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out_of_sample_MCS_and_prelim_volmodels.R
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out_of_sample_MCS_and_prelim_volmodels.R
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##########out of sample volatility models###########
###################################################################################
#
#
# OUT OF SAMPLE QLIKE LOSSES
#
#
###################################################################################
QLikes_DRD <- readRDS("Qlikes_Forecast_DRDmodels_intraday.rds")
QLikes_RM <- readRDS("Qlikes_riskmetrics.rds")
QLikes_rBG <- readRDS("Qlikes_rBG.rds")
QLikes_crBG <- readRDS("Qlikes_crBG.rds")
QLikes_crDCC <- readRDS("Qlikes_crDCC.rds")
QLikes_rDCC <- readRDS("Qlikes_rDCC.rds")
colmeanQLikes_DRD <- lapply(QLikes_DRD, FUN = function(x) colMeans(x))
colmeanQLikes_RM <- lapply(QLikes_RM, FUN = function(x) colMeans(x))
colmeanQLikes_rBG <- lapply(QLikes_rBG, FUN = function(x) colMeans(x))
colmeanQLikes_crBG <- colMeans(QLikes_crBG, na.rm = T)
colmeanQLikes_crDCC <- colMeans(QLikes_crDCC, na.rm = T)
colmeanQLikes_rDCC <- lapply(QLikes_rDCC, FUN = function(x) if(!is.null(x)){colMeans(x, na.rm =T)})
#Minute frequency:
DRD_HAR_Rcov_min <- colmeanQLikes_DRD[[1]][7]
DRD_all_minute <- lapply(colmeanQLikes_DRD, FUN = function(x) mean(x[6:9])/DRD_HAR_Rcov_min)
RM_all_minute <- lapply(colmeanQLikes_RM, FUN = function(x) mean(x[6:9])/DRD_HAR_Rcov_min)
rBG_all_minute <- lapply(colmeanQLikes_rBG, FUN = function(x) mean(x[6:9])/DRD_HAR_Rcov_min)
rBG_all_minute_2 <- lapply(colmeanQLikes_rBG, FUN = function(x) mean(x[6:7])/DRD_HAR_Rcov_min)
rBG_all_minute[[4]] <- rBG_all_minute_2[[4]]
rBG_all_minute[[5]] <- rBG_all_minute_2[[5]]
rBG_all_minute[[6]] <- rBG_all_minute_2[[6]]
rBG_all_minute
crBG_all_minute <- mean(colmeanQLikes_crBG[6:9]/DRD_HAR_Rcov_min)
crDCC_all_minute <- mean(colmeanQLikes_crDCC[6:9]/DRD_HAR_Rcov_min)
rDCC_all_minute <- lapply(colmeanQLikes_rDCC, FUN = function(x) if(!is.null(x)){mean(x[6:9], na.rm =T)/DRD_HAR_Rcov_min})
#second frequency
DRD_all_second <- lapply(colmeanQLikes_DRD, FUN = function(x) mean(x[1:5])/DRD_HAR_Rcov_min)
RM_all_second <- lapply(colmeanQLikes_RM, FUN = function(x) mean(x[1:5])/DRD_HAR_Rcov_min)
rBG_all_second <- lapply(colmeanQLikes_rBG, FUN = function(x) mean(x[1:5])/DRD_HAR_Rcov_min)
crBG_all_second <- mean(colmeanQLikes_crBG[1:5]/DRD_HAR_Rcov_min)
crDCC_all_second <- mean(colmeanQLikes_crDCC[1:5]/DRD_HAR_Rcov_min)
#have zeros in first column
rDCC_all_second <- lapply(colmeanQLikes_rDCC, FUN = function(x) if(!is.null(x)){mean(x[2:5], na.rm =T)/DRD_HAR_Rcov_min})
###################################################################################
#
#
# OUT-OF-SAMPLE STRATIFIED LOSSES
#
#
###################################################################################
######## GETTING THE ERROR VARIANCES FOR THE HARQ MODEL.
########## JUST DO AS IN THE ORIGINAL ARTICLE AND USE RQ INSTEAD OR ERROR VARIANCES
EV_TLT <- list()
errorvariance_TLT <- matrix(0L, ncol = 9, nrow = 2516)
EV_SPY <- list()
errorvariance_SPY <- matrix(0L, ncol = 9, nrow = 2516)
for(k in 1:3){
for(j in 1:9){
for(i in (window+1):2516){
QCchoice <- c(1,7,8)
ICchoice <- c(4,5,6)
#############################################################################################
#
#
# PROXIES
#
#
#############################################################################################
RV5min_TLT <- calccov[[1]][[7]][1,1,(i-window):(i-1)] * 10000
RV5min_SPY <- calccov[[1]][[7]][2,2,(i-window):(i-1)] * 10000
RV5min_TLT <- RV5min_TLT[22:length(RV5min_TLT)]
RV5min_SPY <- RV5min_SPY[22:length(RV5min_SPY)]
#proxy covariance for qlike losses
RCov5min <- calccov[[1]][[7]][,,i] * 10000
proxycor <- proxycorrelation[2,1, (i-window):(i-1)]
#---------------------------------------------------------------------------------------------
#############################################################################################
#
#
# PRELIMINARIES (LAGS AND QUARTICITIES)
#
#
#############################################################################################
#Lags
vol_TLT <- matrix((calccov[[QCchoice[k]]][[j]][1,1,(i-window):(i-1)])) * 10000
volday_TLT <- vol_TLT
volweek_TLT <- rowMeans(cbind(vol_TLT, mlag(vol_TLT,4,mean(vol_TLT))))
