# Cópula (teoria da probabilidade)

Cópula (teoria da probabilidade) (Redirected from Sklar's theorem) Jump to navigation Jump to search This article is about probability theory. Para outros usos, see Copula (desambiguação).

In probability theory and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. Copulas are used to describe/model the dependence (inter-correlation) between random variables.[1] Their name, introduced by applied mathematician Abe Sklar in 1959, comes from the Latin for "link" ou "tie", similar but unrelated to grammatical copulas in linguistics. Copulas have been used widely in quantitative finance to model and minimize tail risk[2] and portfolio-optimization applications.[3] Sklar's theorem states that any multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula which describes the dependence structure between the variables.

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.

Two-dimensional copulas are known in some other areas of mathematics under the name permutons and doubly-stochastic measures.

Conteúdo 1 Mathematical definition 2 Definição 3 Sklar's theorem 4 Stationarity condition 5 Fréchet–Hoeffding copula bounds 6 Families of copulas 6.1 Gaussian copula 6.2 Archimedean copulas 6.2.1 Most important Archimedean copulas 7 Expectation for copula models and Monte Carlo integration 8 Empirical copulas 9 Formulários 9.1 Quantitative finance 9.2 Civil engineering 9.3 Reliability engineering 9.4 Warranty data analysis 9.5 Turbulent combustion 9.6 Medicine 9.7 Geodesy 9.8 Hydrology research 9.9 Climate and weather research 9.10 Solar irradiance variability 9.11 Random vector generation 9.12 Ranking of electrical motors 9.13 Signal processing 10 Mathematical derivation of copula density function 10.1 List of copula density functions and applications 11 Veja também 12 Referências 13 Leitura adicional 14 External links Mathematical definition Consider a random vector {estilo de exibição (X_{1},X_{2},pontos ,X_{d})} . Suppose its marginals are continuous, ou seja. the marginal CDFs {estilo de exibição F_{eu}(x)= Pr[X_{eu}leq x]} are continuous functions. By applying the probability integral transform to each component, the random vector {estilo de exibição (VOCÊ_{1},VOCÊ_{2},pontos ,VOCÊ_{d})= esquerda(F_{1}(X_{1}),F_{2}(X_{2}),pontos ,F_{d}(X_{d})certo)} has marginals that are uniformly distributed on the interval [0, 1].

The copula of {estilo de exibição (X_{1},X_{2},pontos ,X_{d})} is defined as the joint cumulative distribution function of {estilo de exibição (VOCÊ_{1},VOCÊ_{2},pontos ,VOCÊ_{d})} : {estilo de exibição C(você_{1},você_{2},pontos ,você_{d})= Pr[VOCÊ_{1}leq u_{1},VOCÊ_{2}leq u_{2},pontos ,VOCÊ_{d}leq u_{d}].} The copula C contains all information on the dependence structure between the components of {estilo de exibição (X_{1},X_{2},pontos ,X_{d})} whereas the marginal cumulative distribution functions {estilo de exibição F_{eu}} contain all information on the marginal distributions of {estilo de exibição X_{eu}} .

The reverse of these steps can be used to generate pseudo-random samples from general classes of multivariate probability distributions. Aquilo é, given a procedure to generate a sample {estilo de exibição (VOCÊ_{1},VOCÊ_{2},pontos ,VOCÊ_{d})} from the copula function, the required sample can be constructed as {estilo de exibição (X_{1},X_{2},pontos ,X_{d})= esquerda(F_{1}^{-1}(VOCÊ_{1}),F_{2}^{-1}(VOCÊ_{2}),pontos ,F_{d}^{-1}(VOCÊ_{d})certo).} The inverses {estilo de exibição F_{eu}^{-1}} are unproblematic almost surely, since the {estilo de exibição F_{eu}} were assumed to be continuous. Além disso, the above formula for the copula function can be rewritten as: {estilo de exibição C(você_{1},você_{2},pontos ,você_{d})= Pr[X_{1}leq F_{1}^{-1}(você_{1}),X_{2}leq F_{2}^{-1}(você_{2}),pontos ,X_{d}leq F_{d}^{-1}(você_{d})].} Definition In probabilistic terms, {estilo de exibição C:[0,1]^{d}rightarrow [0,1]} is a d-dimensional copula if C is a joint cumulative distribution function of a d-dimensional random vector on the unit cube {estilo de exibição [0,1]^{d}} with uniform marginals.[4] In analytic terms, {estilo de exibição C:[0,1]^{d}rightarrow [0,1]} is a d-dimensional copula if {estilo de exibição C(você_{1},pontos ,você_{i-1},0,você_{i+1},pontos ,você_{d})=0} , the copula is zero if any one of the arguments is zero, {estilo de exibição C(1,pontos ,1,você,1,pontos ,1)=u} , the copula is equal to u if one argument is u and all others 1, C is d-non-decreasing, ou seja, for each hyperrectangle {displaystyle B=prod _{i=1}^{d}[x_{eu},s_{eu}]subseteq [0,1]^{d}} the C-volume of B is non-negative: {estilo de exibição int _{B}matemática {d} C(você)=soma _{mathbf {z} in prod _{i=1}^{d}{x_{eu},s_{eu}}}(-1)^{N(mathbf {z} )}C(mathbf {z} )geq 0,} onde o {estilo de exibição N(mathbf {z} )=#{k:z_{k}=x_{k}}} .

