python programming and basic stochastic calculus. All examples are provided with complete, runnable code that can be adapted for personal or professional use.The material is presented in a clear, technical style focused on implementation details rather than theoretical proofs, making it a practical resource for those looking to incorporate rough volatility models into their quantitative toolkit. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. Reactive PublishingPython for Rough Volatility introduces readers to the practical implementation of rough volatility models in Python, with a focused treatment of the Rough Bergomi model and related stochastic volatility frameworks used in quantitative finance.This book bridges the gap between advanced mathematical theory and working code. It demonstrates how to build, simulate, calibrate, and apply rough volatility models using Python, with emphasis on numerical methods and computational efficiency suitable for real-world quantitative workflows.What the book covers: Core concepts of rough volatility and the Rough Bergomi modelImplementation of stochastic volatility models in PythonHigh-performance calibration techniquesSimulation methods for rough pathsPractical considerations for model application in quantitative financeWritten for quantitative analysts, developers, and researchers working in options pricing, volatility modeling, and financial engineering, the book assumes familiarity with Python programming and basic stochastic calculus. All examples are provided with complete, runnable code that can be adapted for personal or professional use.The material is presented in a clear, technical style focused on implementation details rather than theoretical proofs, making it a practical resource for those looking to incorporate rough volatility models into their quantitative toolkit. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…