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Download a pdf copy of this help file here |
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Download a pdf copy of this help file here |
See also: Copulas in ModelRisk, Bivariate Copula window, Vose Multivariate Copula, Archimedean copulas - Clayton Frank and Gumbel
{=VoseCopulaBiClayton(Alpha, direction)}
Array function that returns random variables from a bivariate Clayton copula.
Alpha - Correlation parameter. Can range from -35 (maximum negative correlation) over 0 (no correlation) to 36 (maximum positive correlation)
Direction - optional parameter that sets the direction of the parameter: can take values 1 (default), 2, 3 or 4.
The output is an array of two cells, with randomly generated copula values between [0,1]. Link the U-parameter of distribution functions to these to generate values of these distributions correlated by this copula.
The optional direction parameter changes the direction of the copula. This can take values 1,2,3,4, orienting the generated densities as illustrated below (when omitted the direction is 1):
For the multivariate version of this copula see VoseCopulaMultiClayton.
So for example, to generate a normal(0,1) and a beta(2,1) value correlated by a Clayton(3) copula, you would do the following:
Select the A1 and B1 spreadsheet cells.
Type =VoseCopulaBiClayton(3) in the Excel formula bar and press CTRL+SHIFT+ENTER - Excel now inserts this as an array function over the two selected cells, indicated by curly brackets.
Insert =VoseNormal(0,1,A1) in the cell A2, and =VoseBeta(2,1, B1) in the cell B2. The cell references are U parameters that refer to the copula values generated in the first cell.
Now the A2 and B2 cell contain random values correlated by the copula.
VoseCopulaBiClayton generates values from this copula.
VoseCopulaBiClaytonFit fits this copula to data.
VoseCopulaBiClaytonFitP returns the parameter(s) of this copula fitted to data.
VoseCopulaBiClaytonObject creates a copula object for this copula (use VoseCopulaSimulate to simulate from it).
VoseCopulaBiClaytonFitObject creates a copula object for this copula fitted to data (use VoseCopulaSimulate to simulate from it).
Example model