extremis (version 1.2.1)

kgvar: K-Geometric Means Algorithm for Value-at-Risk

Description

This function performs k-geometric means for time-varying value-at-risk.

Usage

kgvar(y, centers, iter.max = 10, conf.level = 0.95)

Value

kgvar returns an object of class "kgvar" which has a fitted method. It is a list with at least the following components:

var.new

cluster center value-at-risk function.

clusters

cluster allocation.

Y

raw data.

n.clust

number of clusters.

scale.param

the scale parameters in the Pareto-like tail specification.

conf.level

the confidence level.

hill

hill estimator of extreme value index.

The plot method depicts the k-geometric means algorithm for time-varying value-at-risk. If c.c is TRUE, the method displays the cluster means.

Arguments

y

data frame from which the estimate is to be computed; first column corresponds to time and the second to the remainder of interest.

centers

the number of clusters or a set of initial (distinct) cluster centres. If a number, a random set of (distinct) rows in y is chosen as the initial centers.

iter.max

the maximum number of iterations allowed. The default is 10.

conf.level

the confidence level. The default is 0.95.

Author

Miguel de Carvalho, Rodrigo Rubio.

Details

The intermediate sequence \(\kappa_T\) is chosen proportional to \(T/\log T\).

References

Rubio, R., de Carvalho, M. and Huser, R. (2018) Similarity-Based Clustering of Extreme Losses from the London Stock Exchange. Submitted.

Examples

if (FALSE) {
## Example (Overlapping version of Fig. 8 in Supplementary Materials)
data(lse)
attach(lse)
y <- -apply(log(lse[, -1]), 2, diff) 
fit <- kgvar(y, centers = 3)
plot(fit, c.c = TRUE, ylim = c(0, 0.1))
}