skfolio.metrics.portfolio_variance_calibration_loss#

skfolio.metrics.portfolio_variance_calibration_loss(estimator, X_test, y=None, portfolio_weights=None)[source]#

Portfolio variance calibration loss.

Computes the absolute deviation of portfolio_variance_calibration_ratio from its calibration target of 1.0.

Let \(r_t\) be the one-period realized return vector at time \(t\) and \(w^\top r_t\) the corresponding one-period portfolio return for weights \(w\).

\[\ell = \left\lvert \frac{\sum_{t=1}^{h} (w^\top r_t)^2} {h\, w^\top \Sigma\, w} - 1 \right\rvert\]

When multiple portfolios are provided, the loss is the absolute deviation of the mean ratio from 1.0.

Parameters:

estimatorBaseEstimator: Fitted estimator, must expose covariance_ or return_distribution_.covariance.
X_testarray-like of shape (n_observations, n_assets): Realized returns for the test window.
yIgnored: Present for scikit-learn API compatibility.
portfolio_weightsarray-like of shape (n_assets,) or (n_portfolios, n_assets), optional: Portfolio weights. If None (default), inverse-volatility weights are used, which neutralizes volatility dispersion so that high-volatility assets do not dominate the diagnostic. If a 2D array is provided, each row defines a test portfolio. For equal-weight calibration, pass portfolio_weights=np.ones(n_assets) / n_assets.

Returns:

float: Calibration loss. Lower values are better and the optimum is 0.0.