Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning
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Updated
Nov 18, 2018 - Jupyter Notebook
Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning
Financial Library ( Economic Scenario Generator, Asset Liability Management, Pricing )
Useful functions for Black–Scholes Model in the Julia Language
Pricing derivatives using the explicit finite-difference method
A dynamic strategy that replicates the payoff of a derivative described as a stochastic process
functions and scripts for the course Computational Finance a.c. 2016/2017
An Excel integration of OpenGamma Strata.
Julia Package for Financial Monte Carlo Simulations
Monte Carlo Pricing with extendable PayOff model
Some applications in Financial Mathematics.
Black Scholes Option Pricing calculator with Greeks and implied volatility computations. Geometric Brownian Motion simulator with payoff value diagram and volatility smile plots. Java GUI.
Dockerized development environment with QuantLib C++ library based on Alpine Linux
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.
Matlab code and tools for Quant Research, Data Manipulation and Robust Decision Making
Homepage of Boris Saulnier
Financial options pricing using Monte Carlo implemented on GPU using CUDA.
Implementation of the Carr-Madan formula for fast derivative pricing of European options.
A Machine Learning Approach to Option Pricing
Auxiliary material course Quantitative Finance (Tilburg University)
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