volmonth_TLT <- rowMeans(cbind(vol_TLT, mlag(vol_TLT,21,mean(vol_TLT))))
vol_TLT <- vol_TLT[22:length(vol_TLT)]
volday_TLT <- volday_TLT[22:length(volday_TLT)]
volweek_TLT <- volweek_TLT[22:length(volweek_TLT)]
volmonth_TLT <- volmonth_TLT[22:length(volmonth_TLT)]
vol_SPY <- matrix((calccov[[QCchoice[k]]][[j]][2,2,(i-window):(i-1)])) * 10000
volday_SPY <- vol_SPY
volweek_SPY <- rowMeans(cbind(vol_SPY, mlag(vol_SPY,4,mean(vol_SPY))))
volmonth_SPY <- rowMeans(cbind(vol_SPY, mlag(vol_SPY,21,mean(vol_SPY))))
vol_SPY <- vol_SPY[22:length(vol_SPY)]
volday_SPY <- volday_SPY[22:length(volday_SPY)]
volweek_SPY <- volweek_SPY[22:length(volweek_SPY)]
volmonth_SPY <- volmonth_SPY[22:length(volmonth_SPY)]
rq_TLT <- Quarticities$rq_TLT[(i-window):(i-1),j]
rq_SPY <- Quarticities$rq_SPY[(i-window):(i-1),j]
rq_SPY <- rq_SPY[22:length(rq_SPY)]
rq_TLT <- rq_TLT[22:length(rq_TLT)]
rq_TLT[rq_TLT>10] <- mean(rq_TLT)
rq_SPY[rq_SPY>10] <- mean(rq_SPY)
rq_TLT <- matrix(rq_TLT)
rq_SPY <- matrix(rq_SPY)
sqrtrq_TLT <- sqrt(rq_TLT) - mean(sqrt(rq_TLT))
sqrtrq_SPY <- sqrt(rq_SPY) - mean(sqrt(rq_SPY))
correlation <- Correlations_measures[[j]][(i-window):(i-1), QCchoice[k]]
#############################################################################################
#
#
# DRD-HARQ MODEL (RCOV, MRC, MRK)
#
#
#############################################################################################
HARQ_TLT <- lm(RV5min_TLT[2:end] ~ volday_TLT[1:(end-1)] + volweek_TLT[1:(end-1)] + volmonth_TLT[1:(end-1)] + I(volday_TLT[1:(end-1)] * sqrtrq_TLT[1:(end-1)]))
hhat_HARQ_TLT <- cbind(1, volday_TLT[end], volweek_TLT[end], volmonth_TLT[end],volday_TLT[end] * sqrtrq_TLT[end]) %*% matrix(coef(HARQ_TLT))
HARQ_SPY <- lm(RV5min_SPY[2:end] ~ volday_SPY[1:(end-1)] + volweek_SPY[1:(end-1)] + volmonth_SPY[1:(end-1)]
+ I(volday_SPY[1:(end-1)] * sqrtrq_SPY[1:(end-1)]))
hhat_HARQ_SPY <- cbind(1, volday_SPY[end], volweek_SPY[end], volmonth_SPY[end],volday_SPY[end] * sqrtrq_SPY[end]) %*% matrix(coef(HARQ_SPY))
hhat_HARQ_TLT <- volatility.insanity.filter(hhat_HARQ_TLT, min(RV5min_TLT), max(RV5min_TLT), mean(RV5min_TLT))$vol
hhat_HARQ_SPY <- volatility.insanity.filter(hhat_HARQ_SPY, min(RV5min_SPY), max(RV5min_SPY), mean(RV5min_SPY))$vol
errorvariance_TLT[i,j] <- coef(HARQ_TLT)[2] + coef(HARQ_TLT)[5] * sqrtrq_TLT[end]
errorvariance_SPY[i,j] <- coef(HARQ_SPY)[2] + coef(HARQ_SPY)[5] * sqrtrq_SPY[end]
}
print(sprintf("%s", j))
}
EV_TLT[[k]] <- errorvariance_TLT[1001:2516, ]
EV_SPY[[k]] <- errorvariance_SPY[1001:2516, ]
}
frob.norm <- function(Covar){
frob <- sqrt(sum(diag(t(Covar)%*%Covar)))
return(frob)
}
Quarticities <- readRDS("Quarticity_estimators.rds")
#Remember its error variances and not only quarticities. So get the params of your model.
qmat <- array(0L, dim = c(2,2,2516))
trqmat <- array(0L, dim = c(2,2,2516))
Qmats <- list()
TRQmats <- list()
for(j in 1:9){
for(i in 1:2516){
tltq <- Quarticities$rq_TLT[i,j]
tltq[tltq>30] <- mean(Quarticities$rq_TLT[,j])
spyq <- Quarticities$rq_SPY[i,j]
spyq[spyq>30] <- mean(Quarticities$rq_SPY[,j])
tlttrq <- Quarticities$trq_TLT[i,j]
tlttrq[tlttrq>30] <- mean(Quarticities$trq_TLT[,j])
spytrq <- Quarticities$trq_SPY[i,j]
spytrq[spytrq>30] <- mean(Quarticities$trq_SPY[,j])
tempq <- matrix(c(tltq, 0,0, spyq), ncol = 2, nrow = 2)
temptrq <- matrix(c(tlttrq, 0, 0, spytrq), nrow = 2, ncol = 2)
qmat[,,i] <- tempq
trqmat[,,i] <- temptrq
}
Qmats[[j]] <- qmat
TRQmats[[j]] <- trqmat
}
frobloss.Qmats <- list()
frobs <- numeric()
frobloss.TRQmats <- list()
frobs2 <- numeric()
for(j in 1:9){
for(i in 1:2516){
frobs[i] <- frob.norm(Qmats[[j]][,,i])
frobs2[i] <- frob.norm(TRQmats[[j]][,,i])
}
frobloss.Qmats[[j]] <- frobs
frobloss.TRQmats[[j]] <- frobs2
}
frobloss.Qmats <- lapply(frobloss.Qmats, function(x) x[1001:2516])
frobloss.TRQmats <- lapply(frobloss.TRQmats, function(x) x[1001:2516])
findHighQs <- lapply(frobloss.Qmats, function(x) which(x > quantile(x, 0.95)))
findLowQs <- lapply(frobloss.Qmats, function(x) which(x <= quantile(x, 0.95)))
findHighTRQs <- lapply(frobloss.TRQmats, function(x) which(x > quantile(x, 0.95)))
findLowTRQs <- lapply(frobloss.TRQmats, function(x) which(x <= quantile(x, 0.95)))
#Finding Qlikes for the days of high and low quarticities.
