Rough volatility (20 Ergebnisse)

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Taschenbuch. Zustand: 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.…

Taschenbuch. Zustand: Neu. Neuware - Reactive PublishingThe volatility surface is broken. Rough volatility explains why.This book delivers the most accessible, practical, and comprehensive guide to rough volatility, fractional Brownian motion (fBM), and Hurst-driven modeling ever written for quantitative traders and financial engineers.If you have struggled to understand why standard stochastic volatility models fail, or why modern markets behave with 'memory' and microstructure-induced roughness, this book gives you the complete framework, intuition, and Python workflows to build the next generation of volatility models and trading systems.What This Book Teaches You- The core intuition behind rough volatilityWhy volatility is not smooth, why it cannot be modeled with classical Brownian motion, and how fractional processes capture long-memory behavior in real markets.- Fractional Brownian motion (fBM) from zero to masteryStep-by-step construction, parameterization, simulation, and calibration. Learn Hurst exponents the right way - intuitively first, then rigorously.- Full rough volatility model implementations in PythonIncluding: - The Rough Bergomi Model- Fractional Stochastic Volatility (fSV)- Multi-factor fractional models- Hybrid neural/fractional architecturesAll presented with clean, reusable code templates.- How rough volatility transforms tradingUse fractional features to detect volatility clustering, regime shifts, option mispricings, and structural breaks that traditional models miss.- Complete volatility trading systemsConcrete, plug-and-play strategies built from fractional features: - Hurst-driven volatility filters- fBM momentum/anti-momentum signals- Rough volatility mean-reversion engines- Volatility-of-volatility predictorsIncludes full Python implementations.Why Rough Volatility MattersClassical models assume volatility is smooth, Markovian, and memoryless. Modern markets are the opposite. They are rough, self-similar, and exhibit long-range dependence - and this book shows how to model that structure directly.Rough volatility is not an academic curiosity. It is the new foundation of volatility modeling at banks, hedge funds, and derivatives desks worldwide.Who This Book Is ForQuant traders, systematic volatility researchers, derivatives professionals, financial engineers, and advanced Python quants who want to push beyond Black-Scholes-era assumptions and into the future of stochastic modeling.If you want to understand how volatility really behaves, and build trading systems that exploit it, this is the most practical, complete, and modern guide available.…

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Taschenbuch. Zustand: Neu. Neuware - Reactive PublishingIn today's high-frequency, data-rich markets, traditional volatility models fall short. Enter the powerful fusion of rough volatility frameworks and modern deep learning, specifically Transformers, that is redefining how quantitative traders, researchers, and risk managers capture market dynamics.This cutting-edge guide bridges stochastic processes, fractional Brownian motion, and state-of-the-art neural architectures to deliver practical, high-performance solutions for: - Rough volatility modeling (including the Rough Bergomi model and its extensions)- Neural calibration of volatility surfaces with unprecedented speed and accuracy- Transformer-based forecasting of implied volatility dynamics and term-structure behavior- Advanced options arbitrage strategies that exploit mispricings across strikes, maturities, and underlyings- Real-time surface modeling and hedging in equity, FX, and commodity marketsYou'll discover how to implement end-to-end pipelines in Python, from simulating rough paths and training attention-based models to deploying arbitrage engines that adapt to regime shifts. Complete with mathematical foundations, code repositories, performance benchmarks, and production-ready techniques, this book equips you to move beyond Black-Scholes limitations and extract genuine alpha from volatility surfaces.Perfect for: - Quantitative researchers and PhD-level practitioners- Options traders and volatility arbitrage desks- Machine learning engineers working in finance- Risk managers and portfolio strategists seeking next-generation toolsWhether you're refining Heston-style models, exploring fractional volatility, or building Transformer-driven trading systems, Neural Rough Volatility provides the rigorous theory and battle-tested code you need to stay ahead in an increasingly competitive market. …