Por exemplo, in the bivariate case, {estilo de exibição C:[0,1]vezes [0,1]rightarrow [0,1]} is a bivariate copula if {estilo de exibição C(0,você)=C(você,0)=0} , {estilo de exibição C(1,você)=C(você,1)=u} e {estilo de exibição C(você_{2},v_{2})-C(você_{2},v_{1})-C(você_{1},v_{2})+C(você_{1},v_{1})geq 0} para todos {displaystyle 0leq u_{1}leq u_{2}leq 1} e {displaystyle 0leq v_{1}leq v_{2}leq 1} .

Sklar's theorem Density and contour plot of a Bivariate Gaussian Distribution Density and contour plot of two Normal marginals joint with a Gumbel copula Sklar's theorem, named after Abe Sklar, provides the theoretical foundation for the application of copulas.[5][6] Sklar's theorem states that every multivariate cumulative distribution function {estilo de exibição H(x_{1},pontos ,x_{d})= Pr[X_{1}leq x_{1},pontos ,X_{d}leq x_{d}]} of a random vector {estilo de exibição (X_{1},X_{2},pontos ,X_{d})} can be expressed in terms of its marginals {estilo de exibição F_{eu}(x_{eu})= Pr[X_{eu}leq x_{eu}]} and a copula {estilo de exibição C} . De fato: {estilo de exibição H(x_{1},pontos ,x_{d})=Cleft(F_{1}(x_{1}),pontos ,F_{d}(x_{d})certo).} In case that the multivariate distribution has a density {estilo de exibição h} , and if this is available, it holds further that {estilo de exibição h(x_{1},pontos ,x_{d})=c(F_{1}(x_{1}),pontos ,F_{d}(x_{d}))cdot f_{1}(x_{1})cdot dots cdot f_{d}(x_{d}),} Onde {estilo de exibição c} is the density of the copula.

The theorem also states that, given {estilo de exibição H} , the copula is unique on {nome do operador de estilo de exibição {Ran} (F_{1})times cdots times operatorname {Ran} (F_{d})} , which is the cartesian product of the ranges of the marginal cdf's. This implies that the copula is unique if the marginals {estilo de exibição F_{eu}} are continuous.

A recíproca também é verdadeira: given a copula {estilo de exibição C:[0,1]^{d}rightarrow [0,1]} and marginals {estilo de exibição F_{eu}(x)} então {displaystyle Cleft(F_{1}(x_{1}),pontos ,F_{d}(x_{d})certo)} defines a d-dimensional cumulative distribution function with marginal distributions {estilo de exibição F_{eu}(x)} .

Stationarity condition Copulas mainly work when time series are stationary[7] and continuous.[8] Desta forma, a very important pre-processing step is to check for the auto-correlation, trend and seasonality within time series.

When time series are auto-correlated, they may generate a non existence dependence between sets of variables and result in incorrect Copula dependence structure.[9] Fréchet–Hoeffding copula bounds Graphs of the bivariate Fréchet–Hoeffding copula limits and of the independence copula (in the middle).

The Fréchet–Hoeffding Theorem (after Maurice René Fréchet and Wassily Hoeffding[10]) states that for any Copula {estilo de exibição C:[0,1]^{d}rightarrow [0,1]} and any {estilo de exibição (você_{1},pontos ,você_{d})dentro [0,1]^{d}} the following bounds hold: {estilo de exibição W.(você_{1},pontos ,você_{d})leq C(você_{1},pontos ,você_{d})leq M(você_{1},pontos ,você_{d}).} The function W is called lower Fréchet–Hoeffding bound and is defined as {estilo de exibição W.(você_{1},ldots ,você_{d})=max left{1-d+sum limits _{i=1}^{d}{você_{eu}},,0certo}.} The function M is called upper Fréchet–Hoeffding bound and is defined as {estilo de exibição M(você_{1},ldots ,você_{d})=min{você_{1},pontos ,você_{d}}.} The upper bound is sharp: M is always a copula, it corresponds to comonotone random variables.

The lower bound is point-wise sharp, in the sense that for fixed u, there is a copula {estilo de exibição {tilde {C}}} de tal modo que {estilo de exibição {tilde {C}}(você)=W(você)} . No entanto, W is a copula only in two dimensions, in which case it corresponds to countermonotonic random variables.

In two dimensions, ou seja. the bivariate case, the Fréchet–Hoeffding Theorem states {displaystyle max{u+v-1,,0}leq C(você,v)leq min{você,v}} . Families of copulas Several families of copulas have been described.

