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\name{fAssets-package}
\alias{fAssets-package}
\alias{fAssets}
\docType{package}
\title{Analysing and Modelling Financial Assets}
\description{
The Rmetrics \code{fAssets} package is a collection of functions
to manage, to investigate and to analyze data sets of financial
assets from different points of view.
}
\details{
\tabular{ll}{
Package: \tab fAssets\cr
Type: \tab Package\cr
Date: \tab 2014\cr
License: \tab GPL Version 2 or later\cr
Copyright: \tab (c) 1999-2014 Rmetrics Association\cr
Repository: \tab R-FORGE\cr
URL: \tab \url{https://www.rmetrics.org}
}
}
\keyword{models}
\section{1 Introduction}{
The package \code{fAssets} was written to explore and investigate
data sets of financial asssets
Included are functions to make the the asset selection process easier,
to robustify return and covariances for modeling portfolios, to test
financial returns for multivariate normality, and to measure in a
simple way performance and risk of funds and portfolios.
Beside this many functions for graphs and plots, and for a more
sophisticated explorative data analysis are provided. They range
from simple time series plots to more elaborated statisitical
chart tools:
histogram, density, boxplots, and QQ plots;
pairs,similaries, and covarinace ellipses plots;
star plots, and risk/reward graphs.
}
\section{2 Assets Selection}{
The assets selection chapter containts functions which arrange assets
from a data set according to different measaures applying ideas from
principal component analysis, from hierarchical clustering, or by a
user defined statistical measure:
% assets-arrange.R
\preformatted{
assetsArrange Rearranges the columns in a data set of assets
pcaArrange Returns PCA correlation ordered column names
hclustArrange Returns hierarchical clustered column names
abcArrange Returns assets sorted by column names
orderArrange Returns assets ordered by column names
sampleArrange Returns a re-sampled set of assets
statsArrange Returns statistically rearranged column names
}
In addition we have summarized and bundle of distance measure functions
to determine the similarity or dissimilarity of individual assets from
a set of multivariate financial return series.
% assets-distance.R
\preformatted{
assetsDist Computes the distances between assets
corDist Returns correlation distance measure
kendallDist Returns kendalls correlation distance measure
spearmanDist Returns spearmans correlation distance measure
mutinfoDist Returns mutual information distance measure
euclideanDist Returns Euclidean distance measure
maximumDist Returns maximum distance measure
manhattanDist Returns Manhattan distance measure
canberraDist Returns Canberra distance measure
binaryDist Returns binary distance measure
minkowskiDist Returns Minkowsky distance measure
braycurtisDist Returns Bray Curtis distance measure
mahalanobisDist Returns Mahalanobis distance measure
jaccardDist Returns Jaccard distance mesaure
sorensenDist Returns Sorensen distance measure
}
A last group of functions allows to select assets by concepts from
hierarchical or k-means clustering:
% assets-selection.R
\preformatted{
assetsSelect Selects similar or dissimilar assets
.hclustSelect Selects due to hierarchical clustering
.kmeansSelect Selects due to k-means clustering
}
}
\section{3 Assets Covariance Robustification}{
We provide several functions to compute robust measures for mean and/or
covariance estimates which can be used for example in robustified
Markowitz portfolio Optimization.
% assets-meancov.R
\preformatted{
assetsMeanCov Estimates mean and variance for a set of assets
.covMeanCov uses sample covariance estimation
.mveMeanCov uses "cov.mve" from [MASS]
.mcdMeanCov uses "cov.mcd" from [MASS]
.studentMeanCov uses "cov.trob" from [MASS]
.MCDMeanCov requires "covMcd" from [robustbase]
.OGKMeanCov requires "covOGK" from [robustbase]
.nnveMeanCov uses builtin from [covRobust]
.shrinkMeanCov uses builtin from [corpcor]
.baggedMeanCov uses builtin from [corpcor]
.arwMeanCov uses builtin from [mvoutlier]
.donostahMeanCov uses builtin from [robust]
.bayesSteinMeanCov uses builtin from Alexios Ghalanos
.ledoitWolfMeanCov uses builtin from [tawny]
.rmtMeanCov uses builtin from [tawny]
}
An additional function allows to detect outliers from a PCA outlier
analysis.
