Implementations of Leading Algorithms in Quantitative Finance
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Updated
Jun 10, 2017 - Python
Implementations of Leading Algorithms in Quantitative Finance
Undergraduate research project completed at Penn State University on "The Application of PDEs to Finance: Black-Scholes model for Options Pricing"
Implementation of the Black-Scholes model
Lunchbox of basic quantitative models in practice
The library of models being used in quantitative finance.
Option pricing and Delta hedging performance comparison between Black and Scholes vs Artificial Neural Network
Calculate Black Scholes Implied Volatility - Vectorwise
European option pricing, Black and Scholes Model
Black Scholes Model and Heston Model
Black Scholes PDE to calculate Option price and Greek Letter
Determine implied volatility according to Black-Scholes dynamics.
Hedging options by using Monte Carlo simulations or real data
Modelling and analysing the Asian call/put options compared to the standard European option as well as comparing two different modelling methods
Option pricing using Black-Scholes model, Bachelier model, Binomial Trees and Monte Carlo simulation under different stochastic processes
Vanilla option pricing and visualisation using Black-Scholes model in pure Python
Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.
(Put on hold temporarily) The simple option pricer written in Python. The structure and development path is inspired by the book "Numerical Methods in Finance with C++" , M.Capiński & T. Zastawniak. This will be a base to start playing with automatic differentation.
European/American/Asian option pricing module. BSM/Monte Carlo/Binomial
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