TailProb_sum {AoE} | R Documentation |
Computes an estimate of the probability that w_x * X + w_y * Y > s, where s is large.
TailProb_sum(s, w.x = 1, w.y = 1, tail.x, tail.y, Phi, plot = TRUE)
s |
The level for which the probability of excess is to be estimated. |
w.x |
A positive number; the weight w_x of X. |
w.y |
A positive number; the weight w_y of X. |
tail.x |
An object with class attribute "GPD_par" , i.e. the output of a call to the function GPD_par applied to the X data. |
tail.y |
Idem, now for the Y data. |
Phi |
An object with class attribute "AngularMeasure" , i.e. the output of a call to the function AngularMeasure applied to the data. |
plot |
If TRUE (the default), the results will be plotted. |
If tail.x$k
and tail.y$k
are vectors, the tail probability will be estimated for every possible combination of choices of k for X and Y.
If plot
is TRUE
, the tail probability estimates are plotted as a surface in function of tail.x$k
and tail.y$k
.
The function invisibly returns a matrix with at position (i,j) the estimated tail probability when the tails of X and Y are estimated for k equal to tail.x$k[i]
and tail.x$k[j]
.
AngularMeasure
, GPD_par
, TailQuantile_sum
# estimate probability that the daily logreturn # of a balanced portfolio of stocks ABN AMRO and ING # is less than -0.10, i.e. a loss on the portfolio # of more than about 10 percent data(ABN, ING) GPD.x <- GPD_par(-ABN, method = "Moment", k = (2:30)*10) GPD.y <- GPD_par(-ING, method = "Moment", k = (2:30)*10) Phi <- AngularMeasure(data.x = -ABN, data.y = -ING, k = 100) TailProb_sum(s = 0.10, w.x = 0.5, w.y = 0.5, tail.x = GPD.x, tail.y = GPD.y, Phi = Phi)