# -*- coding: utf-8 -*-
"""
Created on Fri Jan 29 19:19:45 2021
Author: Josef Perktold
Author: Pamphile Roy
License: BSD-3
"""
import numpy as np
from scipy import stats
# scipy compat:
from statsmodels.compat.scipy import multivariate_t
from statsmodels.distributions.copula.copulas import Copula
class EllipticalCopula(Copula):
"""Base class for elliptical copula
This class requires subclassing and currently does not have generic
methods based on an elliptical generator.
Notes
-----
Elliptical copulas require that copula parameters are set when the
instance is created. Those parameters currently cannot be provided in the
call to methods. (This will most likely change in future versions.)
If non-empty ``args`` are provided in methods, then a ValueError is raised.
The ``args`` keyword is provided for a consistent interface across
copulas.
"""
def _handle_args(self, args):
if args != () and args is not None:
msg = ("Methods in elliptical copulas use copula parameters in"
" attributes. `arg` in the method is ignored")
raise ValueError(msg)
else:
return args
def rvs(self, nobs=1, args=(), random_state=None):
self._handle_args(args)
x = self.distr_mv.rvs(size=nobs, random_state=random_state)
return self.distr_uv.cdf(x)
def pdf(self, u, args=()):
self._handle_args(args)
ppf = self.distr_uv.ppf(u)
mv_pdf_ppf = self.distr_mv.pdf(ppf)
return mv_pdf_ppf / np.prod(self.distr_uv.pdf(ppf), axis=-1)
def cdf(self, u, args=()):
self._handle_args(args)
ppf = self.distr_uv.ppf(u)
return self.distr_mv.cdf(ppf)
def tau(self, corr=None):
"""Bivariate kendall's tau based on correlation coefficient.
Parameters
----------
corr : None or float
Pearson correlation. If corr is None, then the correlation will be
taken from the copula attribute.
Returns
-------
Kendall's tau that corresponds to pearson correlation in the
elliptical copula.
"""
if corr is None:
corr = self.corr
if corr.shape == (2, 2):
corr = corr[0, 1]
rho = 2 * np.arcsin(corr) / np.pi
return rho
def corr_from_tau(self, tau):
"""Pearson correlation from kendall's tau.
Parameters
----------
tau : array_like
Kendall's tau correlation coefficient.
Returns
-------
Pearson correlation coefficient for given tau in elliptical
copula. This can be used as parameter for an elliptical copula.
"""
corr = np.sin(tau * np.pi / 2)
return corr
[docs]class GaussianCopula(EllipticalCopula):
r"""Gaussian copula.
It is constructed from a multivariate normal distribution over
:math:`\mathbb{R}^d` by using the probability integral transform.
For a given correlation matrix :math:`R \in[-1, 1]^{d \times d}`,
the Gaussian copula with parameter matrix :math:`R` can be written
as:
.. math::
C_R^{\text{Gauss}}(u) = \Phi_R\left(\Phi^{-1}(u_1),\dots,
\Phi^{-1}(u_d) \right),
where :math:`\Phi^{-1}` is the inverse cumulative distribution function
of a standard normal and :math:`\Phi_R` is the joint cumulative
distribution function of a multivariate normal distribution with mean
vector zero and covariance matrix equal to the correlation
matrix :math:`R`.
Parameters
----------
corr : scalar or array_like
Correlation or scatter matrix for the elliptical copula. In the
bivariate case, ``corr` can be a scalar and is then considered as
the correlation coefficient. If ``corr`` is None, then the scatter
matrix is the identity matrix.
k_dim : int
Dimension, number of components in the multivariate random variable.
Notes
-----
Elliptical copulas require that copula parameters are set when the
instance is created. Those parameters currently cannot be provided in the
call to methods. (This will most likely change in future versions.)
If non-empty ``args`` are provided in methods, then a ValueError is raised.
The ``args`` keyword is provided for a consistent interface across
copulas.
