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| In [[probability theory]] and [[statistics]], a '''copula''' is a multivariate probability distribution for which the [[marginal probability]] distribution of each variable is [[Uniform distribution|uniform]]. Copulas are used to describe the [[Dependent and independent variables|dependence]] between [[random variable]]s. They are named for their resemblance to grammatical [[Copula (linguistics)|copulas]] in [[linguistics]].
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| Sklar's Theorem states that any multivariate [[cumulative distribution function#Multivariate case| joint distribution]] can be written in terms of univariate [[marginal distribution]] functions and a copula which describes the dependence structure between the variables.
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| Copulas are popular in high dimensional statistical applications as they allow one to easily model and estimate the distribution of random vectors by estimating marginals and copulae separately. There are many parametric copula families available, which usually have parameters that control the strength of dependence. Some popular parametric copula models are outlined below. The formula was also adapted to Wall Street, where it took on a life of its own, used to estimate the probability distribution of losses on pools of loans or bonds. The users of the formula have been criticized for creating "evaluation cultures" that took the predictions of the formula as hard probabilities with which to make risk assessments.<ref>Donald MacKenzie and Taylor Spears, [http://www.sps.ed.ac.uk/__data/assets/pdf_file/0003/84243/Gaussian14.pdf ‘The Formula That Killed Wall Street’? The Gaussian Copula and the Material Cultures of Modelling], June 2012</ref>
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| ==Mathematical definition==
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| Consider a random vector <math>(X_1,X_2,\dots,X_d)</math>. Suppose its margins are continuous, i.e. the marginal [[Cumulative distribution function|CDFs]] <math>F_i(x) = \mathbb{P}[X_i\leq x] </math> are continuous functions. By applying the [[probability integral transform]] to each component, the random vector
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| :<math>(U_1,U_2,\dots,U_d)=\left(F_1(X_1),F_2(X_2),\dots,F_d(X_d)\right)</math>
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| has [[uniform distribution (continuous)|uniformly]] distributed marginals.
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| The copula of <math>(X_1,X_2,\dots,X_d)</math> is defined as the [[cumulative distribution function#Multivariate case|joint cumulative distribution function]] of <math>(U_1,U_2,\dots,U_d)</math>:
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| :<math>C(u_1,u_2,\dots,u_d)=\mathbb{P}[U_1\leq u_1,U_2\leq u_2,\dots,U_d\leq u_d] .</math>
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| The copula ''C'' contains all information on the dependence structure between the components of <math>(X_1,X_2,\dots,X_d)</math> whereas the marginal cumulative distribution functions <math>F_i</math> contain all information on the marginal distributions.
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| The importance of the above is that the reverse of these steps can be used to generate [[pseudo-random]] samples from general classes of [[multivariate probability distribution]]s. That is, given a procedure to generate a sample <math>(U_1,U_2,\dots,U_d)</math> from the copula distribution, the required sample can be constructed as
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| :<math>(X_1,X_2,\dots,X_d) = \left(F_1^{-1}(U_1),F_2^{-1}(U_2),\dots,F_d^{-1}(U_d)\right).</math>
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| The inverses <math>F_i^{-1}</math> are unproblematic as the <math>F_i</math> were assumed to be continuous. The above formula for the copula function can be rewritten to correspond to this as: | |
| :<math>C(u_1,u_2,\dots,u_d)=\mathbb{P}[X_1\leq F_1^{-1}(u_1),X_2\leq F_2^{-1}(u_2),\dots,X_d\leq F_d^{-1}(u_d)] .</math>
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| ==Definition==
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| In [[probability theory|probabilistic]] terms, <math>C:[0,1]^d\rightarrow [0,1]</math> is a ''d''-dimensional '''copula''' if ''C'' is a joint [[cumulative distribution function]] of a ''d''-dimensional random vector on the [[unit cube]] <math>[0,1]^d</math> with [[uniform distribution (continuous)|uniform]] [[marginal distribution|marginal]]s.<ref name="nelsen">{{Citation |first=Roger B. |last=Nelsen |year=1999 |title=An Introduction to Copulas |location=New York |publisher=Springer |isbn=0-387-98623-5 }}</ref>
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| In [[Multivariable calculus|analytic]] terms, <math>C:[0,1]^d\rightarrow [0,1]</math> is a ''d''-dimensional '''copula''' if
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| :* <math>C(u_1,\dots,u_{i-1},0,u_{i+1},\dots,u_d)=0 </math>, the copula is zero if one of the arguments is zero,
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| :* <math>C(1,\dots,1,u,1,\dots,1)=u </math>, the copula is equal to ''u'' if one argument is ''u'' and all others 1,
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| :* ''C'' is ''d''-increasing, i.e., for each [[hyperrectangle]] <math>B=\prod_{i=1}^{d}[x_i,y_i]\subseteq [0,1]^d </math> the ''C''-volume of ''B'' is non-negative:
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| :*:<math> \int_B dC(u) =\sum_{\mathbf z\in \times_{i=1}^{d}\{x_i,y_i\}} (-1)^{N(\mathbf z)} C(\mathbf z)\ge 0,</math>
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| ::where the <math>N(\mathbf z)=\#\{k : z_k=x_k\}</math>.