Gaussian copula Cumulative and density distribution of Gaussian copula with ρ = 0.4 The Gaussian copula is a distribution over the unit hypercube {estilo de exibição [0,1]^{d}} . It is constructed from a multivariate normal distribution over {estilo de exibição mathbb {R} ^{d}} by using the probability integral transform.

For a given correlation matrix {displaystyle Rin [-1,1]^{dtimes d}} , the Gaussian copula with parameter matrix {estilo de exibição R} pode ser escrito como {estilo de exibição C_{R}^{texto{Gauss}}(você)=Phi _{R}deixei(Phi^{-1}(você_{1}),pontos ,Phi^{-1}(você_{d})certo),} Onde {displaystyle Phi ^{-1}} is the inverse cumulative distribution function of a standard normal and {displaystyle 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 {estilo de exibição R} . While there is no simple analytical formula for the copula function, {estilo de exibição C_{R}^{texto{Gauss}}(você)} , it can be upper or lower bounded, and approximated using numerical integration.[11][12] The density can be written as[13] {estilo de exibição c_{R}^{texto{Gauss}}(você)={fratura {1}{quadrado {a {R}}}}exp esquerda(-{fratura {1}{2}}{começar{pmatrix}Phi^{-1}(você_{1})\vdots \Phi ^{-1}(você_{d})fim{pmatrix}}^{T}cdot esquerda(R^{-1}-Iright)cdot {começar{pmatrix}Phi^{-1}(você_{1})\vdots \Phi ^{-1}(você_{d})fim{pmatrix}}certo),} Onde {estilo de exibição mathbf {EU} } is the identity matrix.

Archimedean copulas 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. In practice, Archimedean copulas are popular because they allow modeling dependence in arbitrarily high dimensions with only one parameter, governing the strength of dependence.

A copula C is called Archimedean if it admits the representation[14] {estilo de exibição C(você_{1},pontos ,você_{d};teta )=psi ^{[-1]}deixei(psi (você_{1};teta )+cdots +psi (você_{d};teta );theta right)} Onde {estilo de exibição psi !:[0,1]times Theta rightarrow [0,infty )} is a continuous, strictly decreasing and convex function such that {estilo de exibição psi (1;teta )=0} , {estilo de exibição teta } is a parameter within some parameter space {estilo de exibição Theta } , e {estilo de exibição psi } is the so-called generator function and {displaystyle psi ^{[-1]}} is its pseudo-inverse defined by {displaystyle psi ^{[-1]}(t;teta )= esquerda{{começar{variedade}{ll}psi ^{-1}(t;teta )&{mbox{E se }}0leq tleq psi (0;teta )\0&{mbox{E se }}psi (0;teta )leq tleq infty .end{variedade}}right.} Além disso, the above formula for C yields a copula for {displaystyle psi ^{-1}} se e apenas se {displaystyle psi ^{-1}} is d-monotone on {estilo de exibição [0,infty )} .[15] Aquilo é, if it is {displaystyle d-2} times differentiable and the derivatives satisfy {estilo de exibição (-1)^{k}psi ^{-1,(k)}(t;teta )geq 0} para todos {displaystyle tgeq 0} e {displaystyle k=0,1,dots ,d-2} e {estilo de exibição (-1)^{d-2}psi ^{-1,(d-2)}(t;teta )} is nonincreasing and convex.

Most important Archimedean copulas The following tables highlight the most prominent bivariate Archimedean copulas, with their corresponding generator. Not all of them are completely monotone, ou seja. d-monotone for all {displaystyle din mathbb {N} } or d-monotone for certain {displaystyle theta in Theta } apenas.

