W2CWM2C (version 2.2)

WMCC: Wavelet multiple cross-correlation (multivariate case).

Description

The WMCC function (multivariate case) computes the wavelet multiple cross correlation by means of the function wave.multiple.cross.correlation from the wavemulcor package (Fernandez-Macho 2012b) and present the result as a novel plot that reduce the number of plots of the classical function wave.multiple.cross.correlation. The WMCC plot output can be displayed in the screen (by default) or can be saved as PNG, JPG, PDF or EPS. The WMCC function also provides a way to handle multivariate time series easily as a list of N elements (time series).

Usage

WMCC(inputDATA, Wname, J, lmax, device="screen", filename,
     Hfig, WFig, Hpdf, Wpdf)

Arguments

inputDATA

An array of multivariate time series as a ts object (please, check the ts manual to get more information about the ts function in R).

Wname

The wavelet function or filter to use in the decomposition.

J

Specifies the depth of the decomposition.

lmax

The maximum lag.

device

The type of the output device (by default the option is ``screen'', and the other options are ``jpg'', ``png'', ``eps'' and ``pdf'').

filename

The output filename.

Hfig

The height of the 'jpg' or 'png' image.

WFig

The width of the 'jpg' or 'png' image.

Hpdf

The height of the eps or pdf.

Wpdf

The width of the eps or pdf.

Value

Output: Output plot: screen or 'filename + .png, .jpg, .eps or .pdf'.

Output data: The same list of elements of the function wave.multiple.cross.correlation of the wavemulcor package (Fernandez-Macho 2012b).

Details

The WMCC function compute the wavelet multiple cross correlation using the function wave.multiple.cross.correlation from the wavemulcor package (Fernandez-Macho 2012b), but the WMCC function incorporates some graphical improvements (please, look at Figure 7 in Polanco-Martinez and Fernandez-Macho 2014), such as the reduction of the number of plots to present the results of the function wave.multiple.cross.correlation.

References

Fernandez-Macho, J. (2012a). Wavelet multiple correlation and cross-correlation: A multiscale analysis of euro zone stock markets. Physica A: Statistical Mechanics and its Applications, 391(4):1097--1104. 10.1016/j.physa.2011.11.002.

Fernandez-Macho, J. (2012b). wavemulcor: Wavelet routine for multiple correlation. R package version 1.2, The Comprehensive R Archive Network (CRAN), <URL: https://cran.r-project.org/package=wavemulcor>.

Ihaka, R., Murrell, P., Hornik, K., Fisher, J. C. and Zeileis, A. (2012). colorspace: Color Space Manipulation. R package version 1.2.0, The Comprehensive R Archive Network (CRAN), <URL: https://cran.r-project.org/package=colorspace>.

Polanco-Martinez, J. and J. Fernandez-Macho (2014). The package 'W2CWM2C': description, features and applications. Computing in Science & Engineering, 16(6):68--78. 10.1109/MCSE.2014.96.

Examples

# NOT RUN {
 library("colorspace")
 library("wavemulcor")
 library("W2CWM2C")
 data(dataexample)

 #:: Figure 7 (Polanco-Martinez and Fernandez-Macho (2014).

 #:: Transform log returns using: ln(t + deltat) - ln(t). 
 #:: The application in this example uses stock market 
 #:: indexes (it is common to use log returns instead of
 #:: raw data). Other kinds of pre-processing data are possible. 

 dataexample  <- dataexample[-1] #remove the dates!
 lrdatex      <- apply(log(dataexample), 2, diff)
 inputDATA    <- ts(lrdatex, start=1, frequency=1)

 Wname     <- "la8"
 J         <- 8
 lmax      <- 30
 compWCC   <- WMCC(inputDATA, Wname, J, lmax, device="screen", NULL,
                   NULL, NULL, NULL, NULL)
# }