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| For instance, in the bivariate case, <math>C:[0,1]\times[0,1]\rightarrow [0,1]</math> is a bivariate copula if <math>C(0,u) = C(u,0) = 0 </math>, <math>C(1,u) = C(u,1) = u </math> and <math>C(u_2,v_2)-C(u_2,v_1)-C(u_1,v_2)+C(u_1,v_1) \geq 0 </math> for all <math>0 \leq u_1 \leq u_2 \leq 1</math> and <math>0 \leq v_1 \leq v_2 \leq 1</math>.
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| ==Sklar's theorem==
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| [[File:Gaussian copula gaussian marginals.png|thumb|Density and contour plot of a Bivariate Gaussian Distribution]]
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| [[File:Biv gumbel dist.png|thumb|Density and contour plot of two Normal marginals joint with a Gumbel copula]]
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| Sklar's theorem,<ref name="Sklar 1959">{{citation | last=Sklar | first=A. | authorlink = Abe Sklar | title={{lang|fr|Fonctions de répartition à n dimensions et leurs marges}} | journal=Publ. Inst. Statist. Univ. Paris | year=1959 | volume=8 | pages=229–231 | postscript=<!--none-->}}</ref> named after [[Abe Sklar]], provides the theoretical foundation for the application of copulas.
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| Sklar's theorem states that every [[cumulative distribution function#Multivariate case|multivariate cumulative distribution function]]
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| :<math>H(x_1,\dots,x_d)=\mathbb{P}[X_1\leq x_1,\dots,X_d\leq x_d]</math>
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| of a random vector <math>(X_1,X_2,\dots,X_d)</math> with marginals <math>F_i(x) = \mathbb{P}[X_i\leq x] </math> can be written as
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| :<math>H(x_1,\dots,x_d) = C\left(F_1(x_1),\dots,F_d(x_d) \right), </math>
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| where <math>C</math> is a copula.
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| The theorem also states that, given <math>H</math>, the copula is unique on <math> \operatorname{Ran}(F_1)\times\cdots\times \operatorname{Ran}(F_d) </math>, which is the [[cartesian product]] of the [[Range (mathematics)|ranges]] of the marginal cdf's. This implies that the copula is unique if the marginals <math>F_i</math> are continuous.
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| The converse is also true: given a copula <math>C:[0,1]^d\rightarrow [0,1] </math> and margins <math>F_i(x)</math> then <math>C\left(F_1(x_1),\dots,F_d(x_d) \right)</math> defines a ''d''-dimensional cumulative distribution function.
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| ==Fréchet–Hoeffding copula bounds==
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| [[File:copule ord.svg|thumb|right|Graphs of the bivariate Fréchet–Hoeffding copula limits and of the independence copula (in the middle).]]
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| The Fréchet–Hoeffding Theorem (after [[Maurice René Fréchet]] and [[Wassily Hoeffding]] <ref>{{cite web |url=http://www-history.mcs.st-andrews.ac.uk/Biographies/Hoeffding.html |title=Biography of Wassily Hoeffding |author="J J O'Connor and E F Robertson" |date= March 2011 |publisher= School of Mathematics and Statistics, University of St Andrews, Scotland |accessdate=8 November 2011}}</ref>) states that for any Copula <math>C:[0,1]^d\rightarrow [0,1]</math> and any <math>(u_1,\dots,u_d)\in[0,1]^d</math> the following bounds hold:
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| : <math>W(u_1,\dots,u_d) \leq C(u_1,\dots,u_d) \leq M(u_1,\dots,u_d).</math>
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| The function '''W''' is called lower Fréchet–Hoeffding bound and is defined as
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| :<math> W(u_1,\ldots,u_d) = \max\left\{1-d+\sum\limits_{i=1}^d {u_i} , 0 \right\}.</math>
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| The function '''M''' is called upper Fréchet–Hoeffding bound and is defined as
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| :<math> M(u_1,\ldots,u_d) = \min \{u_1,\dots,u_d\}.</math>
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| The upper bound is sharp: ''M'' is always a copula, it corresponds to [[comonotonicity|comonotone random variables]]. | |
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| The lower bound is point-wise sharp, in the sense that for fixed '''u''', there is a copula <math>\tilde{C}</math> such that <math>\tilde{C}(u) = W(u)</math>. However, ''W'' is a copula only in two dimensions, in which case it corresponds to countermonotonic random variables.
