INFOSET (version 4.1)

infoset: Procedure to find the most-left distribution set.

Description

Estimation of the vector of unknown parameters for the density functions associated with the two mixture components.

Usage

infoset(y, plot_cp)

Value

An object of class "infoset" is a list containing the following components for the firse two iterations (k=2):

change.points

a vector of change points.

prior.probability

the a priori probabilities.

first.type.errors

the cumulative distribution functions associated with the leftmost component of the mixture.

second.type.errors

the cumulative distribution functions associated with the rightmost component of the mixture.

mean

the parameters (drift) of the left-hand component of the log-normal mixture.

sd

the parameters (volatility) of the left-hand component of the log-normal mixture.

Arguments

y

object of class "g_ret"

plot_cp

option

References

Mariani, F., Polinesi, G., Recchioni, M. C. (2022). A tail-revisited Markowitz mean-variance approach and a portfolio network centrality. Computational Management Science, 19(3), 425-455.

Mariani, F., Ciommi, M., Chelli, F. M., Recchioni, M. C. (2020). An iterative approach to stratification: Poverty at regional level in Italy. Social Indicators Research, 1-31.

Examples

gross.ret<-as.data.frame(lapply(sample.data, g_ret))
infoset(gross.ret$ETF_1, plot_cp = "T")

############################################################
## EXAMPLE 1: Clustering ETFs
############################################################

# \donttest{
gross.ret<-as.data.frame(lapply(sample.data, g_ret))
result<-NULL
for(i in 1:ncol(gross.ret)){
 result[[i]]<-infoset(gross.ret[,i], plot_cp = "F")
}
output<-matrix(unlist(result),12,ncol=ncol(gross.ret)) # output contains the information set
output<-t(output)
rownames(output)<-colnames(gross.ret)
colnames(output)<-c("ch_1","ch_2","priori_1","priori_2","first_1",
                   "first_2","second_1","second_2","mean_1","mean_2","dev_1", "dev_2")
output<- as.data.frame(output)
group_label <- as.factor(asset.label$label)
d <- dist(output, method = 'euclidean')
hc_SIMS <- hclust(d, method = 'complete')
library(dendextend)
library(colorspace)
dend_SIMS <- as.dendrogram(hc_SIMS)
dend_SIMS <- color_branches(dend_SIMS, k = 4, col = c(1:4))
labels_colors(dend_SIMS) <-
       rainbow_hcl(5)[sort_levels_values(as.numeric(group_label)[order.dendrogram(dend_SIMS)])]
labels(dend_SIMS) <- paste(as.character(group_label)[order.dendrogram(dend_SIMS)],
       '(', labels(dend_SIMS), ')', sep = '')
dend_SIMS <- hang.dendrogram(dend_SIMS, hang_height = 0.001)
dend_SIMS <- assign_values_to_leaves_nodePar(dend_SIMS, 0.5, 'lab.cex')
dev.new()
old_par <- par(no.readonly = TRUE)
on.exit(par(old_par))
par(mar = c(1.8, 1.8, 1.8, 1))
plot(dend_SIMS, main = 'Complete linkage (the labels give the true ETF class)',
     horiz = TRUE, nodePar = list(cex = 0.007))
legend('topleft', legend = c('emerging equity Asia', 'emerging equity America',
                           'corporate bond', 'commodities', 'aggregate bond'),
             fill = c('#BDAB66', '#65BC8C', '#C29DDE', '#E495A5', '#55B8D0'), border = 'white')
# }