QLikes_DRD_highQs <- list()
tempq <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_DRD_lowQs <- list()
tempq2 <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_DRD_highTRQs <- list()
temptrq <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_DRD_lowTRQs <- list()
temptrq2 <- matrix(0L, ncol = 9, nrow = 1516)
#RM
QLikes_RM_highQs <- list()
tempqRM <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_RM_lowQs <- list()
tempq2RM <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_RM_highTRQs <- list()
temptrqRM <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_RM_lowTRQs <- list()
temptrq2RM <- matrix(0L, ncol = 9, nrow = 1516)
#rBG
QLikes_rBG_highQs <- list()
tempqrBG <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_rBG_lowQs <- list()
tempq2rBG <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_rBG_highTRQs <- list()
temptrqrBG <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_rBG_lowTRQs <- list()
temptrq2rBG <- matrix(0L, ncol = 9, nrow = 1516)
#rDCC
QLikes_rDCC_highQs <- list()
tempqrDCC <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_rDCC_lowQs <- list()
tempq2rDCC <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_rDCC_highTRQs <- list()
temptrqrDCC <- matrix(0L, ncol = 9, nrow = 1516)
QLikes_rDCC_lowTRQs <- list()
temptrq2rDCC <- matrix(0L, ncol = 9, nrow = 1516)
#crDCC
tempqcrDCC <- matrix(0L, ncol = 9, nrow = 76)
tempq2crDCC <- matrix(0L, ncol = 9, nrow = 1440)
temptrqcrDCC <- matrix(0L, ncol = 9, nrow = 76)
temptrq2crDCC <- matrix(0L, ncol = 9, nrow = 1440)
#crBG
tempqcrBG <- matrix(0L, ncol = 9, nrow = 76)
tempq2crBG <- matrix(0L, ncol = 9, nrow = 1440)
temptrqcrBG <- matrix(0L, ncol = 9, nrow = 76)
temptrq2crBG <- matrix(0L, ncol = 9, nrow = 1440)
for(k in 1:22){
for(j in 1:9){
tempq[1:76,j] <- QLikes_DRD[[k]][findHighQs[[j]],j]
tempq2[1:1440,j] <- QLikes_DRD[[k]][findLowQs[[j]],j]
temptrq[1:76,j] <- QLikes_DRD[[k]][findHighTRQs[[j]],j]
temptrq2[1:1440,j] <- QLikes_DRD[[k]][findLowTRQs[[j]],j]
}
QLikes_DRD_highQs[[k]] <- tempq[1:76, ]
QLikes_DRD_lowQs[[k]] <- tempq2[1:1440, ]
QLikes_DRD_highTRQs[[k]] <- temptrq[1:76, ]
QLikes_DRD_lowTRQs[[k]] <- temptrq2[1:1440, ]
}
#RM
for(k in 1:6){
for(j in 1:9){
tempqRM[1:76,j] <- QLikes_RM[[k]][findHighQs[[j]],j]
tempq2RM[1:1440,j] <- QLikes_RM[[k]][findLowQs[[j]],j]
temptrqRM[1:76,j] <- QLikes_RM[[k]][findHighTRQs[[j]],j]
temptrq2RM[1:1440,j] <- QLikes_RM[[k]][findLowTRQs[[j]],j]
}
QLikes_RM_highQs[[k]] <- tempqRM[1:76, ]
QLikes_RM_lowQs[[k]] <- tempq2RM[1:1440, ]
QLikes_RM_highTRQs[[k]] <- temptrqRM[1:76, ]
QLikes_RM_lowTRQs[[k]] <- temptrq2RM[1:1440, ]
}
#rBG
#for k > 3 j >7 is invalid.