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| In two dimensions, i.e. the bivariate case, the Fréchet–Hoeffding Theorem states
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| : <math>\max(u+v-1,0) \leq C(u,v) \leq \min\{u,v\}</math>
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| ==Families of copulas==
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| Several families of copulae have been described.
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| ===Gaussian copula===
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| [[File:Copula gaussian.svg|thumb|Cumulative and density distribution of Gaussian copula with ''ρ'' = 0.4]]
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| The Gaussian copula is a distribution over the unit cube <math>[0,1]^d</math>. It is constructed from a [[multivariate normal distribution]] over <math>\mathbb{R}^d</math> by using the [[probability integral transform]].
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| For a given [[correlation matrix]] <math>R\in\mathbb{R}^{d\times d}</math>, the Gaussian copula with parameter matrix <math>R</math> can be written as
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| :<math> C_R^{\text{Gauss}}(u) = \Phi_R\left(\Phi^{-1}(u_1),\dots, \Phi^{-1}(u_d) \right), </math>
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| where <math>\Phi^{-1}</math> is the inverse cumulative distribution function of a standard normal and <math>\Phi_R</math> 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</math>.
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| The density can be written as<ref>{{cite journal |first=Philipp |last=Arbenz |year=2013 |title=Bayesian Copulae Distributions, with Application to Operational Risk Management—Some Comments |journal=Methodology and Computing in Applied Probability |volume=15 |issue=1 |pages=105–108 |doi=10.1007/s11009-011-9224-0 }}</ref>
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| :<math> c_R^{\text{Gauss}}(u)
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| = \frac{1}{\sqrt{\det{R}}}\exp\left(-\frac{1}{2}
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| \begin{pmatrix}\Phi^{-1}(u_1)\\ \vdots \\ \Phi^{-1}(u_d)\end{pmatrix}^T \cdot
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| \left(R^{-1}-\mathbf{I}\right) \cdot
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| \begin{pmatrix}\Phi^{-1}(u_1)\\ \vdots \\ \Phi^{-1}(u_d)\end{pmatrix}
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| \right), </math>
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| where <math>\mathbf{I}</math> is the identity matrix.
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| ===Archimedean copulas===
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| Archimedean copulas are an associative class of copulas. Most common Archimedean copulas admit an explicit formula, something not possible for instance for the Gaussian copula.
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| In practice, Archimedean copulas are popular because they allow modeling dependence in arbitrarily high dimensions with only one parameter, governing the strength of dependence.
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| A copula '''C''' is called Archimedean if it admits the representation<ref>{{cite book |last=Nelsen |first=R. B. |year=2006 |title=An Introduction to Copulas, Second Edition |publisher=Springer Science+Business Media Inc. |location=New York, NY 10013, USA |isbn=978-1-4419-2109-3 }}</ref>
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| :<math> C(u_1,\dots,u_d;\theta) = \psi^{[-1]}\left(\psi(u_1;\theta)+\cdots+\psi(u_d;\theta);\theta\right) \,</math>
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| where <math>\psi\!:[0,1]\times\Theta \rightarrow [0,\infty)</math> is a continuous, strictly decreasing and convex function such that <math>\psi(1;\theta)=0</math>. <math>\theta</math> is a parameter within some parameter space <math>\Theta</math>. <math>\psi</math> is the so-called generator function and <math>\psi^{[-1]}</math> is its pseudo-inverse defined by
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| :<math> \psi^{[-1]}(t;\theta) = \left\{\begin{array}{ll} \psi^{-1}(t;\theta) & \mbox{if }0 \leq t \leq \psi(0;\theta) \\ 0 & \mbox{if }\psi(0;\theta) \leq t \leq\infty. \end{array}\right. \,</math>
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| Moreover, the above formula for '''C''' yields a copula for <math>\psi^{-1}\,</math> if and only if <math>\psi^{-1}\,</math> is [[d-monotone function|d-monotone]] on <math>[0,\infty)</math>.<ref>{{cite journal |last=McNeil |first=A. J. |last2=Nešlehová |first2=J. |year=2009 |title=Multivariate Archimedean copulas, ''d''-monotone functions and <math>\mathit{l}</math>1-norm symmetric distributions |journal=[[Annals of Statistics]] |volume=37 |issue=5b |pages=3059–3097 |doi=10.1214/07-AOS556 }}</ref>
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| That is, if it is <math>d-2</math> times differentiable and the derivatives satisfy
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| :<math> (-1)^k\psi^{-1,(k)}(t;\theta) \geq 0 \,</math>
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| for all <math>t\geq 0</math> and <math>k=0,1,\dots,d-2</math> and <math>(-1)^{d-2}\psi^{-1,(d-2)}(t;\theta)</math> is nonincreasing and [[convex function|convex]].
