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Summarystats.R
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source("Previous functions/projectfunctions.R")
source("functions.R")
library(xts)
library(highfrequency)
library(matlib)
library(MCS)
dataTLT <- readRDS("dataTLT.rds")
dataSPY <- readRDS("dataSPY.rds")
#need sparse sampled data for bandwidth selection.
getDates <- unlist(lapply(dataTLT, function(x) as.character(index(x[1]))))
for(i in 1:length(getDates)){
getDates[i] <- strsplit(getDates, " ")[[i]][1]
}
sparseTLT20min <- list()
sparseSPY20min <- list()
for(i in 1:length(dataTLT)){
sparseTLT20min[[i]] <- aggregatets(dataTLT[[i]], on = "minutes", k = 20)
sparseSPY20min[[i]] <- aggregatets(dataSPY[[i]], on = "minutes", k = 20)
}
for(i in 1:length(dataTLT)){
sparseTLT20min[[i]] <- diff(log(sparseTLT20min[[i]]))[-1]
sparseSPY20min[[i]] <- diff(log(sparseSPY20min[[i]]))[-1]
}
#--------------------------------------------------
secs <-c(1,5,15,20,30,60,300,900,1800)
frequenciesTLT <- list()
frequenciesSPY <- list()
tempTLT <- list()
tempSPY <- list()
for(j in 1:length(secs)){
for(i in 1:length(dataTLT)){
tempTLT[[i]] <- aggregatets(dataTLT[[i]], on="seconds", k=secs[j])
tempSPY[[i]] <- aggregatets(dataSPY[[i]], on="seconds", k=secs[j])
}
frequenciesTLT[[j]] <- tempTLT
frequenciesSPY[[j]] <- tempSPY
print(sprintf("%s",j))
}
#For bandwidth procedure.
HbandwithfreqTLT <- list()
HbandwithfreqSPY <- list()
for(i in 1:length(dataTLT)){
HbandwithfreqTLT[[i]] <- diff(log(frequenciesTLT[[3]][[i]]))[-1]
HbandwithfreqSPY[[i]] <- diff(log(frequenciesSPY[[3]][[i]]))[-1]
}
# percentage log-returns to construct percentage daily averages.
for(j in 1:length(secs)){
for(i in 1:length(dataTLT)){
frequenciesTLT[[j]][[i]] <- diff(log(frequenciesTLT[[j]][[i]]))[-1] * 100
frequenciesSPY[[j]][[i]] <- diff(log(frequenciesSPY[[j]][[i]]))[-1] * 100
}
}
opentocloseTLT <- list()
opentocloseSPY <- list()
for(i in 1:length(dataTLT)){
opentocloseTLT[[i]] <- dataTLT[[i]][c(1,length(dataTLT[[i]]))]
opentocloseSPY[[i]] <- dataSPY[[i]][c(1,length(dataSPY[[i]]))]
}
for(i in 1:length(dataTLT)){
opentocloseTLT[[i]] <- diff(log(opentocloseTLT[[i]]))[-1] * 100
opentocloseSPY[[i]] <- diff(log(opentocloseSPY[[i]]))[-1] * 100
}
mergedopentoclose <- list()
for(i in 1:length(dataTLT)){
mergedopentoclose[[i]] <- cbind(opentocloseTLT[[i]], opentocloseSPY[[i]])
}
#mergedopentoclose <- lapply(mergedopentoclose, function(x) x[c(1,nrow(x))])
mergedopentoclose <- lapply(mergedopentoclose, function(x) colSums(x, na.rm = T))
mergedopentoclose <- lapply(mergedopentoclose, function(x) matrix(x, nrow=1, ncol=2, byrow = T))
for(i in 1:length(dataTLT)){
mergedopentoclose[[i]] <- xts(mergedopentoclose[[i]], order.by = as.Date(getDates[i]))
}
#merging list elements.
mergedfrequencies <- list()
tempmerged <- list()
for(j in 1:length(secs)){
for(i in 1:length(dataTLT)){
tempmerged[[i]] <- na.omit(cbind(frequenciesTLT[[j]][[i]], frequenciesSPY[[j]][[i]]))
}
mergedfrequencies[[j]] <- tempmerged
}
#saveRDS(mergedfrequencies, file = "mergedfrequencies.rds")
#--------------------------------------------------SUMMARY STATISTICS------------------------------------------
#finding optimal bandwidth for MRK:
H <- cbind(bandwidthH(HbandwithfreqTLT,sparseTLT20min), bandwidthH(HbandwithfreqSPY,sparseSPY20min))
H <- rowMeans(H)
library(ggplot2)
ggplot() + geom_line(aes(as.Date(getDates), H, group = 1)) + geom_hline(yintercept = mean(H), col="steelblue", lwd=1)
#We do it on preferred sampling schemes.
