Copulas: Modeling Dependence in Financial Engineering

Related lectures (30)

Page 2 of 3

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Covers elliptical distributions, including properties, applications, and risk management implications.

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Covers the properties and applications of copulas, including examples and coefficients of tail dependence.

Covers the multivariate normal distribution, properties, and sampling methods.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Covers rank correlations, tail dependence, and copula fitting methods.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.