Greconomist - 1 year ago 96

R Question

I have an xts object called

`data`

`head(data, 5)`

DPRICE

2015-01-01 17:00:00 0.000000e+00

2015-01-01 17:05:00 9.797714e-05

2015-01-01 17:10:00 2.027022e-04

2015-01-01 17:15:00 2.735798e-04

2015-01-01 17:20:00 7.768653e-05

tail(data, 5)

DPRICE

2015-12-31 16:40:00 0.0001239429

2015-12-31 16:45:00 0.0001272704

2015-12-31 16:50:00 0.0010186764

2015-12-31 16:55:00 0.0006841370

2015-12-31 17:00:00 0.0002481227

I am trying to standardize the data by their average absolute value for each 5-minute intra-day interval according to McMillan and Speight Daily FX Volatility Forecasts (2012).

The mathematical formula is :

My *code is

`library(xts)`

std_data = abs(data) #create absolute returns

D <- split(std_data, "days") #splits data to days

mts.days <- lapply(seq_along(D) - 1, function(i) {

if (i > 0) rbind(D[[i]]["T17:00:00/T23:55:00"], D[[i + 1]]["T00:00:00/T16:55:00"])

}) #creates a list with 365 elements each containing 288 unique returns

dummy = mapply(sum, mts.days) #add the first,second... observations from each element

With this code I create a list with 365 xts elements each having dimensions

`> dim(mts.days[[2]])`

[1] 288 1

I want to add the same observations from each element to create the denominator of the function above.

Answer Source

I don't understand your request, but will give it a shot nevertheless.

```
## generate bogus data
library(quantmod)
set.seed(123)
ndays <- 3
ndatperday <- 288
data <- cumsum(do.call("rbind", lapply(13:15, function(dd){
xts(rnorm(ndatperday)/1e4,
seq(as.POSIXct(paste0("2016-08-",dd," 17:00:00")),
length = ndatperday, by = 300))
})))
colnames(data) <- "DPRICE"
## calculate percentage returns
ret <- ROC(data, type="discrete")
## this is probably not what you need: returns divided by the overall mean
ret/mean(abs(ret), na.rm=T)
## I suspect indeed that you need returns divided by the daily mean return
library(dplyr)
ret.df <- data.frame(ret)
## create a factor identifying the 3 days of bogus data
ret.df$day <- rep(paste0("2016-08-",13:15),each=ndatperday)
## compute daily mean return
dail <- ret.df %>%
group_by(day) %>%
summarise(mean=mean(abs(DPRICE), na.rm=TRUE))
## attach daily mean returns to the days they actually are associated to
ret.df <- ret.df %>% left_join(dail)
## normalize
ret.df$DPRICE <- ret.df$DPRICE/ret.df$mean
```

%%%%%%%%%

Second shot: after reading the paper (http://onlinelibrary.wiley.com/doi/10.1002/for.1222/full) I might have understood what you were after:

```
library(quantmod)
library(dplyr)
set.seed(123)
## generate bogus 5-min series
ndays <- 365
ndatperday <- 288
data <- as.xts(zoo(0.1+cumsum(rt(ndays*ndatperday, df=3))/1e4,
seq(as.POSIXct("2015-01-01 17:00"),
as.POSIXct("2015-12-31 17:00"), by=300)))
colnames(data) <- "DPRICE"
## calculate 5-min percentage returns
ret <- ROC(data, type="discrete")
## create a factor identifying the 5-minute intra-day interval
ret.df <- as.data.frame(ret)
ret.df$intra5 <- strftime(index(ret), format="%H:%M")
## compute mean returns (over the year) for each of the 288 5-minute intra-day intervals
dail <- ret.df %>%
group_by(intra5) %>%
summarise(mean=mean(abs(DPRICE), na.rm=TRUE))
## attach mean returns to each datapoint
ret.df <- ret.df %>% left_join(dail)
## normalize
ret.df$DPRICE <- ret.df$DPRICE/ret.df$mean
```