Multivariate Distributions: Spherical and Elliptical

Related lectures (31)

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Explores principal components, covariance, correlation, choice, and applications in data analysis.

Covers risk measures used for determining risk capital and capital adequacy.

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Explores risk management concepts, including VaR, ES, and measurement methods.

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Covers spherical and elliptical distributions of random vectors.

Covers copulas, Sklar's Theorem, types of copulas, and simulation of copulas for risk management.

Covers Value at Risk (VaR) and Expected Shortfall (ES) in risk management, including backtesting and multivariate distributions.

Covers copulas, Sklar's Theorem, meta distributions, and various dependence measures like rank correlations and coefficients of tail dependence.

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.