Covariance Estimation

6 algorithms in Volatility and Covariance · 6 with asserted arithmetic.

In this family#

1. Sample Covariance verified

   Add complete rows and audit sample means, denominator and cells. Supplied-parameter educational reference; no fitted performance claim.

   calculate(data)
2. EWMA Covariance verified

   Watch the same new vector compete with the existing matrix. Supplied-parameter educational reference; no fitted performance claim.

   calculate(data)
3. Ledoit-Wolf Shrinkage verified

   Compare estimated outer-product noise with target distance. Supplied-parameter educational reference; no fitted performance claim.

   calculate(data)
4. Oracle Approximating Shrinkage verified

   Trace the finite-dimensional coefficient rather than assuming library equivalence. Supplied-parameter educational reference; no fitted performance claim.

   calculate(data)
5. Factor-Model Covariance verified

   Change loadings, common-factor risk and specific risk independently. Supplied-parameter educational reference; no fitted performance claim.

   calculate(data)
6. Graphical-Lasso Covariance verified

   Vary penalty independently from sample count and inspect precision edges. Supplied-parameter educational reference; no fitted performance claim.

   calculate(data)

What they share#

Every topic here is a record-transform, so once you have called one the rest follow the same shape. Import paths differ only in the final segment:

import { calculate } from "fintech-algorithms/volatility-and-covariance/covariance-estimation/sample-covariance";
import { calculate } from "fintech-algorithms/volatility-and-covariance/covariance-estimation/ewma-covariance";

Read them in the order above — the sequence is pedagogical, not alphabetical.

Where this sits#

Volatility and Covariance collects 22 algorithms across 4 families. For the concept behind this family rather than the call signatures, see the concept guides.