Table with the most important Archimedean copulas[14] Name of copula Bivariate copula {estilo de exibição ;C_{teta }(você,v)} parameter {estilo de exibição ,teta } generator {estilo de exibição ,psi_{teta }(t)} generator inverse {estilo de exibição ,psi_{teta }^{-1}(t)} Ali–Mikhail–Haq[16] {estilo de exibição {fratura {uv}{1-teta (1-você)(1-v)}}} {estilo de exibição teta em [-1,1]} {displaystyle log !deixei[{fratura {1-teta (1-t)}{t}}certo]} {estilo de exibição {fratura {1-teta }{exp(t)-teta }}} Clayton[17] {estilo de exibição à esquerda[max left{u^{-teta }+v^{-teta }-1;0certo}certo]^{-1/teta }} {estilo de exibição teta em [-1,infty )barra invertida {0}} {estilo de exibição {fratura {1}{teta }},(t^{-teta }-1)} {estilo de exibição à esquerda(1+theta tright)^{-1/teta }} Franco {estilo de exibição -{fratura {1}{teta }}registro !deixei[1+{fratura {(exp(-theta u)-1)(exp(-theta v)-1)}{exp(-teta )-1}}certo]} {displaystyle theta in mathbb {R} barra invertida {0}} {textstyle -log !deixei({fratura {exp(-theta t)-1}{exp(-teta )-1}}certo)} {estilo de exibição -{fratura {1}{teta }},registro(1+exp(-t)(exp(-teta )-1))} Gumbel {textstyle exp !deixei[-deixei((-registro(você))^{teta }+(-registro(v))^{teta }certo)^{1/teta }certo]} {estilo de exibição teta em [1,infty )} {estilo de exibição à esquerda(-registro(t)certo)^{teta }} {exp de estilo de exibição !deixei(-t^{1/teta }certo)} Independence {textstyle uv} {displaystyle -log(t)} {exp de estilo de exibição(-t)} João {estilo de texto {1-deixei[(1-você)^{teta }+(1-v)^{teta }-(1-você)^{teta }(1-v)^{teta }certo]^{1/teta }}} {estilo de exibição teta em [1,infty )} {displaystyle -log !deixei(1-(1-t)^{teta }certo)} {displaystyle 1-left(1-exp(-t)certo)^{1/teta }} Expectation for copula models and Monte Carlo integration In statistical applications, many problems can be formulated in the following way. One is interested in the expectation of a response function {estilo de exibição g:mathbb {R} ^{d}rightarrow mathbb {R} } applied to some random vector {estilo de exibição (X_{1},pontos ,X_{d})} .[18] If we denote the cdf of this random vector with {estilo de exibição H} , the quantity of interest can thus be written as {nome do operador de estilo de exibição {E} deixei[g(X_{1},pontos ,X_{d})certo]=int_{mathbb {R} ^{d}}g(x_{1},pontos ,x_{d}),matemática {d} H(x_{1},pontos ,x_{d}).} Se {estilo de exibição H} is given by a copula model, ou seja, {estilo de exibição H(x_{1},pontos ,x_{d})=C(F_{1}(x_{1}),pontos ,F_{d}(x_{d}))} this expectation can be rewritten as {nome do operador de estilo de exibição {E} deixei[g(X_{1},pontos ,X_{d})certo]=int_{[0,1]^{d}}g(F_{1}^{-1}(você_{1}),pontos ,F_{d}^{-1}(você_{d})),matemática {d} C(você_{1},pontos ,você_{d}).} In case the copula C is absolutely continuous, ou seja. C has a density c, this equation can be written as {nome do operador de estilo de exibição {E} deixei[g(X_{1},pontos ,X_{d})certo]=int_{[0,1]^{d}}g(F_{1}^{-1}(você_{1}),pontos ,F_{d}^{-1}(você_{d}))cdot c(você_{1},pontos ,você_{d}),du_{1}cdots mathrm {d} você_{d},} and if each marginal distribution has the density {estilo de exibição f_{eu}} it holds further that {nome do operador de estilo de exibição {E} deixei[g(X_{1},pontos ,X_{d})certo]=int_{mathbb {R} ^{d}}g(x_{1},pontos x_{d})cdot c(F_{1}(x_{1}),pontos ,F_{d}(x_{d}))cdot f_{1}(x_{1})cdots f_{d}(x_{d}),matemática {d} x_{1}cdots mathrm {d} x_{d}.} If copula and marginals are known (or if they have been estimated), this expectation can be approximated through the following Monte Carlo algorithm: Draw a sample {estilo de exibição (VOCÊ_{1}^{k},pontos ,VOCÊ_{d}^{k})sim C;;(k=1,dots ,n)} of size n from the copula C By applying the inverse marginal cdf's, produce a sample of {estilo de exibição (X_{1},pontos ,X_{d})} by setting {estilo de exibição (X_{1}^{k},pontos ,X_{d}^{k})=(F_{1}^{-1}(VOCÊ_{1}^{k}),pontos ,F_{d}^{-1}(VOCÊ_{d}^{k}))sim H;;(k=1,dots ,n)} Approximate {nome do operador de estilo de exibição {E} deixei[g(X_{1},pontos ,X_{d})certo]} by its empirical value: {nome do operador de estilo de exibição {E} deixei[g(X_{1},pontos ,X_{d})certo]Aproximadamente {fratura {1}{n}}soma _{k=1}^{n}g(X_{1}^{k},pontos ,X_{d}^{k})} Empirical copulas When studying multivariate data, one might want to investigate the underlying copula. Suppose we have observations {estilo de exibição (X_{1}^{eu},X_{2}^{eu},pontos ,X_{d}^{eu}),,i=1,dots ,n} from a random vector {estilo de exibição (X_{1},X_{2},pontos ,X_{d})} with continuous marginals. The corresponding “true” copula observations would be {estilo de exibição (VOCÊ_{1}^{eu},VOCÊ_{2}^{eu},pontos ,VOCÊ_{d}^{eu})= esquerda(F_{1}(X_{1}^{eu}),F_{2}(X_{2}^{eu}),pontos ,F_{d}(X_{d}^{eu})certo),,i=1,dots ,n.} No entanto, the marginal distribution functions {estilo de exibição F_{eu}} are usually not known. Portanto, one can construct pseudo copula observations by using the empirical distribution functions {estilo de exibição F_{k}^{n}(x)={fratura {1}{n}}soma _{i=1}^{n}mathbf {1} (X_{k}^{eu}leq x)} instead. Então, the pseudo copula observations are defined as {estilo de exibição ({tilde {você}}_{1}^{eu},{tilde {você}}_{2}^{eu},pontos ,{tilde {você}}_{d}^{eu})= esquerda(F_{1}^{n}(X_{1}^{eu}),F_{2}^{n}(X_{2}^{eu}),pontos ,F_{d}^{n}(X_{d}^{eu})certo),,i=1,dots ,n.} The corresponding empirical copula is then defined as {estilo de exibição C^{n}(você_{1},pontos ,você_{d})={fratura {1}{n}}soma _{i=1}^{n}mathbf {1} deixei({tilde {você}}_{1}^{eu}leq u_{1},pontos ,{tilde {você}}_{d}^{eu}leq u_{d}certo).} The components of the pseudo copula samples can also be written as {estilo de exibição {tilde {você}}_{k}^{eu}=R_{k}^{eu}/n} , Onde {estilo de exibição R_{k}^{eu}} is the rank of the observation {estilo de exibição X_{k}^{eu}} : {estilo de exibição R_{k}^{eu}=soma _{j=1}^{n}mathbf {1} (X_{k}^{j}leq X_{k}^{eu})} Portanto, the empirical copula can be seen as the empirical distribution of the rank transformed data.

