Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging (The Wiley Finance Series) 1st Edition

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.

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.