Covariance Cleaning and Estimators

Related lectures (30)

Page 2 of 3

Explores the Mean-Variance case, Market Equilibrium, CAPM, and Efficient Market Hypothesis.

Covers the derivation of PCA projection, error minimization, and eigenvector optimization.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Explores risk and return in finance, covering securities' performance, rates of return, variance, volatility, and portfolio diversification.

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

Explores variance, covariance, and practical applications in statistics and probability.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Covers risk measures, VaR, ES computation, backtesting, and multivariate distributions for portfolio risk assessment.