Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston
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Updated
Jul 23, 2024 - Python
Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston
Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.
An implementation of the Heston model, a stochastic volatility model for options pricing. We compute prices of European call and put options via Monte Carlo simulation, for a variety of strike prices and maturities. We also show that the Heston model captures volatility smiles/smirks/skews.
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).
The workings for a very interesting exercise from the Econometrics of Financial Markets module of the MSc Quantitative Finance 2023/24 course at Bayes Business School (formerly Cass).
Config files for my GitHub profile.
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This repository provides TensorFlow compatible code for some stochastic volatility models widely used in derivatives pricing.
American and European options pricer web app build with Flask and React
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implement Heston model, which describe stochastic volatility.
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TVP-GVAR-FSVM model proposed in "Measuring international uncertainty using global vector autoregressions with drifting parameters"
Discretize VAR(1) of arbitrary size, with arbitrary covariance matrix for innovations, and optional stochastic volatility.
Python implementation of the Markov-Switching Multifractal model (MSM) of Calvet & Fisher (2004, 2008).
Financial analysis and demonstration of the classic algorithmic trading method, pair trading. This analysis compares the portfolio's growth with the underlying assets value and volatility over time.
Stochastic Volatility Estimated by MCMC (Markov Chain Monte Carlo) Method
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