copulas
There are 2 repositories under copulas topic.
- CopulasLanguage:Python 504
- Language:Jupyter Notebook 134
asnelt / mixedvines
Python package for canonical vine copula trees with mixed continuous and discrete marginals
Language:Python 45M-Soundouss / CopulaConformalMTR
Code for paper "Copula-based conformal prediction for Multi-Target Regression"
Language:Python 25Sinbad-The-Sailor / Abacus
Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.
extreme-value-theorymonte-carlo-simulationvine-copulasmodel-predictive-controlportfolio-optimizationfinancetime-seriesneural-networkstrading-botalgorithmic-tradingrisk-managementcopulastrading-strategiesstochastic-processesstochastic-programmingcryptocurrencycryto-currencyquantitative-financequantitative-tradingLanguage:Jupyter Notebook 17Matlab toolbox for canonical vine copula trees with mixed continuous and discrete marginals
Language:MATLAB 14ElsevierSoftwareX / SOFTX-D-21-00039
MATVines: A Vine Copula Package for MATLAB. To cite this software publication: https://www.sciencedirect.com/science/article/pii/S2352711021000455
Language:MATLAB 13itsoukal / anySim
An R package for the stochastic simulation of processes with any marginal distribution and correlation structure
Language:R 8AlexisDerumigny / MMDCopula
Robust Estimation of Copulas by Maximum Mean Discrepancy
Language:R 5antorguez95 / synthetic_data_generation_framework
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.
Language:Python 5microprediction / microactors
Examples of scheduled jobs estimating copulas at www.microprediction.org
Language:Python 4ramonVDAKKER / research-copulas
Semiparametric efficient rank-based estimation of copula parameters
Language:MATLAB 3Estimation and inference for conditional copulas models
Language:R 2- Language:Jupyter Notebook 2
dcuoliveira / QuantFinDrafts
Mostly experiments of quantitative finance concepts that i wish to get a deeper knowledge of the underlying theory
Language:Jupyter Notebook 2Notebooks in financial mathematics. Ranging from risk management to portfolio management and stochastic processes for financial markets.
Language:Jupyter Notebook 2- sklarpyLanguage:Python 2
- Language:Python 1
faizan90 / phsann
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.
Language:Python 1hetankevin / copulas
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/).
Language:Jupyter Notebook 1Nathan-Potgieter / Making-of-MCmarket
This is where I originally designed my Monte Carlo simulation package (MCmarket) my Mcom financial econometrics course work at Stellenbosch University.
Language:HTML 1sokolmarek / masters-thesis
Master's thesis - Assessment of cognitive load in extreme environment
Language:Jupyter Notebook 1thorpn / MonteCarlo
Monte Carlo used for the seminar Monte Carlo Methods in Econometrics and Finance at the university of Copenhagen
Language:MATLAB 1vpozdnyakov / gen_models_in_trading
Generative Models in Commodity Trading
Language:Jupyter Notebook 1adknudson / PearsonCorrelationMatch.jl
Compute the Pearson correlation to be used in Gaussian copulas
Language:Julia 0- Language:MATLAB 0
holmen1 / economic-scenario-generator
From A to Z
Language:Python 0nikpau / cop-select
Research seminar about a fast selection technique for bivariate copulae.
Language:TeX 0MaxMLang / copula-playground
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.
Language:R