tt <- lapply(mergedfrequencies[[7]], function(x) x*100)
RCov_5min <- lapply(mergedfrequencies[[7]], function(x) realCov(x))
BPCov_5min <- lapply(mergedfrequencies[[7]], function(x) preavBPCOV(x, F, F, F))
TCov_5min <- lapply(mergedfrequencies[[7]], function(x) preavthrCOV(x,F))
RSCovpos_5min <- lapply(mergedfrequencies[[7]], function(x) realsemicov(x,"P"))
RSCovneg_5min <- lapply(mergedfrequencies[[7]], function(x) realsemicov(x,"N"))
RSCovmixed_5min <- lapply(mergedfrequencies[[7]], function(x) realsemicov(x,"M"))
RCov_5sec <- lapply(mergedfrequencies[[2]], function(x) realCov(x))
RCov_daily <- lapply(mergedopentoclose, function(x) realCov(x))
MRC_30sec <- lapply(mergedfrequencies[[5]], function(x) preavCov(x, T, T, F, 1))
PBPCov_30sec <- lapply(mergedfrequencies[[5]], function(x) preavBPCOV(x, T, F, T, 1))
MRK_15sec <- list()
for(i in 1:length(dataTLT)){
MRK_15sec[[i]] <- rKernelCov(list(mergedfrequencies[[3]][[i]][,1], mergedfrequencies[[3]][[i]][,2]),
makeReturns = FALSE, kernel.type = "Parzen", kernel.param = H[i])
}
RCov_5min <- matrix(unlist(lapply(RCov_5min, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(RCov_5min) <- c("TLT", "SPY", "Correlation")
RCov_5sec <- matrix(unlist(lapply(RCov_5sec, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(RCov_5sec) <- c("TLT", "SPY", "Correlation")
BPCov_5min <- matrix(unlist(lapply(BPCov_5min, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(BPCov_5min) <- c("TLT", "SPY", "Correlation")
TCov_5min <- matrix(unlist(lapply(TCov_5min, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(TCov_5min) <- c("TLT", "SPY", "Correlation")
RSCovpos_5min <- matrix(unlist(lapply(RSCovpos_5min, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(RSCovpos_5min) <- c("TLT", "SPY", "Correlation")
RSCovneg_5min <- matrix(unlist(lapply(RSCovneg_5min, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(RSCovneg_5min) <- c("TLT", "SPY", "Correlation")
RSCovmixed_5min <- matrix(unlist(lapply(RSCovmixed_5min, function(x) cbind(x[1,1], x[2,2], x[2,1]))),
nrow = 2516, ncol=3, byrow=T)
colnames(RSCovmixed_5min) <- c("TLT", "SPY", "Covariance")
RCov_daily <- matrix(unlist(lapply(RCov_daily, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(RCov_daily) <- c("TLT", "SPY", "Correlation")
MRC_30sec <- matrix(unlist(lapply(MRC_30sec, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(MRC_30sec) <- c("TLT", "SPY", "Correlation")
PBPCov_30sec <- matrix(unlist(lapply(PBPCov_30sec, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(PBPCov_30sec) <- c("TLT", "SPY", "Correlation")
MRK_15sec <- matrix(unlist(lapply(MRK_15sec, function(x) cbind(x[1,1], x[2,2], x[2,1]/(sqrt(x[2,2])*sqrt(x[1,1]))))),
nrow = 2516, ncol=3, byrow=T)
colnames(MRK_15sec) <- c("TLT", "SPY", "Correlation")
#HOW SHOULD YOU TRANSFORM IT?!?
RCov_5min[,1:2] <- RCov_5min[,1:2]*(24/6.5)
RCov_5sec[,1:2] <- RCov_5sec[,1:2]*(24/6.5)
BPCov_5min[,1:2] <- BPCov_5min[,1:2]*(24/6.5)
TCov_5min[,1:2] <- TCov_5min[,1:2]*(24/6.5)
RSCovpos_5min[,1:2] <- RSCovpos_5min[,1:2]*(24/6.5)
RSCovneg_5min[,1:2] <- RSCovneg_5min[,1:2]*(24/6.5)
RCov_daily[,1:2] <- RCov_daily[,1:2]*(24/6.5)
MRC_30sec[,1:2] <- MRC_30sec[,1:2]*(24/6.5)
PBPCov_30sec[,1:2] <- PBPCov_30sec[,1:2]*(24/6.5)
MRK_15sec[,1:2] <- MRK_15sec[,1:2]*(24/6.5)
library(PerformanceAnalytics)
#using performanceAnalytics package you can easily get your summary statistics.
