Stochastic Volatility Estimated by MCMC (Markov Chain Monte Carlo) Method
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Updated
Nov 20, 2019 - MATLAB
Stochastic Volatility Estimated by MCMC (Markov Chain Monte Carlo) Method
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.
Python implementation of the Markov-Switching Multifractal model (MSM) of Calvet & Fisher (2004, 2008).
Discretize VAR(1) of arbitrary size, with arbitrary covariance matrix for innovations, and optional stochastic volatility.
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Config files for my GitHub profile.
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).
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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.
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.
Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston
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