Derivatives Analytics with Python shows you how toimplement market-consistent valuation and hedging approaches usingadvanced financial models, efficient numerical techniques, and thepowerful capabilities of the Python programming language. Thisunique guide offers detailed explanations of all theory, methods,and processes, giving you the background and tools necessary tovalue stock index options from a sound foundation. You'll find anduse self-contained Python scripts and modules and learn how toapply Python to advanced data and derivatives analytics as youbenefit from the 5,000+ lines of code that are provided to help youreproduce the results and graphics presented. Coverage includesmarket data analysis, risk-neutral valuation, Monte Carlosimulation, model calibration, valuation, and dynamic hedging, withmodels that exhibit stochastic volatility, jump components,stochastic short rates, and more. The companion website featuresall code and IPython Notebooks for immediate execution andautomation.
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 financeprofessional's guide to exploiting Python's capabilities forefficient and performing derivatives analytics.
- Reproduce major stylized facts of equity and options marketsyourself
- Apply Fourier transform techniques and advanced Monte Carlopricing
- Calibrate advanced option pricing models to market data
- Integrate advanced models and numeric methods to dynamicallyhedge options
Recent developments in the Python ecosystem enable analysts toimplement analytics tasks as performing as with C or C++, but usingonly about one-tenth of the code or even less. DerivativesAnalytics with Python — Data Analysis, Models, Simulation,Calibration and Hedging shows you what you need to know tosupercharge your derivatives and risk analytics efforts.