Learn R Programming

VineCopula (version 1.6-1)

BiCopHfunc: Conditional Distribution Function of a Bivariate Copula

Description

This function evaluates the conditional distribution function (h-function) of a given parametric bivariate copula.

Usage

BiCopHfunc(u1, u2, family, par, par2 = 0, obj = NULL)

Arguments

u1, u2
Numeric vectors of equal length with values in [0,1].
family
An integer defining the bivariate copula family: 0 = independence copula 1 = Gaussian copula 2 = Student t copula (t-copula) 3 = Clayton copula 4 = Gumbel copula 5 = Frank
par
Copula parameter.
par2
Second parameter for bivariate copulas with two parameters (t, BB1, BB6, BB7, BB8, Tawn type 1 and type 2; default: par2 = 0).
obj
BiCop object containing the family and parameter specification.

Value

  • hfunc1Numeric vector of the conditional distribution function (h-function) evaluated at u2 given u1, i.e., $h(\code{u2}|\code{u1},\boldsymbol{\theta})$.
  • hfunc2Numeric vector of the conditional distribution function (h-function) evaluated at u1 given u2, i.e., $h(\code{u1}|\code{u2},\boldsymbol{\theta})$.

Details

The h-function is defined as the conditional distribution function of a bivariate copula, i.e., $$h(u|v,\boldsymbol{\theta}) := F(u|v) = \frac{\partial C(u,v)}{\partial v},$$ where $C$ is a bivariate copula distribution function with parameter(s) $\boldsymbol{\theta}$. For more details see Aas et al. (2009). If the family and parameter specification is stored in a BiCop object obj, the alternative version BiCopHfunc(u1, u2, obj) can be used.

References

Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44 (2), 182-198.

See Also

BiCopPDF, BiCopCDF, RVineLogLik, RVineSeqEst, BiCop

Examples

Run this code
# load data set
data(daxreturns)

# h-functions of the Gaussian copula
h1 <- BiCopHfunc(daxreturns[,2], daxreturns[,1], 1, 0.5)
h1

Run the code above in your browser using DataLab