The sample version of Spearman's rho: [19] {displaystyle r={fratura {12}{n^{2}-1}}soma _{i=1}^{n}soma _{i=1}^{n}deixei[C^{n}deixei({fratura {eu}{n}},{fratura {j}{n}}certo)-{fratura {eu}{n}}cdot {fratura {j}{n}}certo]} Applications Quantitative finance Examples of bivariate copulæ used in finance. Typical finance applications: Analyzing systemic risk in financial markets[20] Analyzing and pricing spread options, in particular in fixed income constant maturity swap spread options Analyzing and pricing volatility smile/skew of exotic baskets, por exemplo. best/worst of Analyzing and pricing volatility smile/skew of less liquid FX[esclarecimento necessário] cross, which is effectively a basket: C = S1/S2 or C = S1·S2 Value-at-Risk forecasting and portfolio optimization to minimize tail risk for US and international equities[2] Forecasting equities returns for higher-moment portfolio optimization/full-scale optimization[20] Improving the estimates of a portfolio's expected return and variance-covariance matrix for input into sophisticated mean-variance optimization strategies[3] Statistical arbitrage strategies including pairs trading[21] In quantitative finance copulas are applied to risk management, to portfolio management and optimization, and to derivatives pricing.

For the former, 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 (por exemplo., the global financial crisis of 2007–2008). The formula was also adapted for financial markets and was used to estimate the probability distribution of losses on pools of loans or bonds.

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. Como resultado, 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.[22][23] Por exemplo, anecdotally, we often read financial news headlines reporting the loss of hundreds of millions of dollars on the stock exchange in a single day; Contudo, we rarely read reports of positive stock market gains of the same magnitude and in the same short time frame.

Copulas aid in analyzing the effects of downside regimes by allowing the modelling of the marginals and dependence structure of a multivariate probability model separately. Por exemplo, 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. No entanto, as all traders operate on the same exchange, each trader's actions have an interaction effect with other traders'. This interaction effect can be described by modelling the dependence structure. Portanto, 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. (See also agent-based computational economics, where price is treated as an emergent phenomenon, resulting from the interaction of the various market participants, or agents.) The users of the formula have been criticized for creating "evaluation cultures" that continued to use simple copulæ despite the simple versions being acknowledged as inadequate for that purpose.[24][25] Desta forma, previously, scalable copula models for large dimensions only allowed the modelling of elliptical dependence structures (ou seja, Gaussian and Student-t copulas) that do not allow for correlation asymmetries where correlations differ on the upside or downside regimes. No entanto, the development of vine copulas[26] (also known as pair copulas) enables the flexible modelling of the dependence structure for portfolios of large dimensions.[27] The Clayton canonical vine copula allows for the occurrence of extreme downside events and has been successfully applied in portfolio optimization 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.[28] 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.[29] As regards derivatives pricing, 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 obligations (CDOs).[30] Some believe the methodology of applying the Gaussian copula to credit derivatives to be one of the reasons behind the global financial crisis of 2008–2009;[31][32][33] see David X. Li § CDOs and Gaussian copula.

