QuantLib Python Cookbook

Description

Quantitative finance in Python: a hands-on, interactive look at the QuantLib library through the use of Jupyter notebooks as working examples. Table of Contents A note on Python and C++ Code conventions used in this book Basics QuantLib basics Instruments and pricing engines Numerical Greeks calculation Market quotes Term structures and their reference dates Pricing over a range of days A note on random numbers and dimensionality Interest-rate curves EONIA curve bootstrapping Euribor curve bootstrapping Constructing a yield curve Dangerous day-count conventions Implied term structures Interest-rate sensitivities via zero spread A glitch in forward-rate curves Interest-rate models Simulating interest rates using Hull White model Thoughts on the convergence of Hull-White model Monte Carlo simulations Short interest rate model calibration Par versus indexed coupons Modeling interest rate swaps using QuantLib Caps and floors Equity models Valuing European option using the Heston model Volatility smile and Heston model calibration Heston model parameter calibration in QuantLib Python & SciPy Valuing European and American options Valuing options on commodity futures using the Black formula Defining rho for the Black process Using curves with different day-count conventions Bonds Modeling fixed rate bonds Building irregular bonds Valuation of bonds with credit spreads Modeling callable bonds Discount margin calculation Duration of floating-rate bonds Treasury futures contracts Mischievous pricing conventions More mischievous conventions Appendix Translating QuantLib Python examples to C++