skfolio.moments.BaseVariance#

class skfolio.moments.BaseVariance(assume_centered=False)[source]#

Base class for all variance estimators in skfolio.

Variance estimators estimate the diagonal elements of a covariance matrix, assuming zero correlation between assets. This is appropriate when:

Estimating idiosyncratic (specific) risk in factor models, where residual returns are uncorrelated by construction
Working with orthogonalized or uncorrelated return series
The full covariance structure is not needed or is constructed separately

Parameters:

assume_centeredbool, default=False: If False (default), the data are mean-centered before computing the variance. This is the standard behavior when working with raw returns where the mean is not guaranteed to be zero. If True, the estimator assumes the input data are already centered. Use this when you know the returns have zero mean, such as pre-demeaned data or regression residuals.

Attributes:

variance_ndarray of shape (n_assets,): Estimated variance vector \((\\sigma^2_1, ..., \\sigma^2_n)\).
location_ndarray of shape (n_assets,): Estimated location, i.e. the estimated mean. When assume_centered=True, this is zero. When assume_centered=False, this is the sample mean.
n_features_in_int: Number of assets seen during fit.
feature_names_in_ndarray of shape (n_features_in_,): Names of assets seen during fit. Defined only when X has asset names that are all strings.

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.

fit

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.