for(k in 1:6){
if(k<=3){
for(j in 1:9){
tempqrBG[1:76,j] <- QLikes_rBG[[k]][findHighQs[[j]],j]
tempq2rBG[1:1440,j] <- QLikes_rBG[[k]][findLowQs[[j]],j]
temptrqrBG[1:76,j] <- QLikes_rBG[[k]][findHighTRQs[[j]],j]
temptrq2rBG[1:1440,j] <- QLikes_rBG[[k]][findLowTRQs[[j]],j]
}
}
else{
for(j in 1:7){
tempqrBG[1:76,j] <- QLikes_rBG[[k]][findHighQs[[j]],j]
tempq2rBG[1:1440,j] <- QLikes_rBG[[k]][findLowQs[[j]],j]
temptrqrBG[1:76,j] <- QLikes_rBG[[k]][findHighTRQs[[j]],j]
temptrq2rBG[1:1440,j] <- QLikes_rBG[[k]][findLowTRQs[[j]],j]
}
}
QLikes_rBG_highQs[[k]] <- tempqrBG[1:76, ]
QLikes_rBG_lowQs[[k]] <- tempq2rBG[1:1440, ]
QLikes_rBG_highTRQs[[k]] <- temptrqrBG[1:76, ]
QLikes_rBG_lowTRQs[[k]] <- temptrq2rBG[1:1440, ]
}
#rDCC
for(k in 1:2){
for(j in 1:9){
tempqrDCC[1:76,j] <- QLikes_rDCC[[k]][findHighQs[[j]],j]
tempq2rDCC[1:1440,j] <- QLikes_rDCC[[k]][findLowQs[[j]],j]
temptrqrDCC[1:76,j] <- QLikes_rDCC[[k]][findHighTRQs[[j]],j]
temptrq2rDCC[1:1440,j] <- QLikes_rDCC[[k]][findLowTRQs[[j]],j]
}
QLikes_rDCC_highQs[[k]] <- tempqrDCC[1:76, ]
QLikes_rDCC_lowQs[[k]] <- tempq2rDCC[1:1440, ]
QLikes_rDCC_highTRQs[[k]] <- temptrqrDCC[1:76, ]
QLikes_rDCC_lowTRQs[[k]] <- temptrq2rDCC[1:1440, ]
}
#only goes to 1001 ......
for(k in 5:6){
for(j in 1:7){
tempqrDCC[1:length(which(findHighQs[[j]]<1001)),j] <- QLikes_rDCC[[k]][ findHighQs[[j]][which(findHighQs[[j]]<1001)] ,j]
tempq2rDCC[1:length(which(findLowQs[[j]]<1001)),j] <- QLikes_rDCC[[k]][findLowQs[[j]][which(findLowQs[[j]]<1001)],j]
temptrqrDCC[1:length(which(findHighTRQs[[j]]<1001)),j] <- QLikes_rDCC[[k]][findHighTRQs[[j]][which(findHighTRQs[[j]]<1001)],j]
temptrq2rDCC[1:length(which(findLowTRQs[[j]]<1001)),j] <- QLikes_rDCC[[k]][findLowTRQs[[j]][which(findLowTRQs[[j]]<1001)],j]
}
QLikes_rDCC_highQs[[k]] <- tempqrDCC
QLikes_rDCC_lowQs[[k]] <- tempq2rDCC
QLikes_rDCC_highTRQs[[k]] <- temptrqrDCC
QLikes_rDCC_lowTRQs[[k]] <- temptrq2rDCC
}
for(j in 1:9){
tempqcrDCC[1:76,j] <- QLikes_crDCC[findHighQs[[j]],j]
tempq2crDCC[1:1440,j] <- QLikes_crDCC[findLowQs[[j]],j]
temptrqcrDCC[1:76,j] <- QLikes_crDCC[findHighTRQs[[j]],j]
temptrq2crDCC[1:1440,j] <- QLikes_crDCC[findLowTRQs[[j]],j]
tempqcrBG[1:76,j] <- QLikes_crBG[findHighQs[[j]],j]
tempq2crBG[1:1440,j] <- QLikes_crBG[findLowQs[[j]],j]
temptrqcrBG[1:76,j] <- QLikes_crBG[findHighTRQs[[j]],j]
temptrq2crBG[1:1440,j] <- QLikes_crBG[findLowTRQs[[j]],j]
}
#################### HIGH RQ ###############
library(rugarch)
DRD_highQs <- do.call(cbind, QLikes_DRD_highQs)
RM_highQs <- do.call(cbind, QLikes_RM_highQs)
rBG_highQs <- do.call(cbind, QLikes_rBG_highQs[1:3])
rBG_highQs <- cbind(rBG_highQs, QLikes_rBG_highQs[[4]][,1:7], QLikes_rBG_highQs[[5]][,1:7], QLikes_rBG_highQs[[6]][,1:7])
#rDCC_highQs dont care, just write qlikes down but no mcs
crBG_highQs <- tempqcrBG[,1:8]
crDCC_highQs <- tempqcrDCC[,1:8]
highQs_all <- cbind(DRD_highQs, RM_highQs, rBG_highQs, crBG_highQs, crDCC_highQs)
mcs_highQs <- mcsTest(highQs_all, 0.05, nboot = 1000, nblock = 10, boot = c("stationary"))
sort(mcs_highQs$includedR)
duplicated(t(highQs_all))
#saveRDS(mcs_highQs, "mcs_highQs.rds")
ncol(DRD_highQs) + ncol(RM_highQs)
#minutes
DRD_highQs_minutes <- lapply(QLikes_DRD_highQs, function(x) mean(colMeans(x[,6:9])))
DRD_highs_minutes_HAR <- mean(unlist(DRD_highQs_minutes[1:3]))
DRD_highs_minutes_SHAR <- mean(unlist(DRD_highQs_minutes[16]))
DRD_highs_minutes_HARQ <- mean(unlist(DRD_highQs_minutes[4:6]))
DRD_highs_minutes_CHAR <- mean(unlist(DRD_highQs_minutes[17:19]))
DRD_highs_minutes_CHARQ <- mean(unlist(DRD_highQs_minutes[20:22]))
#seconds
DRD_highQs_seconds <- lapply(QLikes_DRD_highQs, function(x) mean(colMeans(x[,1:5])))