References
----------
.. [1] Joe, Harry, 2014, Dependence modeling with copulas. CRC press.
p. 163
"""
def __init__(self, corr=None, k_dim=2):
super().__init__(k_dim=k_dim)
if corr is None:
corr = np.eye(k_dim)
elif k_dim == 2 and np.size(corr) == 1:
corr = np.array([[1., corr], [corr, 1.]])
self.corr = np.asarray(corr)
self.distr_uv = stats.norm
self.distr_mv = stats.multivariate_normal(cov=corr)
[docs] def dependence_tail(self, corr=None):
"""
Bivariate tail dependence parameter.
Joe (2014) p. 182
Parameters
----------
corr : any
Tail dependence for Gaussian copulas is always zero.
Argument will be ignored
Returns
-------
Lower and upper tail dependence coefficients of the copula with given
Pearson correlation coefficient.
"""
return 0, 0
def _arg_from_tau(self, tau):
# for generic compat
return self.corr_from_tau(tau)
[docs]class StudentTCopula(EllipticalCopula):
"""Student t copula.
Parameters
----------
corr : scalar or array_like
Correlation or scatter matrix for the elliptical copula. In the
bivariate case, ``corr` can be a scalar and is then considered as
the correlation coefficient. If ``corr`` is None, then the scatter
matrix is the identity matrix.
df : float (optional)
Degrees of freedom of the multivariate t distribution.
k_dim : int
Dimension, number of components in the multivariate random variable.
Notes
-----
Elliptical copulas require that copula parameters are set when the
instance is created. Those parameters currently cannot be provided in the
call to methods. (This will most likely change in future versions.)
If non-empty ``args`` are provided in methods, then a ValueError is raised.
The ``args`` keyword is provided for a consistent interface across
copulas.
References
----------
.. [1] Joe, Harry, 2014, Dependence modeling with copulas. CRC press.
p. 181
"""
def __init__(self, corr=None, df=None, k_dim=2):
super().__init__(k_dim=k_dim)
if corr is None:
corr = np.eye(k_dim)
elif k_dim == 2 and np.size(corr) == 1:
corr = np.array([[1., corr], [corr, 1.]])
self.df = df
self.corr = np.asarray(corr)
# both uv and mv are frozen distributions
self.distr_uv = stats.t(df=df)
self.distr_mv = multivariate_t(shape=corr, df=df)
[docs] def cdf(self, u, args=()):
raise NotImplementedError("CDF not available in closed form.")
# ppf = self.distr_uv.ppf(u)
# mvt = MVT([0, 0], self.corr, self.df)
# return mvt.cdf(ppf)
[docs] def spearmans_rho(self, corr=None):
"""
Bivariate Spearman's rho based on correlation coefficient.
Joe (2014) p. 182
Parameters
----------
corr : None or float
Pearson correlation. If corr is None, then the correlation will be
taken from the copula attribute.
Returns
-------
Spearman's rho that corresponds to pearson correlation in the
elliptical copula.
"""
if corr is None:
corr = self.corr
if corr.shape == (2, 2):
corr = corr[0, 1]
tau = 6 * np.arcsin(corr / 2) / np.pi
return tau
[docs] def dependence_tail(self, corr=None):
"""
Bivariate tail dependence parameter.
Joe (2014) p. 182
Parameters
----------
corr : None or float
Pearson correlation. If corr is None, then the correlation will be
taken from the copula attribute.
Returns
-------
Lower and upper tail dependence coefficients of the copula with given
Pearson correlation coefficient.
"""
if corr is None:
corr = self.corr
if corr.shape == (2, 2):
corr = corr[0, 1]
df = self.df
t = - np.sqrt((df + 1) * (1 - corr) / 1 + corr)
# Note self.distr_uv is frozen, df cannot change, use stats.t instead
lam = 2 * stats.t.cdf(t, df + 1)
return lam, lam
def _arg_from_tau(self, tau):
# for generic compat
# this does not provide an estimate of df
return self.corr_from_tau(tau)