highfrequency (version 1.0.1)

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,
  tz = NULL,
  ...
)

Value

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

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).

tz

fallback time zone used in case we we are unable to identify the timezone of the data, by default: tz = NULL. We attempt to extract the timezone from the DT column (or index) of the data, which may fail. In case of failure we use tz if specified, and if it is not specified, we use "UTC".

...

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

Author

Emil Sjoerup.

References

Dong, Y., and Tse, Y. K. (2017). Business time sampling scheme with applications to testing semi-martingale hypothesis and estimating integrated volatility. Econometrics, 5, 51.

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

Examples

data.table::setDTthreads(2)

pData <- sampleTData[,list(DT, PRICE, SIZE)]
# 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$intensityProcess$`2018-01-02`)
rCov(agged$pData[, list(DT, PRICE)], makeReturns = TRUE)
rCov(pData[,list(DT, 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[,list(DT, PRICE)], makeReturns = TRUE)
rCov(pData[,list(DT, PRICE)], makeReturns = TRUE, alignBy = "minutes", alignPeriod = 5)