RPEGLMEN

install.packages("RPEGLMEN", dependencies = TRUE)

library(devtools)
devtools::install_github("AnthonyChristidis/RPEGLMEN")

# Sample Code

# Load the package
library(RPEGLMEN)

# Function to return the periodogram of data series
myperiodogram <- function (data, max.freq = 0.5, twosided = FALSE, keep = 1){
  data.fft <- fft(data)
  N <- length(data)
  tmp <- Mod(data.fft[2:floor(N/2)])^2/N
  tmp <- sapply(tmp, function(x) max(1e-05, x))
  freq <- ((1:(floor(N/2) - 1))/N)
  tmp <- tmp[1:floor(length(tmp) * keep)]
  freq <- freq[1:floor(length(freq) * keep)]
  if (twosided) {
    tmp <- c(rev(tmp), tmp)
    freq <- c(-rev(freq), freq)
  }
  return(list(spec <- tmp, freq <- freq))
}

# Function to compute the standard error based the periodogram of the influence functions time series
SE.Gamma <- function(data, d = 7, alpha = 0.5, keep = 1, exponential.dist = TRUE){
  N<-length(data)
  # Compute the periodograms
  my.periodogram <- myperiodogram(data)
  my.freq <- my.periodogram$freq
  my.periodogram <- my.periodogram$spec
  # Remove values of frequency 0 as it does not contain information about the variance
  my.freq <- my.freq[-1]
  my.periodogram <- my.periodogram[-1]
  # Implement cut-off
  nfreq <- length(my.freq)
  my.freq <- my.freq[1:floor(nfreq*keep)]
  my.periodogram <- my.periodogram[1:floor(nfreq*keep)]
  # GLM with BFGS optimization
  # Create 1, x, x^2, ..., x^d
  x.mat <- rep(1,length(my.freq))
  for(col.iter in 1:d){
    x.mat <- cbind(x.mat,my.freq^col.iter)
  }
  # Fit the Exponential or Gamma model
  if(exponential.dist)
    res <- glmnet_exp(x.mat, my.periodogram, alpha.EN = alpha) else
      res <- fit.glmGammaNet(x.mat, my.periodogram, alpha.EN = alpha)
  # Return the estimated variance
  return(sqrt(exp(res[1])/N))
}

# Loading hedge fund data from PA
data(edhec, package <- "PerformanceAnalytics")
colnames(edhec)

# Computing the expected shortfall for the time series of returns
library(RPEIF)
test.mat <- apply(edhec, 2, IF.ES)
test.mat <- apply(test.mat, 2, as.numeric)

# Returning the standard errors from the Exponential distribution fit
apply(test.mat, 2, SE.Gamma, exponential.dist = TRUE)