#TLT
table.Stats(RCov_5min[,1])
table.Autocorrelation(RCov_5min[,1])
table.Stats(RCov_5sec[,1])
table.Autocorrelation(RCov_5sec[,1])
table.Stats(BPCov_5min[,1])
table.Autocorrelation(BPCov_5min[,1])
table.Stats(TCov_5min[,1])
table.Autocorrelation(TCov_5min[,1])
table.Stats(RSCovpos_5min[,1])
table.Autocorrelation(RSCovpos_5min[,1])
table.Stats(RSCovneg_5min[,1])
table.Autocorrelation(RSCovneg_5min[,1])
table.Stats(RCov_daily[,1])
table.Autocorrelation(RCov_daily[,1])
table.Stats(MRC_30sec[,1])
table.Autocorrelation(MRC_30sec[,1])
table.Stats(PBPCov_30sec[,1])
table.Autocorrelation(PBPCov_30sec[,1])
table.Stats(MRK_15sec[,1])
table.Autocorrelation(MRK_15sec[,1])
#SPY
table.Stats(RCov_5min[,2])
table.Autocorrelation(RCov_5min[,2])
table.Stats(RCov_5sec[,2])
table.Autocorrelation(RCov_5sec[,2])
table.Stats(BPCov_5min[,2])
table.Autocorrelation(BPCov_5min[,2])
table.Stats(TCov_5min[,2])
table.Autocorrelation(TCov_5min[,2])
table.Stats(RSCovpos_5min[,2])
table.Autocorrelation(RSCovpos_5min[,2])
table.Stats(RSCovneg_5min[,2])
table.Autocorrelation(RSCovneg_5min[,2])
table.Stats(RCov_daily[,2])
table.Autocorrelation(RCov_daily[,2])
table.Stats(MRC_30sec[,2])
table.Autocorrelation(MRC_30sec[,2])
table.Stats(PBPCov_30sec[,2])
table.Autocorrelation(PBPCov_30sec[,2])
table.Stats(MRK_15sec[,2])
table.Autocorrelation(MRK_15sec[,2])
#Correlation
table.Stats(RCov_5min[,3])
table.Autocorrelation(RCov_5min[,3])
table.Stats(BPCov_5min[,3])
table.Autocorrelation(BPCov_5min[,3])
table.Stats(TCov_5min[,3])
table.Autocorrelation(TCov_5min[,3])
table.Stats(RSCovpos_5min[,3])
table.Autocorrelation(RSCovpos_5min[,3])
table.Stats(RSCovneg_5min[,3])
table.Autocorrelation(RSCovneg_5min[,3])
table.Stats(RCov_daily[,3])
table.Autocorrelation(RCov_daily[,3])
table.Stats(MRC_30sec[,3])
table.Autocorrelation(MRC_30sec[,3])
table.Stats(PBPCov_30sec[,3])
table.Autocorrelation(PBPCov_30sec[,3])
table.Stats(MRK_15sec[,3])
table.Autocorrelation(MRK_15sec[,3])
#introductory graph
meanpriceTLT <- unlist(lapply(dataTLT, function(x) mean(x)))
p1 <- ggplot() + geom_line(aes(as.Date(getDates), RCov_5min[,1]/(24/6.5)), col = "firebrick") + ylab("Ann. var (%)") +
xlab("Dates") + ggtitle("TLT") + theme(plot.title = element_text(hjust = 0.5))
p2 <- ggplot() + geom_line(aes(as.Date(getDates), RCov_5min[,2]/(24/6.5)), col = "deepskyblue1") + ylab("Ann. var (%)") +
xlab("Dates") + ggtitle("SPY") + theme(plot.title = element_text(hjust = 0.5))
p3 <- ggplot() + geom_line(aes(as.Date(getDates), RCov_5min[,3]), col = "maroon") + ylab("Correlation") +
xlab("Dates") + theme(plot.title = element_text(hjust = 0.5)) +
geom_hline(yintercept = 0, linetype = "dashed")
library(gridExtra)
pf <- grid.arrange(p1,p2,p3, ncol=1)
#ggsave(pf, file="varianceassets.eps", device="eps")