statistics-fundamentals

Calculating volatility, skewness, and kurtosis for financial return data requires applying descriptive statistics to your return series. This yields metrics detailing average performance and distribution shape.

You can run CAPM regression and test alpha significance using numpy and scipy. The Skill applies regression analysis to benchmark returns and hypothesis testing to evaluate the statistical significance of findings.

The best way to estimate covariance and correlation matrices for portfolio construction is through statistical covariance estimation. This calculates how assets move together, providing foundational data for diversification analysis.

Bootstrapping confidence intervals for a Sharpe ratio calculation involves resampling your financial data. This technique estimates confidence intervals for statistics where analytical formulas are complex.

Analyzing asset return distributions requires Python and specific libraries like numpy and scipy. These dependencies handle the underlying mathematical calculations for hypothesis testing and regression analysis.