--- name: Covariance Assessment slug: covariance-assessment category: Quality description: Covariance Assessment rigorously evaluates a new covariance, correlation, or precision estimator against the precise contract, benchmarks, out-of-sample tests, and statistically defensible inference. Use it when comparing or validating a proposed covariance methodology. github: "https://github.com/microprediction/precise/tree/main/.claude/skills/assess-covariance-method" language: Python stars: 333 forks: 58 install: "npx degit https://github.com/microprediction/precise/tree/main/.claude/skills/assess-covariance-method ~/.claude/skills/assess-covariance-method" installs_to: ~/.claude/skills/assess-covariance-method source_path: .claude/skills/assess-covariance-method/SKILL.md collection_size: 6 category_size: 1354 collection_url: "https://dirskills.com/collections/microprediction/precise" added: 2026-08-12T04:43:51.943Z last_synced: 2026-08-12T04:43:51.943Z canonical_url: "https://dirskills.com/skills/covariance-assessment" --- # Covariance Assessment Covariance Assessment rigorously evaluates a new covariance, correlation, or precision estimator against the precise contract, benchmarks, out-of-sample tests, and statistically defensible inference. Use it when comparing or validating a proposed covariance methodology. **Install:** ```bash npx degit https://github.com/microprediction/precise/tree/main/.claude/skills/assess-covariance-method ~/.claude/skills/assess-covariance-method ``` ## README # Assess a new covariance/correlation methodology A protocol for turning "here's a covariance idea" into a defensible verdict. Work the steps in order; stop early only if a step fails. Install: `pip install precise[research]` (the `research/` scripts use scikit-learn and `randomcov`). ## 0. Classify the method first - **Estimator** (produces a matrix) vs **assessor** (scores a matrix)? Different paths below. - **Online** (updatable per observation) or **batch**? precise is an online library; a batch method can still be wrapped, but say so. - Does it target the **covariance**, **correlation**, or **precision**? It must expose, or be convertible to, `covariance_`. - Does evaluating it **need the ground-truth** covariance (simulation only) or work on real data? ## 1. Implement it to the contract Subclass `BaseOnlineCovariance`. The cheapest correct route: **copy the simplest existing estimator and modify it** — read `precise/empirical.py` (and `precise/base.py` for the exact hook signatures, typically `_init_state` / `_update_state` / `_state_to_cov` / `_state_to_mean`). The base class owns `partial_fit`, `fit`, and the derived attributes; the subclass only supplies the state update and the map to a covariance. Register it in `precise/registry.py` so `all_estimators()` includes it. A scoring rule instead? Implement an `Assessor` (see `precise/assessment/`), set `needs_data` / `needs_truth`, and follow the **higher = better** convention. ## 2. Conformance — non-negotiable Run the conformance suite (`tests/test_estimators.py` parametrizes over `all_estimators()`), or check the invariants directly: - `covariance_` is symmetric and PSD (eigenvalues ≥ 0); - `correlation_` has unit diagonal; `precision_ @ covariance_ ≈ I` when well-conditioned; - streaming rows via `partial_fit` equals `fit(X)` for non-windowed estimators; - `set_state(get_state())` round-trips; - it runs on the numpy-only install (no hidden heavy deps). If it fails any of these, fix the implementation before any benchmarking — numbers from a non-conformant estimator are meaningless. ## 3. Pick the right judge BEFORE looking at results This is where most covariance evaluations go wrong. **In high dimensions (`p` comparable to `n`), do not rank estimators by the held-out Gaussian likelihood** — it is dominated by unidentifiable small eigenvalues and ranks estimators below chance. Use the assessor panel and choose by regime (see the **score-covariance-estimate** skill): `BlockPseudoLikelihood` / `SchurLikelihood` / `VariogramScore` / `GMVVariance` in high-d; `LogLikelihood` only when low-d and well-conditioned; a QLIKE/Bregman-consistent loss (not RMSE) when the target is a noisy variance proxy. To choose defensibly, measure the **statistical power** of candidate judges — the probability they reproduce a known quality ordering — with `research/metric_power.py`. ## 4. Benchmark against the registry `research/bakeoff.py` runs every estimator over discriminating scenarios and scores them with `all_assessors()`. Add the new estimator and compare. Report **relative error vs a naive / shrinkage baseline within each scenario, then averaged** (RMSE is scale-sensitive and can be won by doing well only in high-volatility regimes). Always include a `0`/historical-mean baseline for returns-like targets. ## 5. Sweep the data-generating process — results are ensemble-sensitive A win on one generator need not transfer. Generate ground truth across several ensembles (LKJ, Wishart, factor/spiked, Toeplitz/AR, equicorrelation) with `randomcov` plus plain numpy, sample from each, and **report per-ensemble, not just pooled**. A single ensemble can manufacture or hide any effect. ## 6. Out-of-sample and real data; be honest about selectors - Out-of-sample on synthetic ensembles: `research/oos.py`. On real equity data: `research/oos_equity.py` (bundled Ken French returns, no key needed). - If the method is a **recommender / selector** (chooses among estimators), evaluate it **leave-one-generative-family-out** (`leave_one_family_out_trained` in `research/oos.py`), not leave-one-sample-out — selectors generalize across samples far more easily than across novel structure, and the difference is exactly where they fail. - **Report nulls.** If it ties or loses, say so plainly with the number; a method that "matches the best fixed estimator and avoids catastrophe" is a real but smaller claim than "beats it." ## 7. Inference done right Pairwise significance is the easiest thing to get wrong. The loss differentials are almost always **dependent**: overlapping forecast horizons, rolling/trailing targets, repeated expanding-window splits, cross-asset correlation, and many pairwise comparisons. Consequently: - A naive Diebold–Mariano statistic, or standard-error bars computed as if splits were independent, **overstates significance**. Treat such bars as descriptive, not as confidence intervals. - Use a **block bootstrap over dates (and clustering over correlated assets)**, or the **Model Confidence Set** (Hansen, Lunde & Nason 2011), which handles dependence and multiplicity together — preferable to a wall of pairwise stars. - State one- vs two-sided, and any multiple-comparison treatment. - Watch the **trailing-vs-forward target trap**: if the "target" is a rolling window that overlaps already observed data (e.g. next value of a 7-day trailing std), the task is partly mechanical and apparent forecastability is an artifact. Define targets so the forecast origin uses only past information. ## 8. Honest reporting checklist - State exactly what was estimated and scored, and which assessor (and why, given the dimension). - Give effect sizes and relative errors, not just p-values. - Report per-ensemble and on real data; note where the method loses. - Don't read point-error gains as economic/trading signals. - Ship the code and tests so the numbers are reproducible (this repo's `research/` scripts are the model: each headline number has a runnable script and a guarding test). ## Reference - Estimator contract: `precise/base.py`, `precise/empirical.py`; registry: `precise/registry.py`. - Assessors: `precise/assessment/`; panel via `all_assessors()`. - Experiments: `research/bakeoff.py`, `research/metric_power.py`, `research/oos.py`, `research/oos_equity.py`; Schur-likelihood theory: `research/schur_*.py`. - Background on why the high-dimensional likelihood fails and what to use instead: .