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BiCopHfunc

Conditional Distribution Function of a Bivariate Copula

Description

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

Usage

BiCopHfunc(u1, u2, family, par, par2 = 0, obj = NULL, check.pars = TRUE)

BiCopHfunc1(u1, u2, family, par, par2 = 0, obj = NULL, check.pars = TRUE)

BiCopHfunc2(u1, u2, family, par, par2 = 0, obj = NULL, check.pars = TRUE)

Arguments

u1, u2

numeric vectors of equal length with values in [0,1].
family

integer; single number or vector of size length(u1); defines the bivariate copula family:
0 = independence copula
1 = Gaussian copula
2 = Student t copula (t-copula)
3 = Clayton copula
4 = Gumbel copula
5 = Frank copula
6 = Joe copula
7 = BB1 copula
8 = BB6 copula
9 = BB7 copula
10 = BB8 copula
13 = rotated Clayton copula (180 degrees; survival Clayton'') \cr `14` = rotated Gumbel copula (180 degrees; survival Gumbel”)
16 = rotated Joe copula (180 degrees; survival Joe'') \cr `17` = rotated BB1 copula (180 degrees; survival BB1”)
18 = rotated BB6 copula (180 degrees; survival BB6'')\cr `19` = rotated BB7 copula (180 degrees; survival BB7”)
20 = rotated BB8 copula (180 degrees; “survival BB8”)
23 = rotated Clayton copula (90 degrees)
'24' = rotated Gumbel copula (90 degrees)
'26' = rotated Joe copula (90 degrees)
'27' = rotated BB1 copula (90 degrees)
'28' = rotated BB6 copula (90 degrees)
'29' = rotated BB7 copula (90 degrees)
'30' = rotated BB8 copula (90 degrees)
'33' = rotated Clayton copula (270 degrees)
'34' = rotated Gumbel copula (270 degrees)
'36' = rotated Joe copula (270 degrees)
'37' = rotated BB1 copula (270 degrees)
'38' = rotated BB6 copula (270 degrees)
'39' = rotated BB7 copula (270 degrees)
'40' = rotated BB8 copula (270 degrees)
'104' = Tawn type 1 copula
'114' = rotated Tawn type 1 copula (180 degrees)
'124' = rotated Tawn type 1 copula (90 degrees)
'134' = rotated Tawn type 1 copula (270 degrees)
'204' = Tawn type 2 copula
'214' = rotated Tawn type 2 copula (180 degrees)
'224' = rotated Tawn type 2 copula (90 degrees)
'234' = rotated Tawn type 2 copula (270 degrees)

par

numeric; single number or vector of size length(u1); copula parameter.
par2

numeric; single number or vector of size length(u1); second parameter for bivariate copulas with two parameters (t, BB1, BB6, BB7, BB8, Tawn type 1 and type 2; default: par2 = 0). par2 should be an positive integer for the Students's t copula family = 2.
obj

BiCop object containing the family and parameter specification.
check.pars

logical; default is TRUE; if FALSE, checks for family/parameter-consistency are omitted (should only be used with care).

Details

The h-function is defined as the conditional distribution function of a bivariate copula, i.e.,

h_1(u_2|u_1,θ) := P(U_2 ≤ u_2 | U_1 = u_1) = \partial C(u_1,u_2) / \partial u_1,

h_2(u_1|u_2,θ) := P(U_1 ≤ u_1 | U_2 = u_2) := \partial C(u_1,u_2) / \partial u_2,

where (U_1, U_2) \sim C, and C is a bivariate copula distribution function with parameter(s) θ. For more details see Aas et al. (2009).

If the family and parameter specification is stored in a BiCop() object obj, the alternative versions 

BiCopHfunc(u1, u2, obj)
BiCopHfunc1(u1, u2, obj)
BiCopHfunc2(u1, u2, obj)

can be used.

Value

BiCopHfunc returns a list with

hfunc1

Numeric vector of the conditional distribution function (h-function) of the copula family with parameter(s) par, par2 evaluated at u2 given u1, i.e., h_1(u_2|u_1;θ).
hfunc2

Numeric vector of the conditional distribution function (h-function) of the copula family with parameter(s) par, par2 evaluated at u1 given u2, i.e., h_2(u_1|u_2; θ).

BiCopHfunc1 is a faster version that only calculates hfunc1; BiCopHfunc2 only calculates hfunc2.

Author(s)

Ulf Schepsmeier

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

Examples

data(daxreturns)

# h-functions of the Gaussian copula
cop <- BiCop(family = 1, par = 0.5)
h <- BiCopHfunc(daxreturns[, 2], daxreturns[, 1], cop)

# or using the fast versions
h1 <- BiCopHfunc1(daxreturns[, 2], daxreturns[, 1], cop)
h2 <- BiCopHfunc2(daxreturns[, 2], daxreturns[, 1], cop)
all.equal(h$hfunc1, h1)
all.equal(h$hfunc2, h2)
VineCopula

Statistical Inference of Vine Copulas

v2.4.1
GPL (>= 2)
Authors
Thomas Nagler [aut, cre], Ulf Schepsmeier [aut], Jakob Stoeber [aut], Eike Christian Brechmann [aut], Benedikt Graeler [aut], Tobias Erhardt [aut], Carlos Almeida [ctb], Aleksey Min [ctb, ths], Claudia Czado [ctb, ths], Mathias Hofmann [ctb], Matthias Killiches [ctb], Harry Joe [ctb], Thibault Vatter [ctb]
Initial release

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