DRD_highs_seconds_HAR <- mean(unlist(DRD_highQs_seconds[1:3]))
DRD_highs_seconds_SHAR <- mean(unlist(DRD_highQs_seconds[16]))
DRD_highs_seconds_HARQ <- mean(unlist(DRD_highQs_seconds[4:6]))
DRD_highs_seconds_CHAR <- mean(unlist(DRD_highQs_seconds[17:19]))
DRD_highs_seconds_CHARQ <- mean(unlist(DRD_highQs_seconds[20:22]))
#RM
RM_highQs_minutes <- lapply(QLikes_RM_highQs, function(x) mean(colMeans(x[,6:9])))
RM_highQs_minutes <- mean(unlist(RM_highQs_minutes))
RM_highQs_seconds <- lapply(QLikes_RM_highQs, function(x) mean(colMeans(x[,1:5])))
RM_highQs_seconds <- mean(unlist(RM_highQs_seconds)[c(1,4,5,6)])
#rBG
rBG_minutes <- lapply(QLikes_rBG_highQs, function(x) colMeans(x[, 6:9]))
rBG_minutes <- mean(unlist(rBG_minutes))
rBG_seconds <- lapply(QLikes_rBG_highQs, function(x) colMeans(x[, 1:5]))
rBG_seconds <- mean(unlist(rBG_seconds))
#crBG
mean(colMeans(crBG_highQs)[6:8])
mean(colMeans(crBG_highQs)[1:5])
#crDCC
mean(colMeans(crDCC_highQs)[6:8])
mean(colMeans(crDCC_highQs)[1:5])
#rDCC
mean(c(colMeans(QLikes_rDCC_highQs[[1]])[6:9], colMeans(QLikes_rDCC_highQs[[2]])[6:9],
colMeans(QLikes_rDCC_highQs[[5]][1:76, 6:7]),colMeans(QLikes_rDCC_highQs[[6]][1:76, 6:7])))
mean(c(colMeans(QLikes_rDCC_highQs[[1]])[2:5],colMeans(QLikes_rDCC_highQs[[2]])[2:5],
mean(QLikes_rDCC_highQs[[5]][1:45, 1]), mean(QLikes_rDCC_highQs[[6]][1:45, 1]), colMeans(QLikes_rDCC_highQs[[5]][1:76, 2:5]),
colMeans(QLikes_rDCC_highQs[[6]][1:76, 1:5])))
colMeans(DRD_highQs)
colMeans(RM_highQs)
colMeans(rBG_highQs)
colMeans(QLikes_rDCC_highQs[[1]])
colMeans(QLikes_rDCC_highQs[[2]])
mean(QLikes_rDCC_highQs[[5]][1:45, 1])
colMeans(QLikes_rDCC_highQs[[5]][1:76, ])
mean(QLikes_rDCC_highQs[[6]][1:45, 1])
colMeans(QLikes_rDCC_highQs[[6]][1:76, ])
colMeans(crBG_highQs)
colMeans(crDCC_highQs)
################# HIGH TRQ ##################
DRD_highTRQs <- do.call(cbind, QLikes_DRD_highTRQs)
RM_highTRQs <- do.call(cbind, QLikes_RM_highTRQs)
rBG_highTRQs <- do.call(cbind, QLikes_rBG_highTRQs[1:3])
rBG_highTRQs <- cbind(rBG_highTRQs, QLikes_rBG_highTRQs[[4]][,1:7], QLikes_rBG_highTRQs[[5]][,1:7], QLikes_rBG_highTRQs[[6]][,1:7])
crBG_highTRQs <- temptrqcrBG
crDCC_highTRQs <- temptrqcrDCC
highTRQs_all <- cbind(DRD_highTRQs, RM_highTRQs, rBG_highTRQs, crBG_highTRQs, crDCC_highTRQs)
mcs_highTRQs <- mcsTest(highTRQs_all, 0.05, nboot = 1000, nblock = 10, boot = c("stationary"))
colMeans(DRD_highTRQs)
colMeans(RM_highTRQs)
colMeans(rBG_highTRQs)
colMeans(QLikes_rDCC_highTRQs[[1]])
colMeans(QLikes_rDCC_highTRQs[[2]])
mean(QLikes_rDCC_highTRQs[[5]][1:45, 1])
colMeans(QLikes_rDCC_highTRQs[[5]][1:76, ])
mean(QLikes_rDCC_highTRQs[[6]][1:45, 1])
colMeans(QLikes_rDCC_highTRQs[[6]][1:76, ])
colMeans(crBG_highTRQs)
colMeans(crDCC_highTRQs)
#################### LOW RQ ###############
DRD_lowQs <- do.call(cbind, QLikes_DRD_lowQs)
RM_lowQs <- do.call(cbind, QLikes_RM_lowQs)
rBG_lowQs <- do.call(cbind, QLikes_rBG_lowQs[1:3])
rBG_lowQs <- cbind(rBG_lowQs, QLikes_rBG_lowQs[[4]][,1:7], QLikes_rBG_lowQs[[5]][,1:7], QLikes_rBG_lowQs[[6]][,1:7])
#rDCC_lowQs dont care, just write qlikes down but no mcs
crBG_lowQs <- tempq2crBG[,1:8]
crDCC_lowQs <- tempq2crDCC[,1:8]
lowQs_all <- cbind(DRD_lowQs, RM_lowQs, rBG_lowQs, crBG_lowQs, crDCC_lowQs)
duplicated(t(lowQs_all))
mcs_lowQs <- mcsTest(lowQs_all, 0.05, nboot = 2000, nblock = 10, boot = c("stationary"))
sort(mcs_lowQs$includedR)
#saveRDS(mcs_lowQs, "mcs_lowQs.rds")
#minutes
DRD_lowQs_minutes <- lapply(QLikes_DRD_lowQs, function(x) mean(colMeans(x[,6:9])))
DRD_lows_minutes_HAR <- mean(unlist(DRD_lowQs_minutes[1:3]))
