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Multivariate time series generator based on the Phase Annealing algorithm. Various objective functions that focus on multivariate copula properties while annealing. Various plotting routines to visualize results. Take a look at the scripts in the "test" directory for how to use.
A professor I wanted to do research with asked me to read up on copulas before an interview. I ended up doing a bit more than just reading. This is based off the work of Thomas Wiecki (https://twiecki.io/blog/2018/05/03/copulas/).
This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.
The Quant Copula Playground is a Shiny application designed for everyone interested in exploring the dependencies between stock returns using various copula models. This application is inspired by seminal works in the field of copulas, particularly "An Introduction to Copulas" by Roger B. Nelsen.
This is where I originally designed my Monte Carlo simulation package (MCmarket) my Mcom financial econometrics course work at Stellenbosch University.
Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.