Despite this perception, there are documented attempts within 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. The Gaussian copula is lacking as it only allows for an elliptical dependence structure, as dependence is only modeled using the variance-covariance matrix.[28] This methodology is limited such that it does not allow for dependence to evolve as the financial markets exhibit asymmetric dependence, whereby correlations across assets significantly increase during downturns compared to upturns. Portanto, modeling approaches using the Gaussian copula exhibit a poor representation of extreme events.[28][34] There have been attempts to propose models rectifying some of the copula limitations.[34][35][36] Additional to CDOs, Copulas have 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 a basket implied volatility surface,[37] taking into account the volatility smile of basket components. Copulas have since gained popularity in pricing and risk management[38] of options on multi-assets in the presence of a volatility smile, in equity-, foreign exchange- and fixed income derivatives.

Civil engineering Recently, copula functions have been successfully applied to the database formulation for the reliability analysis of highway bridges, and to various multivariate simulation studies in civil engineering,[39] reliability of wind and earthquake engineering,[40] and mechanical & offshore engineering.[41] Researchers are also trying these functions in the field of transportation to understand the interaction between behaviors of individual drivers which, in totality, shapes traffic flow.

Reliability engineering Copulas are being used for reliability analysis of complex systems of machine components with competing failure modes. [42] Warranty data analysis Copulas are being used for warranty data analysis in which the tail dependence is analysed [43] Turbulent combustion Copulas are used in modelling turbulent partially premixed combustion, which is common in practical combustors. [44] [45] Medicine Copulæ have many applications in the area of medicine, por exemplo, Copulæ have been used in the field of magnetic resonance imaging (MRI), por exemplo, to segment images,[46] to fill a vacancy of graphical models in imaging genetics in a study on schizophrenia,[47] and to distinguish between normal and Alzheimer patients.[48] Copulæ have been in the area of brain research based on EEG signals, por exemplo, to detect drowsiness during daytime nap,[49] to track changes in instantaneous equivalent bandwidths (IEBWs),[50] to derive synchrony for early diagnosis of Alzheimer's disease,[51] to characterize dependence in oscillatory activity between EEG channels,[52] and to assess the reliability of using methods to capture dependence between pairs of EEG channels using their time-varying envelopes.[53] Copula functions have been successfully applied to the analysis of neuronal dependencies[54] and spike counts in neuroscience .[55] A copula model has been developed in the field of oncology, por exemplo, to jointly model genotypes, phenotypes, and pathways to reconstruct a cellular network to identify interactions between specific phenotype and multiple molecular features (por exemplo. mutations and gene expression change). Bao et al.[56] used NCI60 cancer cell line data to identify several subsets of molecular features that jointly perform as the predictors of clinical phenotypes. The proposed copula may have an impact on biomedical research, ranging from cancer treatment to disease prevention. Copula has also been used to predict the histological diagnosis of colorectal lesions from colonoscopy images,[57] and to classify cancer subtypes.[58] Geodesy The combination of SSA and Copula-based methods have been applied for the first time as a novel stochastic tool for EOP prediction.[59][60] Hydrology research Copulas have been used in both theoretical and applied analyses of hydroclimatic data. Theoretical studies adopted the copula-based methodology for instance to gain a better understanding of the dependence structures of temperature and precipitation, in different parts of the world.[9][61][62] Applied studies adopted the copula-based methodology to examine e.g., agricultural droughts [63] or joint effects of temperature and precipitation extremes on vegetation growth.[64] Climate and weather research Copulas have been extensively used in climate- and weather-related research.[65][66] Solar irradiance variability Copulas have been used to estimate the solar irradiance variability in spatial networks and temporally for single locations. [67] [68] Random vector generation Large synthetic traces of vectors and stationary time series can be generated using empirical copula while preserving the entire dependence structure of small datasets.[69] Such empirical traces are useful in various simulation-based performance studies.[70] Ranking of electrical motors Copulas have been used for quality ranking in the manufacturing of electronically commutated motors.[71] Signal processing Copulas are important because they represent a dependence structure without using marginal distributions. Copulas have been widely used in the field of finance, but their use in signal processing is relatively new. Copulas have been employed in the field of wireless communication for classifying radar signals, change detection in remote sensing applications, and EEG signal processing in medicine. In this section, a short mathematical derivation to obtain copula density function followed by a table providing a list of copula density functions with the relevant signal processing applications are presented.

Mathematical derivation of copula density function For any two random variables X and Y, the continuous joint probability distribution function can be written as {estilo de exibição F_{XY}(x,y)= Pr {começar{Bmatrix}Xleq {x},Yleq {y}fim{Bmatrix}},} Onde {textstyle F_{X}(x)= Pr {começar{Bmatrix}Xleq {x}fim{Bmatrix}}} e {textstyle F_{S}(y)= Pr {começar{Bmatrix}Yleq {y}fim{Bmatrix}}} are the marginal cumulative distribution functions of the random variables X and Y, respectivamente.

then the copula distribution function {estilo de exibição C(você,v)} can be defined using Sklar's theorem[72][73] Como: {estilo de exibição F_{XY}(x,y)=C(F_{X}(x),F_{S}(y))triangleq C(você,v)} , Onde {displaystyle u=F_{X}(x)} e {displaystyle v=F_{S}(y)} are marginal distribution functions, {estilo de exibição F_{XY}(x,y)} joint and {estilo de exibição você,vinho (0,1)} .

