Name: Crash Course: Copulas – Theory & Hands-On Project with R
Availability: InStock

What You'll Learn

Understand the fundamentals of copulas – Learn what copulas are, their mathematical properties, and their role in modeling dependence structures
Explore Sklar’s Theorem – Understand how joint cumulative distribution functions (CDFs) decompose into marginal distributions and a copula function
Learn different types of copulas – Study Gaussian, t-Student, Clayton, and Gumbel copulas and their characteristics
Estimate copula parameters in R – Use the copula package to estimate copula parameters through statistical methods
Perform goodness-of-fit tests – Assess the quality of fitted copula models using statistical criteria such as AIC, BIC, and log-likelihood
Visualize copulas in R – Generate contour plots, 3D surfaces, and scatter plots to interpret dependence structures
Simulate data using copulas – Use copulas to generate synthetic datasets that preserve the dependence structure of modeled data
Analyze dependencies – Compute Kendall’s Tau, Spearman’s Rho, and tail dependence coefficients to measure both typical and extreme event correlations

Requirements

Basic understanding of probability and statistics – Familiarity with concepts such as probability density functions (PDFs), cumulative distribution functions (CDFs), joint, marginal, and conditional distributions, as well as correlation.
Basic knowledge of statistical modeling and data analysis.
Familiarity with mathematical functions and their characteristics.
Willingness to work with mathematical formulas and apply them in R.
Ability to install and use R and RStudio on a computer.
Access to a computer with an internet connection to download necessary packages.
Introductory experience with R programming – Including data import, working with basic functions, and handling variables.
Curiosity and motivation to learn copula theory and its applications.
Patience and persistence to analyze dependencies between variables and apply copula-based techniques.

Undergraduate and graduate students in statistics, mathematics, finance, economics, actuarial science, or related fields who want to understand dependence structures using copulas.
Data analysts, statisticians, and researchers interested in modeling and analyzing relationships between random variables beyond traditional correlation methods.
Finance and risk management professionals who need to model financial dependencies, portfolio risks, and credit scoring using copulas.
Actuaries and insurance analysts looking to apply copula models for risk aggregation and loss modeling.
Self-learners and R users eager to expand their knowledge of advanced statistical modeling techniques and hands-on R implementations.

Quantitative Investment & Trading Research Educator

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5,639 Students

4 Courses

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