pycop 0.0.13

Copula for multivariate dependence modeling

Overview

Pycop is the most complete tool for modeling multivariate dependence with Python. The package provides methods such as estimation, random sample generation, and graphical representation for commonly used copula functions. The package supports the use of mixture models defined as convex combinations of copulas. Other methods based on the empirical copula such as the non-parametric Tail Dependence Coefficient are given.

Some of the features covered:

• Elliptical copulas (Gaussian & Student) and common Archimedean Copulas functions
• Mixture model of multiple copula functions (up to 3 copula functions)
• Multivariate random sample generation
• Empirical copula method
• Parametric and Non-parametric Tail Dependence Coefficient (TDC)

Available copula function

Copula	Bivariate Graph & Estimation	Multivariate Simulation
Mixture	✓	✓
Gaussian	✓	✓
Student	✓	✓
Clayton	✓	✓
Rotated Clayton	✓	✓
Gumbel	✓	✓
Rotated Gumbel	✓	✓
Frank	✓	✓
Joe	✓	✓
Rotated Joe	✓	✓
Galambos	✓	✗
Rotated Galambos	✓	✗
BB1	✓	✗
BB2	✓	✗
FGM	✓	✗
Plackett	✓	✗
AMH	✗	✓

Usage

Install pycop using pip

pip install pycop

Examples

Estimations on msci returns

Graphical Representations

Simulations

Table of Contents

• Graphical Representation
  • 3d plot
  • Contour plot
  • Mixture plot
• Simulation
  • Gaussian
  • Student
  • Archimedean
  • High dimension
  • Mixture simulation
• Estimation
  • Canonical Maximum Likelihood Estimation
• Tail Dependence Coefficient
  • Theoretical TDC
  • Non-parametric TDC
  • Optimal Empirical TDC

Graphical Representation

We first create a copula object by specifying the copula familly

from pycop import archimedean
cop = archimedean(family="clayton")

Plot the cdf and pdf of the copula.

3d plot

cop = archimedean(family="gumbel")

cop.plot_cdf([2], plot_type="3d", Nsplit=100 )
cop.plot_pdf([2], plot_type="3d", Nsplit=100, cmap="cividis" )

Contour plot

plot the contour

cop = archimedean(family="plackett")

cop.plot_cdf([2], plot_type="contour", Nsplit=100 )
cop.plot_pdf([2], plot_type="contour", Nsplit=100, )

It is also possible to add specific marginals

cop = archimedean.archimedean(family="clayton")

from scipy.stats import norm


marginals = [
    {
        "distribution": norm, "loc" : 0, "scale" : 0.8,
    },
    {
        "distribution": norm, "loc" : 0, "scale": 0.6,
    }]

cop.plot_mpdf([2], marginals, plot_type="3d",Nsplit=100,
            rstride=1, cstride=1,
            antialiased=True,
            cmap="cividis",
            edgecolor='black',
            linewidth=0.1,
            zorder=1,
            alpha=1)

lvls = [0.02, 0.05, 0.1, 0.2, 0.3]

cop.plot_mpdf([2], marginals, plot_type="contour", Nsplit=100,  levels=lvls)

Mixture plot

mixture of 2 copulas

from pycop import mixture

cop = mixture(["clayton", "gumbel"])
cop.plot_pdf([0.2, 2, 2],  plot_type="contour", Nsplit=40,  levels=[0.1,0.4,0.8,1.3,1.6] )
# plot with defined marginals
cop.plot_mpdf([0.2, 2, 2], marginals, plot_type="contour", Nsplit=50)

cop = mixture(["clayton", "gaussian", "gumbel"])
cop.plot_pdf([1/3, 1/3, 1/3, 2, 0.5, 4],  plot_type="contour", Nsplit=40,  levels=[0.1,0.4,0.8,1.3,1.6] )
cop.plot_mpdf([1/3, 1/3, 1/3, 2, 0.5, 2], marginals, plot_type="contour", Nsplit=50)

