Reference desk / edition 2026.10
Statistical estimation
Inspect covariance regularization, regression and smoothing without confusing fitted relationships with forecasts.
Calculation references
Identity-target covariance shrinkage
Apply a declared convex shrinkage intensity without pretending it was statistically estimated.
Zero-mean EWMA variance
Track an explicit initial variance through a zero-mean exponentially weighted update.
Univariate least-squares fit
Inspect an in-sample slope, intercept and fit summary for aligned numerical pairs.
Single-benchmark return beta
Calculate a descriptive raw-return sensitivity to one aligned benchmark series.
Detailed guides
Statistical estimationCovariance Shrinkage Is a Choice of Target, Not Just Intensity
The same shrinkage intensity can preserve individual variances, pull them together or impose factor structure. Those choices produce different risk estimates and allocations.
Statistical estimationEWMA Variance Needs a Seed and an Information Clock
The decay parameter alone does not define an EWMA variance series. Initialization, mean treatment, normalization and observation timing determine its early and subsequent values.
Statistical estimationRegression fit is not forecast evidence
Separate least-squares estimation, conditional predictions and genuinely unseen forecast evaluation, including what a high fitted R-squared cannot establish.
Statistical estimationBeta depends on its window and benchmark
Interpret beta as an estimated slope with a specific return definition, benchmark and observation window, rather than a permanent label attached to an asset.