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| The following table highlights the most prominent bivariate Archimedean copulas with their corresponding generator. Note that not all of them are [[completely monotone function|completely monotone]], i.e. ''d''-monotone for all <math>d\in\mathbb{N}</math> or ''d''-monotone for certain <math>\theta \in \Theta</math> only.
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| {| class="wikitable"
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| |+ Table with the most important generators<ref>{{cite book |last=Nelsen |first=R. B. |year=2006 |title=An Introduction to Copulas, Second Edition |publisher=Springer Science+Business Media Inc. |location=New York, NY 10013, USA |isbn=978-1-4419-2109-3 }}</ref>
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| |-
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| ! name !! bivariate copula <math>\,C_\theta(u,v)</math> !! generator <math>\,\psi_{\theta}(t)</math> !! generator inverse <math>\,\psi_{\theta}^{-1}(t)</math> !! parameter <math>\,\theta</math>
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| |-
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| | [[David Clayton|Clayton]]<ref name="Clayton1978">{{cite journal |authorlink=David Clayton |first=David G. |last=Clayton |year=1978 |title=A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence |journal=Biometrika |volume=65 |issue=1 |pages=141–151 |jstor=2335289 }}</ref> || <math>\left( \max\left\{ u^{-\theta} + v^{-\theta} -1 ; 0 \right\} \right)^{-1/\theta}</math> || <math>\frac{1}{\theta}\,(t^{-\theta}-1)\,</math> || <math>\left(1+\theta t\right)^{-1/\theta}</math> || <math>\theta\in[-1,\infty)\backslash\{0\}</math>
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| |-
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| | Ali-Mikhail-Haq || <math>\frac{uv}{1-\theta (1-u)(1-v)}</math> || <math>\log\!\left(\frac{1-\theta (1-t)}{t}\right)</math> || <math>\frac{1-\theta}{\exp(t)-\theta}</math> || <math>\theta\in[-1,1)</math>
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| |-
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| | [[Emil Julius Gumbel|Gumbel]] || <math>\exp\!\left( -\left( (-\log(u))^\theta + (-\log(v))^\theta \right)^{1/\theta} \right)</math>|| <math>\left(-\log(t)\right)^\theta</math> || <math>\exp\!\left(-t^{1/\theta}\right)</math> || <math>\theta\in[1,\infty)</math>
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| |-
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| | Frank || <math>-\frac{1}{\theta} \log\!\left( 1+\frac{(\exp(-\theta u)-1)(\exp(-\theta v)-1)}{\exp(-\theta)-1} \right)</math> || <math>-\log\!\left(\frac{\exp(-\theta t)-1}{\exp(-\theta)-1}\right)</math> || <math>-\frac{1}{\theta}\,\log(1+\exp(-t)(\exp(-\theta)-1))</math> || <math>\theta\in \mathbb{R}\backslash\{0\} </math>
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| |-
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| | Joe || <math>1-\left( (1-u)^\theta + (1-v)^\theta - (1-u)^\theta(1-v)^\theta \right)^{1/\theta}</math>|| <math>1-\left(1-\exp(-t)\right)^{1/\theta}</math> || <math>-\log\!\left(1-(1-t)^\theta\right)</math> || <math>\theta\in[1,\infty)</math>
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| |-
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| | [[statistical independence|Independence]] || <math>uv</math> || <math>\exp(-t)\,</math> || <math>-\log(t)\,</math> ||
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| |}
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| ==Empirical copulas==
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| When studying multivariate data, one might want to investigate the underlying copula. Suppose we have observations
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| :<math>(X_1^i,X_2^i,\dots,X_d^i), \, i=1,\dots,n</math> | |
| from a random vector <math>(X_1,X_2,\dots,X_d)</math> with continuous margins. The corresponding "true" copula observations would be
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| :<math>(U_1^i,U_2^i,\dots,U_d^i)=\left(F_1(X_1^i),F_2(X_2^i),\dots,F_d(X_d^i)\right), \, i=1,\dots,n.</math>
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| However, the marginal distribution functions <math>F_i</math> are usually not known. Therefore, one can construct pseudo copula observations by using the empirical distribution functions
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| :<math>F_k^n(x)=\frac{1}{n} \sum_{i=1}^n \mathbf{1}(X_k^i\leq x)</math>
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| instead. Then, the pseudo copula observations are defined as
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| :<math>(\tilde{U}_1^i,\tilde{U}_2^i,\dots,\tilde{U}_d^i)=\left(F_1^n(X_1^i),F_2^n(X_2^i),\dots,F_d^n(X_d^i)\right), \, i=1,\dots,n.</math>
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| The corresponding empirical copula is then defined as
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| :<math>C^n(u_1,\dots,u_d) = \frac{1}{n} \sum_{i=1}^n \mathbf{1}\left(\tilde{U}_1^i\leq u_1,\dots,\tilde{U}_d^i\leq u_d\right).</math>
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| The components of the pseudo copula samples can also be written as <math>\tilde{U}_k^i=R_k^i/n</math>, where <math>R_k^i</math> is the rank of the observation <math>X_k^i</math>:
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| :<math>R_k^i=\sum_{j=1}^n \mathbf{1}(X_k^j\leq X_k^i)</math>
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| Therefore, the empirical copula can be seen as the empirical distribution of the rank transformed data.