DRD_lows_minutes_SHAR <- mean(unlist(DRD_lowQs_minutes[16]))
DRD_lows_minutes_HARQ <- mean(unlist(DRD_lowQs_minutes[4:6]))
DRD_lows_minutes_CHAR <- mean(unlist(DRD_lowQs_minutes[17:19]))
DRD_lows_minutes_CHARQ <- mean(unlist(DRD_lowQs_minutes[20:22]))
#seconds
DRD_lowQs_seconds <- lapply(QLikes_DRD_lowQs, function(x) mean(colMeans(x[,1:5])))
DRD_lows_seconds_HAR <- mean(unlist(DRD_lowQs_seconds[1:3]))
DRD_lows_seconds_SHAR <- mean(unlist(DRD_lowQs_seconds[16]))
DRD_lows_seconds_HARQ <- mean(unlist(DRD_lowQs_seconds[4:6]))
DRD_lows_seconds_CHAR <- mean(unlist(DRD_lowQs_seconds[17:19]))
DRD_lows_seconds_CHARQ <- mean(unlist(DRD_lowQs_seconds[20:22]))
#RM
RM_lowQs_minutes <- lapply(QLikes_RM_lowQs, function(x) mean(colMeans(x[,6:9])))
RM_lowQs_minutes <- mean(unlist(RM_lowQs_minutes)[c(1,4,5,6)])
RM_lowQs_seconds <- lapply(QLikes_RM_lowQs, function(x) mean(colMeans(x[,1:5])))
RM_lowQs_seconds <- mean(unlist(RM_lowQs_seconds)[c(1,4,5,6)])
#rBG
rBG_minutes <- lapply(QLikes_rBG_lowQs, function(x) colMeans(x[, 6:9]))
rBG_minutes <- mean(unlist(rBG_minutes))
rBG_seconds <- lapply(QLikes_rBG_lowQs, function(x) colMeans(x[, 1:5]))
rBG_seconds <- mean(unlist(rBG_seconds))
#crBG
mean(colMeans(crBG_lowQs)[6:8])
mean(colMeans(crBG_lowQs)[1:5])
#crDCC
mean(colMeans(crDCC_lowQs)[6:8])
mean(colMeans(crDCC_lowQs)[1:5])
#rDCC
mean(c(colMeans(QLikes_rDCC_lowQs[[1]])[6:9], colMeans(QLikes_rDCC_lowQs[[2]])[6:9],
colMeans(QLikes_rDCC_lowQs[[5]][1:76, 6:7]),colMeans(QLikes_rDCC_lowQs[[6]][1:76, 6:7])))
mean(c(colMeans(QLikes_rDCC_lowQs[[1]])[2:5],colMeans(QLikes_rDCC_lowQs[[2]])[2:5],
mean(QLikes_rDCC_lowQs[[5]][1:45, 1]), mean(QLikes_rDCC_lowQs[[6]][1:45, 1]), colMeans(QLikes_rDCC_lowQs[[5]][1:76, 2:5]),
colMeans(QLikes_rDCC_lowQs[[6]][1:76, 1:5])))
################# LOW TRQ ##################
DRD_lowTRQs <- do.call(cbind, QLikes_DRD_lowTRQs)
RM_lowTRQs <- do.call(cbind, QLikes_RM_lowTRQs)
rBG_lowTRQs <- do.call(cbind, QLikes_rBG_lowTRQs)
crBG_lowTRQs <- temptrq2crBG
crDCC_lowTRQs <- temptrq2crDCC
lowTRQs_all <- cbind(DRD_lowTRQs, RM_lowTRQs, rBG_lowTRQs, crBG_lowTRQs, crDCC_lowTRQs)
mcs_lowTRQs <- mcsTest(lowTRQs_all, 0.05, nboot = 1000, nblock = 10, boot = c("stationary"))
colMeans(DRD_lowTRQs)
colMeans(RM_lowTRQs)
colMeans(rBG_lowTRQs)
colMeans(QLikes_rDCC_lowTRQs[[1]])
colMeans(QLikes_rDCC_lowTRQs[[2]])
mean(QLikes_rDCC_lowTRQs[[5]][1:45, 1])
colMeans(QLikes_rDCC_lowTRQs[[5]][1:76, ])
mean(QLikes_rDCC_lowTRQs[[6]][1:45, 1])
colMeans(QLikes_rDCC_lowTRQs[[6]][1:76, ])
colMeans(crBG_lowQs)
colMeans(crDCC_lowQs)
###################################################################################
#
#
# MCS PROCEDURE
#
#
###################################################################################
#replacing NA with unconditional mean
rdcc6matrix <- matrix(colMeans(QLikes_rDCC[[6]]), ncol = 7, nrow = 515, byrow = T)
rdcc5matrix <- matrix(colMeans(QLikes_rDCC[[5]]), ncol = 7, nrow = 515, byrow = T)
temprdcc <- list(QLikes_rDCC[[1]], QLikes_rDCC[[2]][,-c(1:2)], rbind(QLikes_rDCC[[5]],rdcc5matrix), rbind(QLikes_rDCC[[6]],rdcc6matrix))
library(rugarch)
crBG_models <- QLikes_crBG
crBG_models[836:1516, 9] <- mean(crBG_models[,9], na.rm = T)
crDCC_models <- QLikes_crDCC
crDCC_models[835:1516, 9] <- mean(crDCC_models[,9], na.rm = T)
#constructing loss matrix.
DRD_models <- do.call(cbind, QLikes_DRD)
RM_models <- do.call(cbind, QLikes_RM)
rBG_models <- do.call(cbind, QLikes_rBG)
rDCC_models <- do.call(cbind, temprdcc)[, -c(1,10)]
loss_matrix <- cbind(DRD_models, RM_models, rBG_models, rDCC_models, crBG_models, crDCC_models)
#TO BE USED WITHIN MCS PROCEDURE OF SHEPPARD!