Simulation

Gaussian

from scipy.stats import norm
from pycop import simulation

n = 2 # dimension
m = 1000 # sample size

corrMatrix = np.array([[1, 0.8], [0.8, 1]])
u1, u2 = simulation.simu_gaussian(n, m, corrMatrix)

Adding gaussian marginals, (using distribution.ppf from scipy.statsto transform uniform margin to the desired distribution)

u1 = norm.ppf(u1)
u2 = norm.ppf(u2)

Student

u1, u2 = simulation.simu_tstudent(n, m, corrMatrix, nu=1)

Archimedean

List of archimedean cop available

u1, u2 = simulation.simu_archimedean("gumbel", n, m, theta=2)
u1, u2 = 1 - u1, 1 - u2

Rotated

u1, u2 = 1 - u1, 1 - u2

High dimension

n = 3       # Dimension
m = 1000    # Sample size

corrMatrix = np.array([[1, 0.9, 0], [0.9, 1, 0], [0, 0, 1]])
u = simulation.simu_gaussian(n, m, corrMatrix)
u = norm.ppf(u)

u = simulation.simu_archimedean("clayton", n, m, theta=2)
u = norm.ppf(u)

Mixture simulation

Simulation from a mixture of 2 copulas

n = 3
m = 2000

combination = [
    {"type": "clayton", "weight": 1/3, "theta": 2},
    {"type": "gumbel", "weight": 1/3, "theta": 3}
]

u = simulation.simu_mixture(n, m, combination)
u = norm.ppf(u)

Simulation from a mixture of 3 copulas

corrMatrix = np.array([[1, 0.8, 0], [0.8, 1, 0], [0, 0, 1]])


combination = [
    {"type": "clayton", "weight": 1/3, "theta": 2},
    {"type": "student", "weight": 1/3, "corrMatrix": corrMatrix, "nu":2},
    {"type": "gumbel", "weight": 1/3, "theta":3}
]

u = simulation.simu_mixture(n, m, combination)
u = norm.ppf(u)

Estimation

Estimation available : CMLE

Canonical Maximum Likelihood Estimation (CMLE)

Import a sample with pandas

import pandas as pd
import numpy as np

df = pd.read_csv("data/msci.csv")
df.index = pd.to_datetime(df["Date"], format="%m/%d/%Y")
df = df.drop(["Date"], axis=1)

for col in df.columns.values:
    df[col] = np.log(df[col]) - np.log(df[col].shift(1))

df = df.dropna()

from pycop import estimation, archimedean

cop = archimedean("clayton")
data = df[["US","UK"]].T.values
param, cmle = estimation.fit_cmle(cop, data)

clayton estim: 0.8025977727691012

Tail Dependence coefficient

Theoretical TDC

from pycop import archimedean

cop = archimedean("clayton")

cop.LTDC(theta=0.5)
cop.UTDC(theta=0.5)

For a mixture copula, the copula with lower tail dependence comes first, and the one with upper tail dependence is last.

from pycop import mixture

cop = mixture(["clayton", "gaussian", "gumbel"])

LTDC = cop.LTDC(weight = 0.2, theta = 0.5) 
UTDC = cop.UTDC(weight = 0.2, theta = 1.5) 

Non-parametric TDC

Create an empirical copula object

from pycop import empirical

cop = empirical(df[["US","UK"]].T.values)

Compute the non-parametric Upper TDC (UTDC) or the Lower TDC (LTDC) for a given threshold:

cop.LTDC(0.01) # i/n = 1%
cop.UTDC(0.99) # i/n = 99%

Optimal Empirical TDC

Returns the optimal non-parametric TDC based on the heuristic plateau-finding algorithm from Frahm et al (2005) "Estimating the tail-dependence coefficient: properties and pitfalls"

cop.optimal_tdc("upper")
cop.optimal_tdc("lower")