QuantLib Python Cookbook

✍ Scribed by Luigi Ballabio, Goutham Balaraman

• Year: 2022
• Tongue: English
• Leaves: 286
• Category: Library

✦ Synopsis

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

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++