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| ==Monte Carlo integration for copula models==
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| In statistical applications, many problems can be formulated in the following way. One is interested in the expectation of a response function <math>g:\mathbb{R}^d\rightarrow\mathbb{R}</math> applied to some random vector <math>(X_1,\dots,X_d)</math>.<ref>Alexander J. McNeil, Rudiger Frey and Paul Embrechts (2005) "Quantitative Risk Management: Concepts, Techniques, and Tools", Princeton Series in Finance</ref> If we denote the cdf of this random vector with <math>H</math>, the quantity of interest can thus be written as
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| : <math> \mathbb{E} \left[ g(X_1,\dots,X_d) \right] = \int_{\mathbb{R}^d} g(x_1,\dots,x_d) \, dH(x_1,\dots,x_d).</math>
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| If <math>H</math> is given by a copula model, i.e.,
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| :<math>H(x_1,\dots,x_d)=C(F_1(x_1),\dots,F_d(x_d))</math> | |
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| this expectation can be rewritten as
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| :<math>\mathbb{E}\left[g(X_1,\dots,X_d)\right]=\int_{[0,1]^d}g(F_1^{-1}(u_1),\dots,F_d^{-1}(u_d)) \, dC(u_1,\dots,u_d).</math>
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| In case the copula '''C''' is [[absolutely continuous]], i.e. '''C''' has a density '''c''', this equation can be written as | |
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| :<math>\mathbb{E}\left[g(X_1,\dots,X_d)\right]=\int_{[0,1]^d}g(F_1^{-1}(u_1),\dots,F_d^{-1}(u_d))c(u_1,\dots,u_d) \, du_1\cdots du_d.</math>
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| If copula and margins are known (or if they have been estimated), this expectation can be approximated through the following Monte Carlo algorithm:
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| # Draw a sample <math>(U_1^k,\dots,U_d^k)\sim C\;\;(k=1,\dots,n)</math> of size '''n''' from the copula '''C'''
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| # By applying the inverse marginal cdf's, produce a sample of <math>(X_1,\dots,X_d)</math> by setting <math>(X_1^k,\dots,X_d^k)=(F_1^{-1}(U_1^k),\dots,F_d^{-1}(U_d^k))\sim H\;\;(k=1,\dots,n)</math>
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| # Approximate <math>\mathbb{E}\left[g(X_1,\dots,X_d)\right]</math> by its empirical value:
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| :::<math>\mathbb{E}\left[g(X_1,\dots,X_d)\right]\approx \frac{1}{n}\sum_{k=1}^n g(X_1^k,\dots,X_d^k)</math>
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| ==Applications==
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| ===Quantitative finance===
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| In risk/portfolio management, copulas are used to perform stress-tests and robustness checks that are especially important during “downside/crisis/panic regimes” where extreme downside events may occur (e.g., the global financial crisis of 2008–2009).
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| During a downside regime, a large number of investors who have held positions in riskier assets such as equities or real estate may seek refuge in ‘safer’ investments such as cash or bonds. This is also known as a [[flight-to-quality]] effect and investors tend to exit their positions in riskier assets in large numbers in a short period of time. As a result, during downside regimes, correlations across equities are greater on the downside as opposed to the upside and this may have disastrous effects on the economy.
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| <ref>{{Citation
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| | last1 = Longin |first1= F |last2= Solnik |first2= B| year = 2001
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| | title = Extreme correlation of international equity markets
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| | journal = Journal of Finance
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| | volume= 56
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| | issue= 2
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| | pages=649–676
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| | postscript = <!--none-->
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| }}</ref>
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| <ref>{{Citation
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| | last1 = Ang |first1= A |last2= Chen |first2= J
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| | year = 2002
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| | title = Asymmetric correlations of equity portfolios
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| | journal = Journal of Financial Economics
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| | volume=63
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| | issue=3
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| | pages=443–494
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| | postscript = <!--none-->
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| }}</ref> For example, anecdotally, we often read financial news headlines reporting the loss of hundreds of millions of dollars on the stock exchange in a single day; however, we rarely read reports of positive stock market gains of the same magnitude and in the same short time frame.
