Estimation and inference for conditional copulas models
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Updated
May 31, 2024 - R
Estimation and inference for conditional copulas models
A library to model multivariate data using copulas.
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series, from ServiceNow Research
Semiparametric efficient rank-based estimation of copula parameters
Compute the Pearson correlation to be used in Gaussian copulas
Multivariate data modelling with Copulas in Python
Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.
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.
Master's thesis - Assessment of cognitive load in extreme environment
Python package for canonical vine copula trees with mixed continuous and discrete marginals
Copula fitting in Python.
From A to Z
Mostly experiments of quantitative finance concepts that i wish to get a deeper knowledge of the underlying theory
Monte Carlo used for the seminar Monte Carlo Methods in Econometrics and Finance at the university of Copenhagen
A Python Package to Create Synthetic Tabular Data
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.
Notebooks in financial mathematics. Ranging from risk management to portfolio management and stochastic processes for financial markets.
Examples of scheduled jobs estimating copulas at www.microprediction.org
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.
Flow-based PC algorithm for causal discovery using Normalizing Flows
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