#write.table(loss_matrix, file="lossmatrix.csv")
ncol(loss_matrix)
#skipped: DRD = daily (8), RM = daily (2), rBG = daily (2), rBG = misses last two rows of PBPCov, MRC and MRK (6)
#crBG = daily (1), crDCC = daily (1), rDCC = daily (2), misses BPCov and PBPCov (18) and 2 rows in TCov,MRC,MRK (6) and
#missing 1 and 10 (2).
#so the total on the models: 346 + 8 + 2 + 2 + 6 + 1 + 1+ 2 + 18 + 6 + 2 = 394 (CORRECT)!
ncol(DRD_models) #+ ncol(RM_models) + ncol(rBG_models)
library(tictoc)
tic()
mcs_realized <- mcsTest(loss_matrix, 0.05, nboot = 1000, nblock = 10, boot = c("stationary"))
toc()
mcs_realized <- readRDS("MCS_results_volmodels.rds")
#saveRDS(mcs_realized, "MCS_results_volmodels.rds")
#The last integer in $includedSQ or $includedR gives you the one with highest probability in MCS procedure.
superiorlosses <- colMeans(loss_matrix[, mcs_realized$includedSQ])
superiorlosses
length(mcs_realized$includedR)
mcs_realized$includedSQ
mcs_realized$includedR
#HARQF-MRK-5SEC
mean(QLikes_DRD[[9]][,2])
###################################################################################
#
#
# MINVAR MCS PROCEDURE
#
#
###################################################################################
test <- t(matrix(c(0.1, 0.9))) %*% (calccov[[1]][[7]][,,1] *10000)%*% matrix(c(0.1, 0.9))
minvar_RM <- readRDS("mvpvariance_RM.rds")
minvar_DRD <- readRDS("mvpvariance_DRD.rds")
minvar_rBG <- readRDS("insample_rBGminvar.rds")
minvar_rDCC <- readRDS("insample_rDCCminvar.rds")
losses_DRD <- do.call(cbind, minvar_DRD)
losses_RM <- do.call(cbind, minvar_RM)
losses_rBG <- do.call(cbind, minvar_rBG)
losses_rDCC <- do.call(cbind, minvar_rDCC)
temprBG <- losses_rBG[1000:2515,-c(10, 19, 28, 42)]
temprDCC <- losses_rDCC[1000:2515,-c(10, 19, 28, 42) ]
loss_matrix_minvar <- cbind(losses_DRD, losses_RM, temprBG, temprDCC) #1.15
ncol(losses_DRD) + ncol(losses_RM)
loss_matrix_minvar <- loss_matrix_minvar[,-c(284,328)] * 1e-4 #downscaled by 1e-4 so we dont work with percentage variance.
#write.table(loss_matrix_minvar, "loss_matrix_minvar.csv")
ncol(losses_DRD) + ncol(losses_RM)
duplicated(t(loss_matrix_minvar))
ncol(loss_matrix_minvar)
mcs_realized_minvar <- mcsTest(loss_matrix_minvar, 0.05, nboot = 1000, nblock = 10, boot = c("stationary"))
#saveRDS(mcs_realized_minvar, "MCS_results_minvar_volmodels.rds")
mcs_realized_minvar <- readRDS("MCS_results_minvar_volmodels.rds")
#if you want to construct tables you can just do ascend since p-vals does not matter. Only which model are contained within.
sortedinclusions <- sort(mcs_realized_minvar$includedSQ)
sortedinclusions
length(sortedinclusions)
colMeans(loss_matrix_minvar[, mcs_realized_minvar$includedSQ]) * 10000
#insamples since I lost out of sample covariances.....
#However, insample still performs much worse than har models. So this only helps
#narrow the superior set down.
rBG_minvar <- list()
rDCC_minvar <- list()
temp_rBG_minvar <- matrix(0L, nrow = 2516, ncol = 7)
temp_rDCC_minvar <- matrix(0L, nrow = 2516, ncol = 7)
for(k in 5:6){
for(j in 2:7){
choice <- c(1,4,5,6,7,8)
rBGpars <- rBG$vPar
rDCCpars <- rDCC$vPar
rBG <- EstimateBivarGARCH(dailyretotc * 100, calccov[[choice[k]]][[j]] * 10000, tol = 1e-5, vPar = rBGpars)
rDCC <- Estimate_rDCC(dailyretotc * 100, calccov[[choice[k]]][[j]] * 10000, getDates, tol = 1e-5, vPar = rDCCpars)
for(i in 1:2515){
weights <- minvar(rBG$vSigma2[,,i])
weightsrDCC <- minvar(rDCC$vSigma2[,,i])
temp_rBG_minvar[i, j] <- t(weights) %*% (calccov[[1]][[7]][,,i+1] * 10000) %*% weights
temp_rDCC_minvar[i, j] <- t(weightsrDCC) %*% (calccov[[1]][[7]][,,i+1] * 10000) %*% weightsrDCC
}
print(sprintf("%s", j))
}
rBG_minvar[[k]] <- temp_rBG_minvar
rDCC_minvar[[k]] <- temp_rDCC_minvar
}
#saveRDS(rBG_minvar, "insample_rBGminvar.rds")
#saveRDS(rDCC_minvar, "insample_rDCCminvar.rds")
###################################################################################
#
#
# DRD-HAR(Q)-RCOV PLOTS OVER A TWO WEEK PERIOD
#
#
###################################################################################
library(xts)
library(matlib)
source("Previous functions/projectfunctions.R")
source("functions.R")
