Package 'INFOSET'

Title: Computing a New Informative Distribution Set of Asset Returns
Description: Estimation of the most-left informative set of gross returns (i.e., the informative set). The procedure to compute the informative set adjusts the method proposed by Mariani et al. (2022a) <doi:10.1007/s11205-020-02440-6> and Mariani et al. (2022b) <doi:10.1007/s10287-022-00422-2> to gross returns of financial assets. This is accomplished through an adaptive algorithm that identifies sub-groups of gross returns in each iteration by approximating their distribution with a sequence of two-component log-normal mixtures. These sub-groups emerge when a significant change in the distribution occurs below the median of the financial returns, with their boundary termed as the “change point" of the mixture. The process concludes when no further change points are detected. The outcome encompasses parameters of the leftmost mixture distributions and change points of the analyzed financial time series. The functionalities of the INFOSET package include: (i) modelling asset distribution detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function) (iv) portfolio construction (ptf_construction function). The package also provide a specific function to construct rolling windows of different length size and overlapping time.
Authors: Gloria Polinesi [aut, cre], Francesca Mariani [aut], Maria Cristina Recchioni [aut]
Maintainer: Gloria Polinesi <[email protected]>
License: GPL (>= 2)
Version: 4.1
Built: 2024-12-24 06:31:18 UTC
Source: CRAN

Help Index

Data for clustering and labeling ETFs

Description

Contains asset class of ETFs

Usage

asset.label

Format

A data frame with 44 observations (rows) on 3 variables (columns)

id

name of ETF

label

from 1 to 5 according to the specific asset class

class

specific asset class (5 categories)

Source

Created in-house to serve as an example

Examples

data(asset.label)

Function to create overlapping windows.

Description

Function to create overlapping windows.

Usage

create_overlapping_windows(data, FT = 1290, ov = 125)

Arguments

data

vector or data frame
FT

Window size. By default set to 1290 training days (five years).
ov

Number of different days for two consecutive time windows.. By default set to 125 training days (six months).

Value

a list containing the rolling windows

Function to compute gross returns.

Description

Calculate gross returns from prices.

Usage

g_ret(x)

Arguments

x

data object containing ordered price observations

Value

An object of the same class as x with the gross returns

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)

Arguments

y

object of class "g_ret"
plot_cp

option

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.

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
############################################################


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')

Function to compute Left risk measure.

Description

Function to compute Left risk measure.

Usage

LR_cp(data, FT, ov)

Arguments

data

A (T x N) matrix or data.frame containing the N time series over period T
FT

Window size.
ov

umber of different days for two consecutive time windows.

Value

A (N x T) data.frame containing the LR_cp measure for the N time series over time windows

Examples

LR <- LR_cp(sample.data, FT= 1290, ov = 125)
df <- as.data.frame(matrix(unlist(LR), nrow = length(LR), ncol = ncol(sample.data)))
colnames(df) <- c(paste("tw", rep(1:16)))
plot(df[,1], pch=19, col=asset.label$label, ylab="LR_cp", xlab="ETFs")

Plot methods for a LR_cp object

Description

Plot methods for a LR_cp object

Usage

plot_LR_cp(LR_cp_measure, asset_label)

Arguments

LR_cp_measure

object of class LR_cp
asset_label

vector containing asset label

Value

plot of LR_cp measures by asset classes

Plot methods for a ptf_construction object

Description

Plot methods for a ptf_construction object

Usage

plot_ptf(ptf.oos.values)

Arguments

ptf.oos.values

object of class ptf_construction

Value

plot oos portfolio values

Function to compute portfolio values

Description

Function to compute portfolio values

Usage

ptf_construction(
  data,
  FT,
  ov,
  LR_cp_measure,
  ptf = c("M", "C_M", "EDC", "C_EDC")
)

Arguments

data

A (T x N) matrix or data.frame containing the N time series over period T
FT

Window size.
ov

Overlap.
LR_cp_measure

object of class LR_cp (only for "C_M" and "C_EDC" asset allocation strategies)
ptf

Type of portfolio to be computed. Asset allocation strategies available are: "M" is the Markowitz portfolio, "C_M" is the combined Markowitz portfolio, "EDC" uses the extreme downside correlation and "C_EDC" is the combined extreme downside correlation portfolio

Value

An object of class "ptf_construction" is a list containing the following components for all the time windows considered:

ptf oos value

a vector of out of sample returns.

weigths

portfolio weights.

Data for infoset function

Description

Contains daily prices of ETFs

Usage

sample.data

Format

A data frame with 3174 rows and 44 columns

Source

Created in-house to serve as an example

Examples

data(sample.data)

Data with time points for portfolio construction using the LR_cp measure

Description

Contains daily prices of ETFs

Usage

sample.data.ts

Format

A data frame with 3175 rows and 45 columns

Source

Created in-house to serve as an example

Examples

data(sample.data.ts)

Plot methods for a ptf_construction object

Description

Plot methods for a ptf_construction object

Usage

summary_ptf(ptf.oos.values)

Arguments

ptf.oos.values

object of class ptf_construction

Value

summary of oos portfolio values

Function to find the most-left distribution set.

Description

An adaptive clustering algorithm identifies sub-groups of gross returns at each iteration by approximating their distribution with a sequence of two-component log-normal mixtures.

Usage

tail_mixture(y, shift, n_it, plot)

Arguments

y

vector or data frame
shift

double
n_it

integer
plot

option

Value

data object