% assets-outliers.R
\preformatted{
assetsOutliers Detects outliers in multivariate assets sets
}
}
\section{4 Testing Assets for Normality}{
The multivariate Shapiro test and the E-Statistic Energy Test
allow to test multivariate Normality of financial returns.
% assets-testing.R
\preformatted{
assetsTest Tests for multivariate Normal Assets
mvshapiroTest Multivariate Shapiro Test
mvenergyTest Multivariate E-Statistic (Energy) Test
}
}
\section{5 Lower Partial Moments Measures}{
The computation of Lower partial moments is done by the following
two functions:
% assets-lpm.R
\preformatted{
assetsLPM Computes asymmetric lower partial moments
assetsSLPM Computes symmetric lower partial moments
}
}
\section{6 Assets Time Series and Density Plot Functions}{
Dozens of tailored plot functions are included in the \code{fAssets}
package. This makes it very easy to visualize properties and to
perform an explorative data analysis. Starting from simple time
series functions.
% plotSeries.R
\preformatted{
assetsReturnPlot Displays time series of individual assets
assetsCumulatedPlot Displays time series of individual assets
assetsSeriesPlot Displays time series of individual assets
}
we can also explore the distributional properties of the returns by
histogram, density, boxplots, and QQ Plots:
% plot-hist.R | plot-binning.R | % plot-boxplot.R | % plot-qqplot.R
\preformatted{
assetsHistPlot Displays a histograms of a single asset
assetsLogDensityPlot Displays a pdf plot on logarithmic scale
assetsHistPairsPlot Displays a bivariate histogram plot
assetsBoxPlot Displays a standard box plot
assetsBoxPercentilePlot Displays a side-by-side box-percentile plot
assetsQQNormPlot Displays normal qq-plots of individual assets
}
}
\section{7 Assets Dependency and Structure Plot Functions}{
Corellation and similarities are another source of information about
the dependence structure of individual financial returns. The functions
which help us to detect those properties in data sets of financial
assets include:
% plot-pairs.R and plot-panels.R | plot-similaries.R | plot-ellipses.R
\preformatted{
assetsPairsPlot Displays pairs of scatterplots of assets
assetsCorgramPlot Displays pairwise correlations between assets
assetsCorTestPlot Displays and tests pairwise correlations
assetsCorImagePlot Displays an image plot of a correlations
covEllipsesPlot Displays a covariance ellipses plot
assetsDendrogramPlot Displays hierarchical clustering dendrogram
assetsCorEigenPlot Displays ratio of the largest two eigenvalues
}
Beside correlations und dependencies also risk/reward graphs give
additional insight into the structure of assets.
% plot-risk.R
\preformatted{
assetsRiskReturnPlot Displays risk-return diagram of assets
assetsNIGShapeTrianglePlot Displays NIG Shape Triangle
assetsTreePlot Displays a minimum spanning tree of assets
}
Statistic visualized by star plots is a very appealing tool for
characterization and classification of assets by eye:
% plot-stars.R
\preformatted{
assetsStarsPlot Draws segment/star diagrams of asset sets
assetsBasicStatsPlot Displays a segment plot of basic return stats
assetsMomentsPlot Displays a segment plot of distribution moments
assetsBoxStatsPlot Displays a segment plot of box plot statistics
assetsNIGFitPlot Displays a segment plot NIG parameter estimates
}
}
\section{About Rmetrics:}{
The \code{fAssets} Rmetrics package is written for educational
support in teaching "Computational Finance and Financial Engineering"
and licensed under the GPL.
}
\keyword{package}
|