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| Copulas are useful in portfolio/risk management and help us analyse the effects of downside regimes by allowing the modelling of the marginals and dependence structure of a multivariate probability model separately. For example, consider the stock exchange as a market consisting of a large number of traders each operating with his/her own strategies to maximize profits. The individualistic behaviour of each trader can be described by modelling the marginals. However, as all traders operate on the same exchange, each traders’ actions have an interaction effect with other traders'. This interaction effect can be described by modelling the dependence structure. Therefore, copulas allow us to analyse the interaction effects which are of particular interest during downside regimes as investors tend to herd their trading behaviour and decisions.
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| Previously, scalable copula models for large dimensions only allowed the modelling of elliptical dependence structures (i.e., Gaussian and Student-t copulas) that do not allow for correlation asymmetries where correlations differ on the upside or downside regimes. However, the recent development of vine copulas (also known as pair copulas) enables the flexible modelling of the dependence structure for portfolios of large dimensions.
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| <ref>{{Citation
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| | last1 = Aas |first1= K |last2= Czado |first2= C|last3= Bakken|first3= H | year = 2009
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| | title = Pair-copula constructions of multiple dependence
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| | journal = Insurance: Mathematics and Economics
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| | volume=44
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| | issue=2
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| | pages=182–198
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| | postscript = <!--none-->
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| }}</ref>
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| The Clayton canonical vine copula allows for the occurrence of extreme downside events and has been successfully applied in portfolio choice and risk management applications. The model is able to reduce the effects of extreme downside correlations and produces improved statistical and economic performance compared to scalable elliptical dependence copulas such as the Gaussian and Student-t copula.
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| <ref>{{Citation
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| | last1 = Low |first1= R |last2= Alcock |first2= J|last3= Brailsford|first3= T |last4= Faff|first4= R
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| | year = 2013
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| | title = Canonical vine copulas in the context of modern portfolio management: Are they worth it?
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| | journal = Journal of Banking and Finance
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| | volume=37
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| | issue=8
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| | pages=3085–3099
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| | postscript = <!--none--> | doi=10.1016/j.jbankfin.2013.02.036
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| }}</ref> Other models developed for risk management applications are panic copulas that are glued with market estimates of the marginal distributions to analyze the effects of panic regimes on the portfolio profit and loss distribution. Panic copulas are created by Monte Carlo simulation, mixed with a re-weighting of the probability of each scenario.<ref>{{Citation
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| | last = Meucci
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| | first = Attilio
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| | year = 2011
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| | title = A New Breed of Copulas for Risk and Portfolio Management
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| | journal = Risk
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| | volume=24
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| | issue=9
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| | pages=122–126
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| | url = http://symmys.com/node/335
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| | postscript = <!--none-->
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| }}</ref>
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| As far as derivatives pricing is concerned, dependence modelling with copula functions is widely used in applications of financial risk assessment and actuarial analysis – for example in the pricing of [[collateralized debt obligation]]s (CDOs).<ref>{{Citation |last1= Meneguzzo |first1=David |authorlink= |first2=Walter|last2= Vecchiato |year=2003 |month=Nov |title=Copula sensitivity in collateralized debt obligations and basket default swaps |journal=[[Journal of Futures Markets]] |volume=24 |issue=1 |pages=37–70 |id= |doi=10.1002/fut.10110 |accessdate=2008-10-29 |quote= |postscript= <!--none--> }}</ref> Some believe the methodology of applying the Gaussian copula to [[credit derivative]]s to be one of the reasons behind the [[global financial crisis of 2008–2009]].<ref>[http://www.wired.com/techbiz/it/magazine/17-03/wp_quant?currentPage=all Recipe for Disaster: The Formula That Killed Wall Street] ''Wired'', 2/23/2009</ref><ref>{{Citation
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| | last = MacKenzie
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| | first = Donald
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| | publication-date = 2008-05-08
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| | year = 2008
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| | title = End-of-the-World Trade
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| | periodical = London Review of Books
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| | url = http://www.lrb.co.uk/v30/n09/mack01_.html
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| | accessdate = 2009-07-27
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| | postscript = <!--none-->
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| }}</ref>
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| Despite this perception, there are documented attempts of the financial industry, occurring before the crisis, to address the limitations of the Gaussian copula and of copula functions more generally, specifically the lack of dependence dynamics{{clarify|reason=what is dependence dynamics|date=September 2011}} and the poor representation of extreme events.<ref name="Lipton">{{cite book| first1=Alexander|last1= Lipton |first2= Andrew |last2=Rennie |title=Credit Correlation: Life After Copulas|publisher= World Scientific|ISBN= 978-981-270-949-3}}</ref> There have been attempts to propose models rectifying some of the copula limitations.<ref name="Lipton"/><ref>{{Cite journal
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| | last1 = Donnelly|first1= C|last2= Embrechts|first2= P,
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| | year = 2010
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| | title = The devil is in the tails: actuarial mathematics and the subprime mortgage crisis
