CVaR und VaR Code added

rm(list=ls())

# Pakete laden

library("quantmod")

library("lubridate")

library("zoo")

# Symbol 

symbols <- c("^GDAXI", "^GSPC","^FCHI","^FTSE") # DAX, S&P500, CAC40 Indizes

start_date <- Sys.Date() - years(20)

log.ret.series <- data.frame()

for(symbol in symbols){

  # Daten von Yahoo abrufen

  getSymbols(symbol, src = "yahoo", from = start_date, periodicity = "daily")

  # Adjusted Close Preise extrahieren

  prices <- Ad(get(gsub("\\^","",symbol)))

  prices <- na.locf(prices) # Fehlende Werte mit dem letzten Wert auffüllen

  # Plot the prices

  plot(prices, main = paste("Adjusted Close Prices of", symbol), col = "blue", lwd = 2)

  log.ret <- na.omit(diff(log(prices))) # logarithmische Renditen

  log.ret.series <- cbind(log.ret, log.ret.series) # Renditen in Dataframe speichern

  plot.zoo(log.ret, 

           main = paste("Log. Returns of Adj. Close Prices of the", symbol),

           xlab = "Time",

           ylab = "Log. Returns",

           col = "red", 

           type="l",

           lwd = 2

  )

  #VaR

  VaR <- quantile(log.ret, probs = 0.01,na.rm=TRUE)# Value at Risk (VaR) bei 1% Quantil

  # Horizontale Linie beim VaR

  abline(h = VaR, col = "blue", lty = 2, lwd = 2) # Horizontale Linie beim VaR

  df <- cbind(log.ret, VaR) # Renditen und VaR zusammen anzeigen)

  names(df) <- c("log.ret", "VaR") # Spaltennamen setzen

  df$less_than_VaR <- ifelse(df$log.ret < df$VaR, 1, 0) # 1 wenn log.ret < VaR, sonst 0

  CVaR <- mean(df[df$less_than_VaR == 1,"log.ret"]) # Durchschnitt der Renditen, die kleiner als VaR sind

  cat(paste0("---------------------------",symbol,"---------------------------\n",

             "Anzahl der Renditen:\t\t\t", nrow(df),"\n",

             "Anzahl der Renditen kleiner als VaR:\t", sum(df$less_than_VaR),"\n",

             "Value at Risk (VaR) bei 1% Quantil:\t", round(VaR, 4),"\n",

             "Conditional Value at Risk (CVaR):\t", round(CVaR, 4),"\n\n"

  ))

}

colors <- c("red", "blue", "green", "purple") # Farben für die Plots

# Plot der Dichte der logarithmischen Renditen

plot(density(log.ret.series[,1], na.rm = TRUE), 

     main = "Density of Logarithmic Returns",

     xlab = "Logarithmic Returns",

     ylab = "Density",

     xlim = c(-0.15, -0),

     ylim = c(0, 2),

     col = colors[1], 

     lwd = 2

)

for(ii in 1:ncol(log.ret.series)){

  density_x <- density(log.ret.series[,ii], na.rm = TRUE)

  lines(density_x, col = colors[ii], lwd = 2)

}