Descriptive.Analysis <- function(DGP=1:15, mets=7, res=2, replic=1000)
{
################################################################################
#                                                                              #
#      Descriptive Analysis Function by Breno de Andrade Pinheiro Néri         #
#                                                                              #
#            brenoneri@gmail.com        www.fgv.br/aluno/bneri                 #
#                                                                              #
#                                                    October, 2005 - Version 3 #
#                                                                              #
################################################################################
#                                                                              #
# This code computes descritive statistics to help you to analyze the results  #
# of the Monte Carlo Simulations generated by MC_VaR.R or MC_VaRox.R.          #
#                                                                              #
# You enter the DGP range (DGP, observe that it is a vector), the number of    #
# methodologies generated in your Monte Carlo Simulations (mets), the desired  #
# test statistics (res) and the number                                         #
# of replications generated in your MC Simulations (replic).                   #
#                                                                              #
# You can choose among eleven test statistics generated by the code            #
# BackTest.R, setting up the res variable. Namely:                             #
# 1: frequency of violations;                                                  #
# 2: number of violations (default);                                           #
# 3: ---Not Available---;                                                      #
# 4: Unconditional Coverage (Kupiec, 1995) Likelihood Ratio Test;              #
# 5: Independence (Christoffersen, 1997) Likelihood Ratio Test;                #
# 6: Conditional Coverage (Christoffersen, 1997) Likelihood Ratio Test;        #
# 7: Dynamic Quantile (Engle and Manganelli, 2002) Test Statistic;             #
# 8: Unconditional Coverage (Kupiec, 1995) P-Value;                            #
# 9: Independence (Christoffersen, 1997) P-Value;                              #
# 10: Conditional Coverage (Christoffersen, 1997) P-Value;                     #
# 11: Dynamic Quantile (Engle and Manganelli, 2002) P-Value;                   #
# 12: Magnitude Loss Function (Lopez, 1998).                                   #
#                                                                              #
# Defaults: DGP=1:15, mets=7, res=2, replic=1000.                              #
#                                                                              #
################################################################################
#                                                                              #
# You may use this code only if you accept the conditions:                     #
# (1) I am not liable for any problem caused by this code (i.e. you use it at  #
# your own risk);                                                              #
# (2) You must give me credit in your papers where this code has been used.    #
#                                                                              #
################################################################################

library(fBasics)
library(tseries)
for(j in DGP)
{
    x <- array(0, c(replic, mets))
    for(i in 1:replic)
    {
        load(paste("MC_VaR", j, i, "R", sep="."))
        x[i,] <- result[[res]]
    }
    for(met in 1:mets)
    {
        message("")
        message("VaR methodology ", met, " under DGP ", j, ":")
        message("")
        message("Mean: ", mean(x[,met]))
        message("Median: ", median(x[,met]))
        message("Variance: ", var(x[,met]))
        message("Min: ", min(x[,met]))
        message("Max: ", max(x[,met]))
        message("Range: ", max(x[,met])-min(x[,met]))
        message("Skewness: ", skewness(x[,met]))
        message("Excess Kurtosis: ", kurtosis(x[,met]))
        message("P-Value of the Jarque-Bera Test for Normality: ", jarque.bera.test(x[,met])[[3]])
        message("")
    }
    message("")
}
}