Financial Recipes

Running Jupyter Locally

The easiest is probably to install Docker and use the provided Dockerfile.

The Makefile is set up to do just this, so once you have docker installed, just type:

make

And open your browser at address localhost:8888. The changes you make will be stored to the notebooks folder in the current directory, which you can then commit to git.

Models and Pricing

Heston stochastic volatility model
Black-Scholes
Vasicek
Hull-White for interest rates [1-factor, 2-factor]
Garman-Kohlhagen for FX
CEV (constant elasticity of variance) model
Bachelier model
SABR model
A DSL for models (and basis for stochastic sampling)

Computational ingredients/components

Brownian bridge
Quasirandom number generation
- Sobol numbers
- van der Corput numbers
Pseudorandom number generation
Monte-Carlo simulation
Longstaff-Schwartz (for American options);

Finite differencing for PDE solving

binomial trees, lattices

Risk

Value at Risk
“Greeks” — sensitivities to designated parameters (risk factors)
- Automatic differentiation

Date/time

Day count conventions
Data conversion

Notes on Parallelism

Vectorisation

Related Work

Paolo Brandimarte. Numerical methods in finance and economics: a MATLAB-based introduction. John Wiley & Sons, Inc., 2006. Available via REX: http://onlinelibrary.wiley.com.ep.fjernadgang.kb.dk/book/10.1002/0470080493
Seydel, Rüdiger. Tools for computational finance. Berlin: Springer, 2006. Available via REX: http://link.springer.com.ep.fjernadgang.kb.dk/book/10.1007%2F3-540-27926-1

HIPERFIT/recipes