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| | publisher = ASTIN Bulletin 40(1), 1–33
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| | postscript = <!--none-->
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| }}</ref><ref>{{Cite book
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| | last1 = Brigo |first1=D |last2= Pallavicini |first2= A|last3= Torresetti |first3= R
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| | year = 2010
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| | title = Credit Models and the Crisis: A Journey into CDOs, Copulas, Correlations and dynamic Models
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| | publisher = Wiley and Sons
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| | postscript = <!--none-->
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| }}</ref>
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| While the application of copulas in credit has gone through popularity as well as misfortune during the global financial crisis of 2008–2009,<ref name="ft">{{Citation |title=The formula that felled Wall St |url=http://www.ft.com/cms/s/2/912d85e8-2d75-11de-9eba-00144feabdc0.html |first=Sam |last=Jones |newspaper=[[Financial Times]] |date=April 24, 2009 |postscript=<!--none--> }}</ref> it is arguably an industry standard model for pricing CDOs. Copulas have also been applied to other asset classes as a flexible tool in analyzing multi-asset derivative products. The first such application outside credit was to use a copula to construct an implied basket volatility surface,<ref>{{Cite journal
| |
| | last = Qu, Dong,
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| | year = 2001
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| | title = Basket Implied Volatility Surface
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| | journal= Derivatives Week,|issue=4 June.
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| | postscript = <!--none-->
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| }}</ref> taking into account the [[volatility smile]] of basket components. Copulas have since gained popularity in pricing and risk management
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| <ref>{{Cite journal
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| | last = Qu, Dong,
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| | year = 2005
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| | title = Pricing Basket Options With Skew
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| | journal = Wilmott Magazine |issue= July.
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| | postscript = <!--none-->
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| }}</ref> of
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| options on multi-assets in the presence of volatility smile/skew, in equity, foreign exchange and fixed income derivative business. Some typical example applications of copulas are listed below:
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| *Analyzing and pricing volatility smile/skew of exotic baskets, e.g. best/worst of;
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| *Analyzing and pricing volatility smile/skew of less liquid FX{{clarify|reason=what is FX|date=September 2011}} cross, which is effectively a basket: ''C'' = ''S''<sub>1</sub>/''S''<sub>2</sub> or ''C'' = ''S''<sub>1</sub>·''S''<sub>2</sub>;
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| *Analyzing and pricing spread options, in particular in fixed income [[constant maturity swap]] spread options.
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| ===Civil engineering===
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| Recently, copula functions have been successfully applied to the database formulation for the [[Reliability (statistics)|reliability]] analysis of highway bridges, and to various multivariate [[simulation]] studies in civil,<ref>{{Citation
| |
| | last1 = Thompson| first1 = David
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| | last2 = Kilgore| first2 = Roger
| |
| | publication-date = 2011| year = 2011
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| | title = Estimating Joint Flow Probabilities at Stream Confluences using Copulas
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| | journal = Transportation Research Record
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| | volume=2262
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| | pages=200–206
| |
| | url = http://trb.metapress.com/content/m3146tg612k80771/?p=d6b0d7200af148b8a4e18e592ca1e269&pi=3
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| | accessdate = 2012-02-21
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| | postscript = <!--none-->
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| }}</ref> mechanical and offshore engineering.{{Citation needed|date=October 2008}}
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| ===Reliability engineering===
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| Copulas are being used for [[Reliability (statistics)|reliability]] analysis of complex systems of machine components with competing failure modes.{{Citation needed|date=December 2013}}
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| ===Medicine===
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| Copula functions have been successfully applied to the analysis of neuronal dependencies
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| <ref>{{Citation
| |
| | year = 2013
| |
| | last1=Eban
| |
| | first1 = E
| |
| | last2 = Rothschild
| |
| | first2 = R
| |
| | last3 = Mizrahi
| |
| | first3 = A
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| | last4 = Nelken
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| | first4 = I
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| | last4 = Elidan
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| | first4 = G
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| | editor1-last = Carvalho
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| | editor1-first = C
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| | editor2-last = Ravikumar
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| | editor2-first = P
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| | title = Dynamic Copula Networks for Modeling Real-valued Time Series
| |
| | volume = 31
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| | journal = Journal of Machine Learning Research
| |
| | url = http://jmlr.org/proceedings/papers/v31/eban13a.pdf
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| | postscript = <!--none-->
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| }}</ref> and spike counts in neuroscience
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| <ref>{{Citation
| |
| | doi = 10.1371/journal.pcbi.1000577
| |
| | year = 2009
| |
| | last1=Onken
| |
| | first1 = A
| |
| | last2 = Grünewälder
| |
| | first2 = S
| |
| | last3 = Munk
| |
| | first3 = MH
| |
| | last4 = Obermayer
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| | first4 = K
| |
| | editor1-last = Aertsen
| |
| | editor1-first = Ad
| |
| | title = Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation
| |
| | pmid = 19956759
| |
| | pages = e1000577
| |
| | issue = 11
| |
| | volume = 5
| |
| | journal = PLoS Computational Biology
| |
| | pmc = 2776173
| |
| | url = http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000577
| |
| | postscript = <!--none-->
| |
| }}</ref>
| |
| .
