Quantitative finance and derivative pricing
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Updated
Oct 12, 2024 - Python
Quantitative finance and derivative pricing
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
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.
Config files for my GitHub profile.
Code of numerical experiments in Master's thesis [TBD]
Introducing the data-driven concept through neural networks to price an option whose volatility is measured as a stochastic process.
Investigating Wiener Processes
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
R implementation of the Heston option pricing function
Generate realizations of stochastic processes in python.
R Code to accompany "A Note on Efficient Fitting of Stochastic Volatility Models"
This is a collection of Stochastic indicators. It's developed in PineScript for the technical analysis platform of TradingView.
Comparison of different implementations of the same stochastic volatility model (stochvol, JAGS, Stan)
R codes to implement two examples for the mode and importance sampling estimation methods.
Monte Carlo option pricing algorithms for vanilla and exotic options
A list (quite disorganized for now) of papers tackling the Bayesian estimation of Ito processes (and their discrete time version)
Demonstrates how to price derivatives in a Heston framework, using successive approximations of the invariant distribution of a Markov ergodic diffusion with decreasing time discretization steps. The framework is that of G. Pagès & F. Panloup.
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