highfrequency (version 0.7.0.1)

businessTimeAggregation: Business time aggregation

Description

Time series aggregation based on `business time` statistics. Instead of equidistant sampling based on time during a trading day, business time sampling creates measures and samples equidistantly using these instead. For example when sampling based on volume, business time aggregation will result in a time series that has an equal amount of volume between each observation (if possible).

Usage

businessTimeAggregation(
  pData,
  measure = "volume",
  obs = 390,
  bandwidth = 0.075,
  ...
)

Arguments

pData

xts or data.table containing data to aggregate.

measure

character denoting which measure to use. Valid options are "intensity", "vol", and "volume", denoting the trade intensity process of Oomen (2005), volatility, and volume, respectively. Default is "volume"

obs

integer valued numeric of length 1 denoting how many observations is wanted after the aggregation procedure.

bandwidth

numeric of length one, denoting which bandwidth parameter to use in the trade intensity process estimation of Oomen (2005.)

...

extra arguments passed on to spotVol when measure is "vol"

Value

A list containing "pData" which is the aggregated data and a list containing the intensity process, split up day by day.

References

Roel C. A. Oomen Properties of realized variance under alternative sampling schemes. (2006) Journal of Business & Economic Statistics 24, pages 219-237

Yingjie Dong and Yiu Kuen Tse. (2017) Business time sampling scheme with applications to testing semi-martingale hypothesis and estimating integrated volatility. Econometrics, 5

Examples

# NOT RUN {
pData <- sampleTData[,c("PRICE", "SIZE")]
storage.mode(pData) <- "numeric"
# Aggregate based on the trade intensity measure. Getting 390 observations.
agged <- businessTimeAggregation(pData, measure = "intensity", obs = 390, bandwidth = 0.075)
# Plot the trade intensity measure
plot.ts(agged$intensityProcess$`2008-01-04`)
rCov(agged$pData[,"PRICE"], makeReturns = TRUE)
rCov(pData[,"PRICE"], makeReturns = TRUE, alignBy = "minutes", alignPeriod = 1)

# Aggregate based on the volume measure. Getting 78 observations.
agged <- businessTimeAggregation(pData, measure = "volume", obs = 78)
rCov(agged$pData[,"PRICE"], makeReturns = TRUE)
rCov(pData[,"PRICE"], makeReturns = TRUE, alignBy = "minutes", alignPeriod = 5)

# }