Source code for skfolio.moments.covariance._empirical_covariance
"""Empirical Covariance Estimators."""
# Copyright (c) 2023
# Author: Hugo Delatte <delatte.hugo@gmail.com>
# License: BSD 3 clause
# Implementation derived from:
# scikit-learn, Copyright (c) 2007-2010 David Cournapeau, Fabian Pedregosa, Olivier
# Grisel Licensed under BSD 3 clause.
import numpy as np
import numpy.typing as npt
from skfolio.moments.covariance._base import BaseCovariance
[docs]
class EmpiricalCovariance(BaseCovariance):
"""Empirical Covariance estimator.
Parameters
----------
window_size : int, optional
Window size. The model is fitted on the last `window_size` observations.
The default (`None`) is to use all the data.
ddof : int, default=1
Normalization is by `(n_observations - ddof)`.
Note that `ddof=1` will return the unbiased estimate, and `ddof=0`
will return the simple average. The default value is `1`.
nearest : bool, default=True
If this is set to True, the covariance is replaced by the nearest covariance
matrix that is positive definite and with a Cholesky decomposition than can be
computed. The variance is left unchanged.
A covariance matrix that is not positive definite often occurs in high
dimensional problems. It can be due to multicollinearity, floating-point
inaccuracies, or when the number of observations is smaller than the number of
assets. For more details, see :func:`~skfolio.utils.stats.cov_nearest`.
The default is `True`.
higham : bool, default=False
If this is set to True, the Higham & Nick (2002) algorithm is used to find the
nearest PD covariance, otherwise the eigenvalues are clipped to a threshold
above zeros (1e-13). The default is `False` and use the clipping method as the
Higham & Nick algorithm can be slow for large datasets.
higham_max_iteration : int, default=100
Maximum number of iteration of the Higham & Nick (2002) algorithm.
The default value is `100`.
Attributes
----------
covariance_ : ndarray of shape (n_assets, n_assets)
Estimated covariance matrix.
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 assets names that are all strings.
"""
def __init__(
self,
window_size: int | None = None,
ddof: int = 1,
nearest: bool = True,
higham: bool = False,
higham_max_iteration: int = 100,
):
super().__init__(
nearest=nearest,
higham=higham,
higham_max_iteration=higham_max_iteration,
)
self.window_size = window_size
self.ddof = ddof
[docs]
def fit(self, X: npt.ArrayLike, y=None) -> "EmpiricalCovariance":
"""Fit the empirical covariance estimator.
Parameters
----------
X : array-like of shape (n_observations, n_assets)
Price returns of the assets.
y : Ignored
Not used, present for API consistency by convention.
Returns
-------
self : EmpiricalCovariance
Fitted estimator.
"""
X = self._validate_data(X)
if self.window_size is not None:
X = X[-int(self.window_size) :]
covariance = np.cov(X.T, ddof=self.ddof)
self._set_covariance(covariance)
return self