We start by using the relationship between joint probability density function (PDF) and joint cumulative distribution function (CDF) and its partial derivatives.

{estilo de exibição {começar{alignedat}{6}f_{XY}(x,y)={}&{parcial ^{2}F_{XY}(x,y) over partial x,y parcial}\vdots \f_{XY}(x,y)={}&{parcial ^{2}C(F_{X}(x),F_{S}(y)) over partial x,y parcial}\vdots \f_{XY}(x,y)={}&{parcial ^{2}C(você,v) over partial u,partial v}cdot {partial F_{X}(x) over partial x}cdot {partial F_{S}(y) over partial y}\vdots \f_{XY}(x,y)={}&c(você,v)f_{X}(x)f_{S}(y)\vdots \{fratura {f_{XY}(x,y)}{f_{X}(x)f_{S}(y)}}={}&c(você,v)fim{alignedat}}} Onde {estilo de exibição c(você,v)} is the copula density function, {estilo de exibição f_{X}(x)} e {estilo de exibição f_{S}(y)} are the marginal probability density functions of X and Y, respectivamente. It is important to understand that there are four elements in this equation, and if any three elements are known, the fourth element can be calculated. Por exemplo, it may be used, when joint probability density function between two random variables is known, the copula density function is known, and one of the two marginal functions are known, então, the other marginal function can be calculated, or when the two marginal functions and the copula density function are known, then the joint probability density function between the two random variables can be calculated, or when the two marginal functions and the joint probability density function between the two random variables are known, then the copula density function can be calculated. List of copula density functions and applications Various bivariate copula density functions are important in the area of signal processing. {displaystyle u=F_{X}(x)} e {displaystyle v=F_{S}(y)} are marginal distributions functions and {estilo de exibição f_{X}(x)} e {estilo de exibição f_{S}(y)} are marginal density functions. Extension and generalization of copulas for statistical signal processing have been shown to construct new bivariate copulas for exponential, Weibull, and Rician distributions.[74] Zeng et al.[75] presented algorithms, simulation, optimal selection, and practical applications of these copulas in signal processing.

