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library(timeSeries)
library(MTS)
library(Rblpapi)
blpConnect(host='192.168.9.61')
data <- feather::read_feather("/home/serenitas/CorpCDOs/data/index_returns.fth")
data$date <- as.Date(data$date)
df <- bdh(paste("VIX", "Index"), "PX_LAST", start.date=as.Date("2009-03-20"))
df <- as.tibble(df)
R <- na.omit(returns)
R <- scale(R, scale=F)
chol <- MCholV(na.omit(returns))
ema <- function(x, alpha=0.1, init=x[1]){
## exponential moving average with parameter lambda=1-beta
filter(alpha*x, filter = 1-alpha, method = "recursive", init = init)
}
ema.slow <- function(y, lambda, init=y[1]) {
# slower but more explicit
mu <- init
mu <- vapply(y, function(x) mu <<- mu*lambda + x*(1 - lambda), numeric(1))
return( mu )
}
cov.ewm <- function(X, lambda, init=X[1,]) {
ema(X
beta.ewm <- function(R, lambda, span) {
# computes beta between two assets using exponential moving averages
if(ncol(R) != 2) {
stop("only works for two assets")
}
if(missing(lambda)) {
alpha <- 2/(span+1)
} else {
alpha <- 1-lambda
}
R <- scale(R, scale=F)
cov12 <- ema(R[,1] * R[,2], alpha)
var1 <- ema(R[,1] * R[,1], alpha)
return ( cov12/var1 )
}
## library(rugarch)
## spec <- ugarchspec(variance.model=list(model="sGARCH"),
## mean.model = list(armaOrder=c(0,0), include.mean = TRUE))
## fit.ig <- ugarchfit(spec, na.omit(df$ig), solver='hybrid')
## fit.hy <- ugarchfit(spec, na.omit(df$hy), solver='hybrid')
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