Additional information
by Yves Hilpisch (Author)
Supercharge options analytics and hedging using the power of Python
Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation.
Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics.
- Reproduce major stylized facts of equity and options markets yourself
- Apply Fourier transform techniques and advanced Monte Carlo pricing
- Calibrate advanced option pricing models to market data
- Integrate advanced models and numeric methods to dynamically hedge options
Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python -- Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.
Front Jacket
Market-based valuation of stock index options is an essential task for every buy-side and sell-side decision maker in the derivatives analytics domain. In Derivatives Analytics with Python, you'll discover why Python has established itself in the financial industry and how to leverage this powerful programming language so you can implement market-consistent valuation and hedging approaches.
Written for Quant developers, traders, risk managers, compliance officers, and model validators, this reliable resource skillfully covers the four areas necessary to effectively value options: market-based valuation as a process; sound market model; numerical techniques; and technology. Presented in three parts, Part One looks at the risks affecting the value of equity index options and empirical facts regarding stocks and interest rates. Part Two covers arbitrage pricing theory, risk-neutral valuation in discrete time, continuous time, and introduces the two popular methods of Carr-Madan and Lewis for Fourier-based option pricing. Finally, Part Three considers the whole process of a market-based valuation effort and the Monte Carlo simulation as the method of choice for the valuation of exotic and complex index options and derivatives.
Practical and informative, with self-contained Python scripts and modules and 5,000+ lines of code provided to help you reproduce the results and graphics presented. In addition, the companion website (http: //wiley. quant-platform.com) features all code and IPython Notebooks for immediate execution and automation.
Author Yves Hilpisch explores market-based valuation as a process, as well as empirical findings about market realities. By reading this book, you'll be equipped to develop much-needed tools during a market-based valuation with balanced coverage of:
- Market-based valuation
- Risk-neutral valuation
- Discrete market models
- Black-Scholes-Merton Model
- Fourier-based option pricing
- Valuation of American options
- Stochastic volatility and jump-diffusion models
- Model calibration
- Simulation and valuation
Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver pricing, trading, and risk management results. Learn to implement market-consistent valuation and hedging approaches for European and American options with the solid guidance found in Derivatives Analytics with Python.
Back Jacket
Market-based valuation of stock index options is an essential task for every buy-side and sell-side decision maker in the derivatives analytics domain. In Derivatives Analytics with Python, you'll discover why Python has established itself in the financial industry and how to leverage this powerful programming language so you can implement market-consistent valuation and hedging approaches.
Written for Quant developers, traders, risk managers, compliance officers, and model validators, this reliable resource skillfully covers the four areas necessary to effectively value options: market-based valuation as a process; sound market model; numerical techniques; and technology. Presented in three parts, Part One looks at the risks affecting the value of equity index options and empirical facts regarding stocks and interest rates. Part Two covers arbitrage pricing theory, risk-neutral valuation in discrete time, continuous time, and introduces the two popular methods of Carr-Madan and Lewis for Fourier-based option pricing. Finally, Part Three considers the whole process of a market-based valuation effort and the Monte Carlo simulation as the method of choice for the valuation of exotic and complex index options and derivatives.
Practical and informative, with self-contained Python scripts and modules and 5,000+ lines of code provided to help you reproduce the results and graphics presented. In addition, the companion website (http: //wiley.quant-platform.com) features all code and IPython Notebooks for immediate execution and automation.
Author Yves Hilpisch explores market-based valuation as a process, as well as empirical findings about market realities. By reading this book, you'll be equipped to develop much-needed tools during a market-based valuation with balanced coverage of:
- Market-based valuation
- Risk-neutral valuation
- Discrete market models
- Black-Scholes-Merton Model
- Fourier-based option pricing
- Valuation of American options
- Stochastic volatility and jump-diffusion models
- Model calibration
- Simulation and valuation
Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver pricing, trading, and risk management results. Learn to implement market-consistent valuation and hedging approaches for European and American options with the solid guidance found in Derivatives Analytics with Python.
Author Biography
YVES HILPISCH is founder and Managing Partner of The Python Quants, a group that focuses on Python & Open Source Software for Quantitative Finance. Yves is also a Computational Finance Lecturer on the CQF Program. He works with clients in the financial industry around the globe and has ten years of experience with Python. Yves is the organizer of Python and Open Source for Quant Finance conferences and meetup groups in Frankfurt, London and New York City.
