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

In extremis: Statistics of Extremes

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)

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.

Details

The intermediate sequence κ_T is chosen proportional to T/\log T.

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.

Author(s)

Miguel de Carvalho, Rodrigo Rubio.

References

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

Examples

## Not run: 
## 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))

## End(Not run)

extremis documentation built on Dec. 9, 2022, 5:08 p.m.