mergedfrequencies <- readRDS("mergedfrequencies.rds")
calccov <- readRDS("calculatedcovariances.rds")
dataTLT <- readRDS("dataTLT.rds")
getDates <- unlist(lapply(dataTLT, function(x) as.character(index(x[1]))))
for(i in 1:length(getDates)){
getDates[i] <- strsplit(getDates, " ")[[i]][1]
}
getDates[1415:1429]
ts.plot(calccov[[1]][[7]][2,2,1415:1429] * 10000)
proxycorrelation <- array(0L, dim = c(2,2,2516))
for(i in 1:2516){
proxycorrelation[,,i] <- realCov(mergedfrequencies[[7]][[i]]*100, T)
}
library(xts)
library(highfrequency)
library(matlib)
library(MCS)
library(Rsolnp)
library(matrixcalc)
library(MASS)
library(Matrix)
Correlations_measures <- readRDS("Correlations_measures.rds")
Quarticities <- readRDS("Quarticity_estimators.rds")
#SKETCH
sigmas_HAR <- array(0L, dim = c(2,2,2516))
sigmas_HARQ <- array(0L, dim = c(2,2,2516))
harq_params <- matrix(0L, ncol=2, nrow = 2516)
window <- 1000
for(i in (window+1):2516){
#############################################################################################
#
#
# PROXIES
#
#
#############################################################################################
RV5min_TLT <- calccov[[1]][[7]][1,1,(i-window):(i-1)] * 10000
RV5min_SPY <- calccov[[1]][[7]][2,2,(i-window):(i-1)] * 10000
RV5min_TLT <- RV5min_TLT[22:length(RV5min_TLT)]
RV5min_SPY <- RV5min_SPY[22:length(RV5min_SPY)]
#proxy covariance for qlike losses
RCov5min <- calccov[[1]][[7]][,,i] * 10000
proxycor <- proxycorrelation[2,1, (i-window):(i-1)]
#---------------------------------------------------------------------------------------------
#############################################################################################
#
#
# PRELIMINARIES (LAGS AND QUARTICITIES)
#
#
#############################################################################################
#Lags
vol_TLT <- matrix((calccov[[7]][[2]][1,1,(i-window):(i-1)])) * 10000
volday_TLT <- vol_TLT
volweek_TLT <- rowMeans(cbind(vol_TLT, mlag(vol_TLT,4,mean(vol_TLT))))
volmonth_TLT <- rowMeans(cbind(vol_TLT, mlag(vol_TLT,21,mean(vol_TLT))))
vol_TLT <- vol_TLT[22:length(vol_TLT)]
volday_TLT <- volday_TLT[22:length(volday_TLT)]
volweek_TLT <- volweek_TLT[22:length(volweek_TLT)]
volmonth_TLT <- volmonth_TLT[22:length(volmonth_TLT)]
vol_SPY <- matrix((calccov[[7]][[2]][2,2,(i-window):(i-1)])) * 10000
volday_SPY <- vol_SPY
volweek_SPY <- rowMeans(cbind(vol_SPY, mlag(vol_SPY,4,mean(vol_SPY))))
volmonth_SPY <- rowMeans(cbind(vol_SPY, mlag(vol_SPY,21,mean(vol_SPY))))
vol_SPY <- vol_SPY[22:length(vol_SPY)]
volday_SPY <- volday_SPY[22:length(volday_SPY)]
volweek_SPY <- volweek_SPY[22:length(volweek_SPY)]
volmonth_SPY <- volmonth_SPY[22:length(volmonth_SPY)]
rq_TLT <- Quarticities$rq_TLT[(i-window):(i-1),2]
rq_SPY <- Quarticities$rq_SPY[(i-window):(i-1),2]
rq_SPY <- rq_SPY[22:length(rq_SPY)]
rq_TLT <- rq_TLT[22:length(rq_TLT)]
rq_TLT[rq_TLT>10] <- mean(rq_TLT)
rq_SPY[rq_SPY>10] <- mean(rq_SPY)
rq_TLT <- matrix(rq_TLT)
rq_SPY <- matrix(rq_SPY)
sqrtrq_TLT <- sqrt(rq_TLT) - mean(sqrt(rq_TLT))
sqrtrq_SPY <- sqrt(rq_SPY) - mean(sqrt(rq_SPY))
#############################################################################################
#
#
# DRD-HAR MODEL (RCOV, MRC, MRK)
#
#
#############################################################################################
correlation <- Correlations_measures[[2]][(i-window):(i-1), 7]
end <- length(RV5min_TLT)
HAR_TLT <- lm(RV5min_TLT[2:end] ~ volday_TLT[1:(end-1)] + volweek_TLT[1:(end-1)] + volmonth_TLT[1:(end-1)])
hhat_HAR_TLT <- cbind(1, volday_TLT[end], volweek_TLT[end], volmonth_TLT[end]) %*% matrix(coef(HAR_TLT))
HAR_SPY <- lm(RV5min_SPY[2:end] ~ volday_SPY[1:(end-1)] + volweek_SPY[1:(end-1)] + volmonth_SPY[1:(end-1)])
hhat_HAR_SPY <- cbind(1, volday_SPY[end], volweek_SPY[end], volmonth_SPY[end]) %*% matrix(coef(HAR_SPY))
hhat_HAR_TLT <- volatility.insanity.filter(hhat_HAR_TLT, min(RV5min_TLT), max(RV5min_TLT), mean(RV5min_TLT))$vol
hhat_HAR_SPY <- volatility.insanity.filter(hhat_HAR_SPY, min(RV5min_SPY), max(RV5min_SPY), mean(RV5min_SPY))$vol
DRD_HAR <- suppressWarnings(EstimatecorrHAR(cbind(hhat_HAR_TLT, hhat_HAR_SPY),
correlation = correlation, proxy = proxycor, 0, T))