implement Heston model, which describe stochastic volatility.
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Updated
Sep 9, 2023 - Jupyter Notebook
implement Heston model, which describe stochastic volatility.
Application used to price an option under the BarbequeRTRM framework
📚SDE research and modelling in Finance📚
The code here is used for several basic financial models and methods, including Black Scholes formula, Monte Carlo Simulation, etc. The codes in this repository are written with C#.
Pricing in a Heston model context, using the QE scheme, the Andersen scheme and Monte-Carlo methods to price vanilla options.
Stochastic volatility models and their application to Deribit crypro-options exchange
Black Scholes Model and Heston Model
R implementation of the Heston option pricing function
American and European options pricer web app build with Flask and React
Quantitative finance and derivative pricing
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.
We apply Finite Element Method (FEM) for option pricing problem under Heston's Model.
Machine Learning for Finance (FIN-418 EPFL) final project: Comparison of different option pricers for the Heston model
This is a simulation project for the seconder order discretization schemes for the CIR process.
Some applications in Financial Mathematics.
Stochastic Valuation Processes for stock prices and bond rates
Lunchbox of basic quantitative models in practice
Determine implied volatility according to Black-Scholes dynamics.
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