# Load selected data

data <- read.csv2("IBEX.csv")
data <- read.csv2("CAC.csv") 
data <- read.csv2("DAX.csv") 

head(data)
dim(data)

# Parameter estimations

val <- as.matrix(data[,2]) 
n <- nrow(val)

val.ln <- -diff(log(val)) # Factor risk changes

# Empirical TVaR calculation

# 95% condifence level
VaR95 <- quantile(val.ln,0.95, type=1)

val.ln_pos95<-c()
for(i in 1:length(val.ln)) val.ln_pos95<-cbind(val.ln_pos95,max(0,val.ln[i]-VaR95))

ES95<-mean(val.ln_pos95)
TVaR95 <- VaR95+(1/(1-0.95))*ES95
round(TVaR95,4)

# 99% condifence level
VaR99 <- quantile(val.ln,0.99,type=1)

val.ln_pos99<-c()
for(i in 1:length(val.ln)) val.ln_pos99<-cbind(val.ln_pos99,max(0,val.ln[i]-VaR99))

ES99<-mean(val.ln_pos99)
TVaR99 <- VaR99+(1/(1-0.99))*ES99
round(TVaR99,4)


# 99.5% condifence level
VaR99.5 <- quantile(val.ln,0.995,type=1)

val.ln_pos99.5<-c()
for(i in 1:length(val.ln)) val.ln_pos99.5<-cbind(val.ln_pos99.5,max(0,val.ln[i]-VaR99.5))

ES99.5<-mean(val.ln_pos99.5)
TVaR99.5 <- VaR99.5+(1/(1-0.995))*ES99.5
round(TVaR99.5,4)