Skip to content

ShrunkCovariance

Fixed-intensity shrinkage covariance, towards a constant-correlation or identity target.

Where LedoitWolfCovariance and OASCovariance estimate the shrinkage intensity from the data, ShrunkCovariance uses a fixed delta (transparent and predictable, mirroring sklearn.covariance.ShrunkCovariance) and offers a choice of target.

When to use it. When you want explicit, reproducible control over how much regularisation is applied rather than letting the data decide. The constant-correlation target (every pair shrunk towards the average sample correlation) is the finance-relevant default: assets share a positive baseline correlation, so pulling towards that is more sensible than pulling towards the zero-correlation identity target.

Parameters

  • fading_factor

    Type → float

    Default → 0.5

    Recency weight of the underlying exponentially weighted covariance.

  • delta

    Type → float

    Default → 0.1

    Shrinkage intensity in [0, 1] (0 = the raw covariance, 1 = the target).

  • target

    Type → str

    Default → constant_correlation

    Either "constant_correlation" (default) or "identity".

Attributes

  • matrix

Examples

from river import covariance, datasets

tickers = ["AAPL", "JPM", "XOM"]
cov = covariance.ShrunkCovariance(fading_factor=0.02, delta=0.3)
for x, _ in datasets.SP500Stocks():
    cov.update({t: x[t] for t in tickers})
cov

       AAPL    JPM     XOM
AAPL   1.944   0.784   0.797
 JPM   0.784   1.492   0.885
 XOM   0.797   0.885   1.705

With delta=0 the estimator reduces to a plain exponentially weighted covariance, and with delta=1 it returns the target exactly.

Methods

update

Update with a single sample.

Parameters

  • x — dict

update_many

Update with a dataframe of samples.

Any narwhals-compatible eager dataframe (pandas, polars, pyarrow, ...) is accepted. The result is identical to feeding the rows one at a time with update, in row order.

Parameters

  • X — IntoDataFrame

References