From A to Z
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Updated
Oct 29, 2023 - Python
From A to Z
Compute the Pearson correlation to be used in Gaussian copulas
Research seminar about a fast selection technique for bivariate copulae.
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.
Ensemble of Trees of Pairwise Copulas for extremes
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.
This is where I originally designed my Monte Carlo simulation package (MCmarket) my Mcom financial econometrics course work at Stellenbosch University.
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/).
Generative Models in Commodity Trading
Monte Carlo used for the seminar Monte Carlo Methods in Econometrics and Finance at the university of Copenhagen
Flow-based PC algorithm for causal discovery using Normalizing Flows
A Python Package to Create Synthetic Tabular Data
Notebooks in financial mathematics. Ranging from risk management to portfolio management and stochastic processes for financial markets.
Mostly experiments of quantitative finance concepts that i wish to get a deeper knowledge of the underlying theory
Estimation and inference for conditional copulas models
Master's thesis - Assessment of cognitive load in extreme environment
Semiparametric efficient rank-based estimation of copula parameters
Examples of scheduled jobs estimating copulas at www.microprediction.org
Copula fitting in Python.
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.
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