squeeze-kernel 0.2.0

Streaming, PSD-by-construction covariance estimator with Fisher-kernel weighting and adaptive shrinkage

Squeeze Kernel Covariance Estimator

A streaming covariance estimator for panels of daily financial returns. One O(n²) update per day, positive semi-definite by construction at every step, missing values handled natively, and defaults that require no tuning. Only dependency: NumPy.

Reference: "The Squeeze Kernel Covariance Estimator: Dual-Timescale Tracking with Adaptive Shrinkage" (Kende, 2026).

Why

Rolling-window estimators (Ledoit–Wolf, nonlinear shrinkage, RMT denoising) refit over a fixed window each day and cannot adapt within it; multivariate GARCH (DCC) adapts but needs multi-stage estimation and a fragile news coefficient. The Squeeze Kernel is a single streaming recursion that:

• is PSD at every step, structurally — never needs eigenvalue clipping, nearest-PSD projection, or a solver;
• adapts fastest exactly when it matters — a Fisher-information kernel up-weights high-dispersion (stress) days, when correlation regimes actually move;
• regularises itself — an adaptive equicorrelation shrinkage activates automatically as the asset count approaches the effective sample size, with a provable condition-number bound;
• ingests missing values natively — listings, delistings, and halts enter as NaN; no imputation or complete-case subsetting;
• is fast — a full 30-year daily pass takes ~0.75 s at n=100 and ~3.4 s at n=300 (single-threaded), 30–40× faster than rolling-window baselines at scale.

On a 30-year S&P 500 panel (n=100, ~7,600 out-of-sample days) it statistically ties DCC on one-step density forecasts and beats EWMA, Ledoit–Wolf, OAS, nonlinear shrinkage, RMT denoising, and the Gerber statistic — and it is the only method in the 90% model confidence set together with DCC. At n=300 it leads every competitor that remains statistically viable.

Installation

pip install squeeze-kernel          # NumPy only
pip install "squeeze-kernel[full]"  # + SciPy (kappa calibration, chi² kernel)

Quickstart

import numpy as np
from squeeze_kernel import SqueezeKernelEstimator

# daily_returns: array of shape (T, n) — may contain NaN for missing assets
est = SqueezeKernelEstimator(n_assets=daily_returns.shape[1])

for r_t in daily_returns:          # stream one day at a time
    est.update(r_t)

cov  = est.get_cov()               # (n, n) covariance, PSD by construction
corr = est.get_corr()              # (n, n) correlation

That is the whole API for most uses. The defaults (lambda_vol=0.98, lambda_corr=0.996, kappa=0.25) are the paper-recommended settings for daily returns, selected by time-series cross-validation and robust across a 50× parameter sweep — deploy them as-is.

Batch mode, if you prefer the full path in one call:

from squeeze_kernel import estimate_squeeze_cov

cov_path, corr_path, weights = estimate_squeeze_cov(daily_returns, with_weights=True)
# cov_path: (T, n, n) — the estimate after each day

A complete runnable walkthrough (streaming, missing data, batch) is in examples/quickstart.py.

Missing values

Pass NaN for any asset not observed on a given day — nothing else to do:

r_t = np.array([0.004, np.nan, -0.011])   # asset 2 not trading today
est.update(r_t)                            # PSD preserved, no imputation

Parameters

Parameter	Default	Meaning
lambda_vol	0.98	volatility EWMA decay (half-life ≈ 34 days)
lambda_corr	0.996	correlation EWMA decay (half-life ≈ 173 days, T_eff ≈ 250)
kappa	0.25	Fisher kernel saturation; higher = stronger calm-day filtering
shrinkage	"auto"	adaptive equicorrelation shrinkage ("none" or a float to override)
shrinkage_delta	0.10	concentration threshold at which shrinkage activates

Useful read-only state after each update(): est.weight (last kernel weight), est.effective_sample_size (kernel-weighted T_eff), est.shrinkage_intensity (current α).

To recalibrate kappa for a different asset class (requires the full extra):

kappa = SqueezeKernelEstimator.calibrate_kappa(burn_in_returns, target_weight=0.6)

Advanced options

Score-exact weighting (weight_statistic="mahalanobis", use with kappa=1.0): drives the kernel with the Mahalanobis surprise z'C⁻¹z/N against the estimator's own correlation instead of the marginal dispersion. Improves accuracy in the moderate-concentration regime — use only when n / T_eff ≲ 0.5 (e.g. n ≤ 100 at the default lambda_corr); at higher concentration the estimated inverse degrades it and the default is strictly better.

est = SqueezeKernelEstimator(n_assets=100, kappa=1.0, weight_statistic="mahalanobis")

Score-driven memory (lambda_corr_fast=0.99): lets stress days also shorten the correlation memory (decay slides from lambda_corr toward lambda_corr_fast as the kernel weight rises). Do not combine with the Mahalanobis option — they act on the same channel and the combination degrades accuracy.

Alternative kernels: pass kernel_fn=kernel_exponential (with kernel_kwargs={"gamma": ...}) or kernel_chi2_cdf, or any callable (d2, *, n_observed, **kw) -> float mapping to [0, 1). The PSD guarantee holds for any such kernel.

How it works

Three mechanisms in one recursion:

1. Dual-timescale EWMA — fast per-asset volatility (lambda_vol) is separated from slow correlation dynamics (lambda_corr), so variance shocks don't contaminate the correlation estimate.
2. Fisher kernel weighting — each day's standardized outer product enters with weight w = d²/(d² + kappa), where d² is the mean squared standardized return: calm days contribute little, dispersion shocks contribute fully.
3. Adaptive equicorrelation shrinkage — alpha = min(1, max(0, n/(2·S) − delta)) blends toward an equicorrelation target using the estimator's own kernel-weighted sample size S; it is a no-op at low dimension and provides provably bounded conditioning at high dimension.

The complete update is a natural-gradient step on the Gaussian log-likelihood, with the kernel weight acting as an adaptive Riemannian learning rate (paper, Appendix B).

Development

uv sync --extra full --extra dev
uv run python -m pytest        # test suite
uv run python -m ruff check .  # lint
uv build                       # build sdist + wheel

Releases: publishing a GitHub release from a v* tag triggers the publish workflow, which builds and uploads to PyPI via trusted publishing.

Citation

@article{kende2026squeeze,
  title  = {The Squeeze Kernel Covariance Estimator: Dual-Timescale Tracking with Adaptive Shrinkage},
  author = {Kende, Robert},
  year   = {2026}
}

See also CITATION.cff.

License

MIT