inst/scripts/M/P2_fig_10/4_Plot.R

In LAJordanger/localgaussSpec: Local Gaussian Spectral Analysis

###----------------------------------------------------------------###
##  The cGARCH-example from P2_fig_10.

##  This script contains the code needed in order to reproduce the
##  plot that shows the pseudo-normalised version of the multivariate
##  cGARCH-model that was fitted to the log-returns of the
##  'EuStockMarkets'-data, with focus on the variables DAX and CAC.
##  It does not depend on any local Gaussian correlations, which is
##  the reason there is no "2_Data.R"-file in this folder.

###----------------------------------------------------------------###

##  Load the required libraries.

library(localgaussSpec) 
library(rmgarch)
library(ggplot2)
library(grid)

###----------------------------------------------------------------###

## Access the 'EuStockMarkets'-data from the 'datasets'-pakcage.

data(EuStockMarkets)

##  Compute the daily log-returns, since they are the ones used in the
##  calculations.

.first <- head(EuStockMarkets, n = -1)
.second <- tail(EuStockMarkets, n = -1)
.TS <- log(.second/.first)
rm(.first, .second)

##  Specify the model (using the default marginals).

uspec <- rugarch::ugarchspec(
                      mean.model = list(armaOrder = c(0,0)),
                      variance.model = list(garchOrder = c(1,1), model = "sGARCH"), 
                      distribution.model = "norm")
uspec <- rugarch::multispec(replicate(n = dim(.TS)[2],
                                      expr = uspec))

##  Simulate 'nr_samples' samples of length 'N' from the time series
##  corresponding to 'TS_key', and save it into the file-hierarchy.

nr_samples <- 1
N <- dim(.TS)[1] ## = 1859
TS_key <- "rmgarch"

.seed_for_sample <- 245
set.seed(.seed_for_sample)

##  Generate the sample.

.TS_sample <- TS_sample(
    TS_key = TS_key,
    N = N,
    nr_samples = nr_samples,
    uspec = uspec,
    data = .TS,
    rseed = NULL)

##  And extract the compoent of interest for the plot.

.TS <- .TS_sample$TS[1, , ]

rm(nr_samples, N, .seed_for_sample, uspec, .TS_sample)

##  Update the dimension-names with DAX and CAC, so the same code can
##  be used here as was used in P2_fig_07.

dimnames(.TS)$variables <- dimnames(EuStockMarkets)[[2]]

###----------------------------------------------------------------###

##  Extract the desired comonents, and then replace them with their
##  pseudo-normalised observations.

Y1_DAX <-  local({
    .tmp <- .TS[, "DAX"]
    qnorm(p = (rank(.tmp) - 0.5) /length(.tmp))
})
Y3_CAC <-  local({
    .tmp <- .TS[, "CAC"]
    qnorm(p = (rank(.tmp) - 0.5) /length(.tmp))
})
rm(.TS)


###----------------------------------------------------------------###

##  Create a plot to present the DAX-values.

DAX_plot <- ggplot(
    data = data.frame(
        x = seq_along(Y1_DAX),
        y = Y1_DAX),
    mapping = aes(x = x, y = y)) + 
    geom_line(
        mapping = aes(x = x, y = y),
        lwd = 0.1,
        alpha = 0.6) + 
    theme(axis.title.x = element_blank(),
          axis.title.y = element_blank()) +
    annotate(geom = "text",
             x = -Inf,
             y = Inf,
             size = 3,
             label = "cGARCH, Y1",
             col = "brown",
             alpha = 1,
             vjust = 1.3,
             hjust = -0.1) +
    theme(axis.ticks = element_line(linewidth = 0.25),
          axis.ticks.length = unit(.04, "cm"),
          axis.text = element_text(size = 4.5))  +
    ##  Add information about the levels of interest later on,
    ##  i.e. the 10%, 50% and 90% percentiles of the standard normal
    ##  distribution.
    geom_hline(yintercept = qnorm(p = c(0.1, 0.5, 0.9)),
               linetype = 2,
               col = "brown",
               alpha = 0.8,
               lwd = 0.1)
    
##  Create a plot to present the CAC-values.

CAC_plot <- ggplot(
    data = data.frame(
        x = seq_along(Y3_CAC),
        y = Y3_CAC),
    mapping = aes(x = x, y = y)) + 
    geom_line(
        mapping = aes(x = x, y = y),
        lwd = 0.1,
        alpha = 0.6) + 
    theme(axis.title.x = element_blank(),
          axis.title.y = element_blank()) +
    annotate(geom = "text",
             x = -Inf,
             y = Inf,
             size = 3,
             label = "cGARCH, Y3",
             col = "brown",
             alpha = 1,
             vjust = 1.3,
             hjust = -0.1) +
    theme(axis.ticks = element_line(linewidth = 0.25),
          axis.ticks.length = unit(.04, "cm"),
          axis.text = element_text(size = 4.5))  +
    ##  Add information about the levels of interest later on,
    ##  i.e. the 10%, 50% and 90% percentiles of the standard normal
    ##  distribution.
    geom_hline(yintercept = qnorm(p = c(0.1, 0.5, 0.9)),
               linetype = 2,
               col = "brown",
               alpha = 0.8,
               lwd = 0.1)
    
##  This plot is shrinked a lot later on, and then the tiny `lwd`
##  gives a decent view.

###----------------------------------------------------------------###

##  Create the desired grid of plots, and save this grid to disk.
##  Note: It is only after having saved the result to a file, that the
##  effect of the size-arguments for the text can be properly
##  investigated.

##  WARNING: For this particular example the file will be saved in the
##  active working directory of R.

.save_file <- "P2_fig_10.pdf"

pdf(file = .save_file) 

grid.newpage()
pushViewport(viewport(
    layout = grid.layout(8, 1)))
print(DAX_plot,
      vp = viewport(
          layout.pos.row = 1,
          layout.pos.col = 1))
print(CAC_plot,
      vp = viewport(
          layout.pos.row = 2,
          layout.pos.col = 1))

dev.off()

##  Crop the result.  This approach requires that 'pdfcrop' is
##  available on the system.

.crop_code <- sprintf("pdfcrop --margins 0 %s %s", .save_file, .save_file)
system(.crop_code)
rm(.crop_code, .save_file)