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

Reseña del editor:

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.

Reseña del editor:

"Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging "provides the necessary backgroundinformation, theoretical foundations and numerical tools to implement amarket-based valuation of stock index options. Topics are, amongst others, stylized facts of equity and options markets, risk-neutral valuation, Fouriertransform methods, Monte Carlo simulation, model calibration, valuation anddynamic hedging. The financial models introduced in this book exhibit featureslike stochastic volatility, jump components and stochastic short rates. Theapproach is a practical one in that all important aspects are illustrated by aset of self-contained Python scripts.Benefits of Reading the Book: Data Analysis: Learn how to usePython for data and financial analysis. Reproduce major stylized facts ofequity and options markets by yourself.Models: Learn risk-neutral pricingtechniques from ground up, apply Fourier transform techniques to Europeanoptions and advanced Monte Carlo pricing to American options.Simulation: Monte Carlo simulationis the most powerful and flexible numerical method for derivatives analytics.Simulate models with jumps, stochastic volatility and stochastic short rates.Calibration: Use global and localoptimization techniques (incl. penalties) to calibrate advanced option pricingmodels to market quotes for options with different strikes and maturities.Hedging: Learn how to use advancedoption pricing models in combination with advanced numerical methods todynamically hedge American options.Python: All results, graphics, etc.presented are in general reproducible with the Python scripts accompanying thebook. Benefit from more than 5,500 lines of code.

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