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| ===Weather research===
| |
| | |
| Copulas have been extensively used in climate and weather related research.<ref>{{cite doi|10.5194/npg-15-761-2008
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| }}</ref>
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| | |
| ===Random vector generation===
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| Large synthetic traces of vectors and stationary time series can be generated using empirical copula while preserving the entire dependence structure of small datasets.<ref name="pw;Copula">{{cite journal|last=Strelen|first=Johann Christoph|title=T ools for Dependent Simulation Input with Copulas|journal=SIMUTools|year=2009|doi=10.1145/1537614.1537654}}</ref> Such empirical traces are useful in various simulation-based performance studies.<ref name="ResQue">{{cite journal|last=Bandara|first=H. M. N. D.|coauthors=A. P. Jayasumana|title=On Characteristics and Modeling of P2P Resources with Correlated Static and Dynamic Attributes|journal=IEEE GLOBECOM|year=2011|month=Dec.|pages=1–6|url=http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6134288}}</ref>
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| ==References==
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| {{Reflist|30em}}
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| | |
| ==Further reading==
| |
| * The standard reference for an introduction to copulas. Covers all fundamental aspects, summarizes the most popular copula classes, and provides proofs for the important theorems related to copulas
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| ::Roger B. Nelsen (1999), "An Introduction to Copulas", Springer. ISBN 978-0-387-98623-4
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| * A book covering current topics in mathematical research on copulas:
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| ::Piotr Jaworski, Fabrizio Durante, Wolfgang Karl Härdle, Tomasz Rychlik (Editors): (2010): "Copula Theory and Its Applications" Lecture Notes in Statistics, Springer. ISBN 978-3-642-12464-8
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| * A reference for sampling applications and stochastic models related to copulas is
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| ::Jan-Frederik Mai, Matthias Scherer (2012): ''Simulating Copulas (Stochastic Models, Sampling Algorithms and Applications).'' World Scientific. ISBN 978-1-84816-874-9
| |
| * A paper covering the historic development of copula theory, by the person associated with the "invention" of copulas, [[Abe Sklar]].
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| ::Abe Sklar (1997): "Random variables, distribution functions, and copulas – a personal look backward and forward" in Rüschendorf, L., Schweizer, B. und Taylor, M. (eds) ''Distributions With Fixed Marginals & Related Topics'' (Lecture Notes – Monograph Series Number 28). ISBN 978-0-940600-40-9
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| * The standard reference for multivariate models and copula theory in the context of financial and insurance models
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| ::Alexander J. McNeil, Rudiger Frey and Paul Embrechts (2005) "Quantitative Risk Management: Concepts, Techniques, and Tools", Princeton Series in Finance. ISBN 978-0-691-12255-7
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| ==External links==
| |
| * {{springer|title=Copula|id=p/c110410}}
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| * [http://sites.google.com/site/copulawiki/ Copula Wiki: community portal for researchers with interest in copulas]
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| * [http://www.mathfinance.cn/tags/copula A collection of Copula simulation and estimation codes]
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| * [http://www.math.uni-leipzig.de/~tschmidt/TSchmidt_Copulas.pdf Thorsten Schmidt (2006) "Coping with Copulas"]
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| * [http://www.crystalballservices.com/Resources/ConsultantsCornerBlog/tagid/21/Correlation.aspx Copulas & Correlation using Excel Simulation Articles]
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| * [http://www.worldscientific.com/doi/suppl/10.1142/p842/suppl_file/p842_chap01.pdf Chapter 1 of Jan-Frederik Mai, Matthias Scherer (2012) "Simulating Copulas: Stochastic Models, Sampling Algorithms, and Applications"]
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| {{Statistics|analysis}}
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| {{DEFAULTSORT:Copula (Statistics)}}
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| [[Category:Actuarial science]]
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| [[Category:Multivariate statistics]]
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| <!--redirect from Sklar's theorem also indexed here-->
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| [[Category:Statistical dependence]]
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| [[Category:Systems of probability distributions]]
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