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Paperback. Zustand: New. Volatility underpins financial markets by encapsulating uncertainty about prices, individual behaviors, and decisions and has traditionally been modeled as a semimartingale, with consequent scaling properties. This mathematical description has been an active topic of research for decades, however, driven by empirical estimates of the scaling behavior of volatility, a new paradigm has emerged, whereby paths of volatility are rougher than those of semimartingales. According to this perspective, volatility is path-dependent and exhibits jump-like short-term behavior.The first book to offer a comprehensive exploration of the subject, Rough Volatility contributes to the understanding and application of rough volatility models by equipping readers with the tools and insights needed to delve into the topic, exploring the motivation for rough volatility modeling and providing a toolbox for computation and practical implementation, and organizing the material to reflect the subject's development and progression.…

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Gebundene Ausgabe. Zustand: Neu. Most books on price dynamics assume you already know the mathematics. This one teaches it.This book begins with a coin flip and builds toward a calibrated volatility surface. Then it takes the framework beyond the Black-Scholes frontier, into regime switching, long memory, rough volatility, agent-based markets, machine learning, generative models, and neural stochastic differential equations.THE MODELS COVERED IN THIS BOOK? The random walk - ruin, the arcsine law, and why square-root scaling is not a metaphor? Arithmetic Brownian motion - Bachelier's forgotten thesis, and the negative prices of April 2020? Geometric Brownian motion - the log-normal world and exactly where it lies to you? Mean-reverting models - Ornstein-Uhlenbeck, and the trap of estimating a half-life from short samples? Jump diffusion and L?vy processes - what a crash is, mathematically? Stochastic, local, and stochastic-local volatility - Heston, Dupire, and the smile? GARCH - volatility clustering, and why yesterday still mattersTHE INTERPRETATION SECTIONS ARE THE POINT OF THE BOOK.Plenty of readers can fit a model. Far fewer can say why the persistence estimate crept toward one, what a negative estimated jump intensity means, when a calibrated local volatility surface should be disbelieved, or why adding one parameter can flip the sign of another.FORSelf-taught quants, graduate students entering mathematical finance and industry practitioners. Sprache: Englisch Gewicht in Gramm: 1082.…

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Paperback. Zustand: New. Volatility underpins financial markets by encapsulating uncertainty about prices, individual behaviors, and decisions and has traditionally been modeled as a semimartingale, with consequent scaling properties. This mathematical description has been an active topic of research for decades, however, driven by empirical estimates of the scaling behavior of volatility, a new paradigm has emerged, whereby paths of volatility are rougher than those of semimartingales. According to this perspective, volatility is path-dependent and exhibits jump-like short-term behavior.The first book to offer a comprehensive exploration of the subject, Rough Volatility contributes to the understanding and application of rough volatility models by equipping readers with the tools and insights needed to delve into the topic, exploring the motivation for rough volatility modeling and providing a toolbox for computation and practical implementation, and organizing the material to reflect the subject's development and progression.…

Gebundene Ausgabe. Zustand: Neu. Most books on price dynamics assume you already know the mathematics. This one teaches it.This book begins with a coin flip and builds toward a calibrated volatility surface. Then it takes the framework beyond the Black-Scholes frontier, into regime switching, long memory, rough volatility, agent-based markets, machine learning, generative models, and neural stochastic differential equations.THE MODELS COVERED IN THIS BOOK? The random walk - ruin, the arcsine law, and why square-root scaling is not a metaphor? Arithmetic Brownian motion - Bachelier's forgotten thesis, and the negative prices of April 2020? Geometric Brownian motion - the log-normal world and exactly where it lies to you? Mean-reverting models - Ornstein-Uhlenbeck, and the trap of estimating a half-life from short samples? Jump diffusion and L?vy processes - what a crash is, mathematically? Stochastic, local, and stochastic-local volatility - Heston, Dupire, and the smile? GARCH - volatility clustering, and why yesterday still mattersTHE INTERPRETATION SECTIONS ARE THE POINT OF THE BOOK.Plenty of readers can fit a model. Far fewer can say why the persistence estimate crept toward one, what a negative estimated jump intensity means, when a calibrated local volatility surface should be disbelieved, or why adding one parameter can flip the sign of another.FORSelf-taught quants, graduate students entering mathematical finance and industry practitioners. Sprache: Englisch Gewicht in Gramm: 1082.…