Copula density: c(você, v) Use Gaussian {estilo de exibição {começar{alinhado}={}&{fratura {1}{quadrado {1-oh ^{2}}}}exp esquerda(-{fratura {(um^{2}+b^{2})oh ^{2}-2abrho }{2(1-oh ^{2})}}certo)\&{texto{Onde }}rho in (-1,1)\&{texto{Onde }}a={quadrado {2}}nome do operador {erf} ^{-1}({2u-1})\&{texto{Onde }}b={quadrado {2}}nome do operador {erf} ^{-1}({2v-1})\&{texto{Onde }}nome do operador {erf} (z)={fratura {2}{quadrado {pi }}}int limits _{0}^{z}exp(-t^{2}),dtend{alinhado}}} supervised classification of synthetic aperture radar (SAR) images,[76] validating biometric authentication,[77] modeling stochastic dependence in large-scale integration of wind power,[78] unsupervised classification of radar signals[79] Exponential {estilo de exibição {começar{alinhado}={}&{fratura {1}{1-rho }}exp esquerda({fratura {rho (ln(1-você)+ln(1-v))}{1-rho }}certo)cdot I_{0}deixei({fratura {2{quadrado {rho ln(1-você)ln(1-v)}}}{1-rho }}certo)\&{texto{Onde }}x=F_{X}^{-1}(você)=-ln(1-você)/lambda \&{texto{Onde }}y=F_{S}^{-1}(v)=-ln(1-v)/mu end{alinhado}}} queuing system with infinitely many servers[80] Rayleigh bivariate exponential, Rayleigh, and Weibull copulas have been proved to be equivalent[81][82][83] change detection from SAR images[84] Weibull bivariate exponential, Rayleigh, and Weibull copulas have been proved to be equivalent[81][82][83] digital communication over fading channels[85] Log-normal bivariate log-normal copula and Gaussian copula are equivalent[83][82] shadow fading along with multipath effect in wireless channel[86][87] Farlie–Gumbel–Morgenstern (FGM) {estilo de exibição {começar{alinhado}={}&1+theta (1-2você)(1-2v)\&{texto{Onde }}theta in [-1,1]fim{alinhado}}} information processing of uncertainty in knowledge-based systems[88] Clayton {estilo de exibição {começar{alinhado}={}&(1+teta )(uv)^{(-1-teta )}(-1+u^{-teta }+v^{-teta })^{(-2-1/teta )}\&{texto{Onde }}theta in (-1,infty ),theta neq 0end{alinhado}}} location estimation of random signal source and hypothesis testing using heterogeneous data[89][90] Franco {estilo de exibição {começar{alinhado}={}&{fratura {teta e^{teta (u+v)}(e^{teta }-1)}{(e^{teta }-e^{theta u}-e^{theta v}+e^{teta (u+v)})^{2}}}\&{texto{Onde }}theta in (-infty ,+infty ),theta neq 0end{alinhado}}} change detection in remote sensing applications[91] Student's t {estilo de exibição {começar{alinhado}={}&{fratura {Gama (0.5v)Gama (0.5v+1)(1+(t_{v}^{-2}(você)+t_{v}^{-2}(v)-2rho t_{v}^{-1}(você)t_{v}^{-1}(v))/(v(1-oh ^{2})))^{-0.5(v+2)})}{{quadrado {1-oh ^{2}}}cdot Gamma (0.5(v+1))^{2}(1+t_{v}^{-2}(você)/v)^{-0.5(v+1)}(1+t_{v}^{-2}(v)/v)^{-0.5(v+1)}}}\&{texto{Onde }}rho in (-1,1)\&{texto{Onde }}phi (z)={fratura {1}{quadrado {2pi }}}int limits _{-infty }^{z}exp esquerda({fratura {-t^{2}}{2}}certo),dt\&{texto{Onde }}t_{v}(xmid v)=int limits _{-infty }^{x}{fratura {Gama {(0.5(v+1))}}{{quadrado {vpi }}(Gama {0.5v})(1+v^{-1}t^{2})^{0.5(v+1)}}}dt\&{texto{Onde }}v={texto{degrees of freedom}}\&{texto{Onde }}Gama {texto{ is the Gamma function}}fim{alinhado}}} supervised SAR image classification,[84] fusion of correlated sensor decisions[92] Nakagami-m Rician See also Coupling (probability) References ^ Thorsten Schmidt (2006) "Coping with Copulas", https://web.archive.org/web/20100705040514/http://www.tu-chemnitz.de/mathematik/fima/publikationen/TSchmidt_Copulas.pdf ^ Jump up to: a b Low, R.K.Y.; Alcock, J.; Faff, R.; Brailsford, T. 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IEEE Transactions on Aerospace and Electronic Systems. 47 (1): 454–471. Bibcode:2011ITAES..47..454S. doi:10.1109/taes.2011.5705686. ISSN 0018-9251. S2CID 22562771. 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 Roger B. Nelson (1999), "An Introduction to Copulas", Springer. ISBN 978-0-387-98623-4 A book covering current topics in mathematical research on copulas: 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 A reference for sampling applications and stochastic models related to copulas is Jan-Frederik Mai, Matthias Scherer (2012): Simulating Copulas (Stochastic Models, Sampling Algorithms and Applications). Mundial Científico. 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. Abe Sklar (1997): "Random variables, distribution functions, and copulas – a personal look backward and forward" in Rüschendorf, EU., Schweizer, B. und Taylor, M. (eds) Distributions With Fixed Marginals & Related Topics (Lecture Notes – Monograph Series Number 28). ISBN 978-0-940600-40-9 The standard reference for multivariate models and copula theory in the context of financial and insurance models 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 links externos "Cópula", Enciclopédia de Matemática, Imprensa EMS, 2001 [1994] Copula Wiki: community portal for researchers with interest in copulas A collection of Copula simulation and estimation codes Copulas & Correlation using Excel Simulation Articles Chapter 1 of Jan-Frederik Mai, Matthias Scherer (2012) "Simulating Copulas: Stochastic Models, Sampling Algorithms, and Applications" hide vte Statistics OutlineIndex show Descriptive statistics show Data collection show Statistical inference show CorrelationRegression analysis hide Categorical / Multivariate / Time-series / Survival analysis Categorical Cohen's kappaContingency tableGraphical modelLog-linear modelMcNemar's testCochran-Mantel-Haenszel statistics Multivariate RegressionManovaPrincipal componentsCanonical correlationDiscriminant analysisCluster analysisClassificationStructural equation model Factor analysisMultivariate distributions Elliptical distributions Normal Time-series General DecompositionTrendStationaritySeasonal adjustmentExponential smoothingCointegrationStructural breakGranger causality Specific tests Dickey–FullerJohansenQ-statistic (Ljung–Box)Durbin–WatsonBreusch–Godfrey Time domain Autocorrelation (ACF) parcial (PACF)Cross-correlation (XCF)ARMA modelARIMA model (Box–Jenkins)Autoregressive conditional heteroskedasticity (ARCH)Vector autoregression (VAR) Frequency domain Spectral density estimationFourier analysisLeast-squares spectral analysisWaveletWhittle likelihood Survival Survival function Kaplan–Meier estimator (product limit)Proportional hazards modelsAccelerated failure time (AFT) modelFirst hitting time Hazard function Nelson–Aalen estimator Test Log-rank test show Applications Category Mathematics portalCommons WikiProject Authority control: National libraries IsraelUnited States Categories: Actuarial scienceMultivariate statisticsIndependence (teoria da probabilidade)Systems of probability distributions

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