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.
