📚SDE research and modelling in Finance📚
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Updated
Jun 3, 2024 - Jupyter Notebook
📚SDE research and modelling in Finance📚
Python Financial ENGineering (PyFENG package in PyPI.org)
Implement pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston
A UI-friendly program calculating Black-Scholes options pricing with advanced algorithms incorporating option Greeks, IV, Heston model, etc. Reads input from users, files, databases, and real-time, external market feeds (e.g. APIs).
Quantitative finance and derivative pricing
Collection of notebooks about quantitative finance, with interactive python code.
Financial Derivatives Calculator with 168+ Models (Options Calculator)
Some applications in Financial Mathematics.
American and European options pricer web app build with Flask and React
implement Heston model, which describe stochastic volatility.
Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader
Stochastic volatility models and their application to Deribit crypro-options exchange
Modelling the implicit volatility, using multi-factor statistical models.
R implementation of the Heston option pricing function
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.
Option pricing with various models (Black-Scholes, Heston, Merton jump diffusion, etc) and methods (Monte Carlo, finite difference, Fourier).
Pricing in a Heston model context, using the QE scheme, the Andersen scheme and Monte-Carlo methods to price vanilla options.
Determine implied volatility according to Black-Scholes dynamics.
Vollab (Volatility Laboratory) is a python package for testing out different approaches to volatility modelling within the field of mathematical finance.
Black Scholes Model and Heston Model
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