DriftBurstHypothesis (version 0.4.0.1)

driftBursts: Drift Bursts

Description

Calculates the Test-Statistic for the Drift Burst Hypothesis.

Usage

driftBursts(timestamps = NULL, logPrices, testTimes = seq(34260, 57600, 60),
                        preAverage = 5, ACLag = -1L, 
                        meanBandwidth = 300L, varianceBandwidth = 900L, 
                        sessionStart = 34200, sessionEnd = 57600,
                        parallelize = FALSE, nCores = NA, warnings = TRUE)

Arguments

timestamps

Either: A numeric of timestamps for the trades in seconds after midnight. Or: NULL, when the time argument is NULL, the logprices argument must be an xts object.

logPrices

A numeric or xts object containing the log-prices.

testTimes

A numeric containing the times at which to calculate the tests. The standard of seq(34260, 57600, 60) denotes calculating the test -statistic once per minute, i.e. 390 times for a typical 6.5 hour trading day from 9:31:00 to 16:00:00. See details.

preAverage

An integer denoting the length of pre-averaging estimates of the log-prices.

ACLag

A positive integer greater than 1 denoting how many lags are to be used for the HAC estimator of the variance - the standard of -1 denotes using an automatic lag selection algorithm for each iteration.

meanBandwidth

An integer denoting the bandwidth for the left-sided exponential kernel for the mean.

varianceBandwidth

An integer denoting the bandwidth for the left-sided exponential kernel for the variance.

sessionStart

A double to denote the start of the trading session in seconds after midnight.

sessionEnd

A double to denote the end of the trading session in seconds after midnight.

parallelize

A Boolean to determine whether to parallelize the underlying C++ code. (Using OpenMP)

nCores

An integer denoting the number of cores to use for calculating the code when paralellized. If this argument is not provided, sequential evaluation will be used even though bParallelize is TRUE

warnings

A logical denoting whether warnings should be shown.

Value

An object of class DBH and list containing the series of the drift burst hypothesis test-statistic as well as the estimated spot drift and variance series. The list also contains some information such as the variance and mean bandwidths along with the pre-averaging setting and the amount of observations. Additionally, the list will contain information on whether testing happened for all testTimes entries.

warning

When using getMu(DBH), beware that the series may be padded with zeros (a warning will be shown when running the driftBursts in this case). This will for example happen when using testTimes for an entire trading day but the data is from a half trading day. Therefore, methods for calculating the mean and variance of the series that take this into account are provided and documented here.

Details

If the testTimes vector contains instructions to test before the first trade, or more than 15 minutes after the last trade, these entries will be deleted, as not doing so may cause crashes.

The test statistic is unstable before max(meanBandwidth , varianceBandwidth) seconds has passed.

If timestamps is provided and logPrices is an xts object, the indices of logPrices will be used regardless.

Note that using an xts logPrices argument is slower than using a numeric due to the creation of the timestamps from the index of the input. When using xts objects, be careful to use the correct time zones. For example, if I as a dane use the "America/New_York" time zone for my xts objects, I have to add 14400 to my testing times. Same correction will have to be made to the startTime and endTime arguments in the plotting methods.

The lags from the Newey-West algorithm is increased by 2 * (preAveage-1) due to the pre-averaging we know atleast this many lags should be corrected for. The maximum of 20 lags is also increased by this same factor for the same reason.

The methods getMu(DBH) and getSigma(DBH) have ad-hoc adjustments made to the output that are based on simulations. These adjustments give a slight down-ward bias in the value of the drift and sigma if the preAverage argument is VERY high, i.e. 10 or 20 for a noise-less price series of e.g. 23400 trades. If you find any better adjustments, please contact me.

To get the unadjusted series, use DBH$mu or DBH$sigma for the drift, and volatility respectively.

References

Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>.

See Also

The DBH methods for the DBH class are documented here.

Examples

# NOT RUN {
#Simulate a price series containing both a flash crash and a flash-rally
flashCrashSim = function(iT, dSigma, dPhi, dMu){
  vSim = numeric(iT)
  vEps = rnorm(iT , sd =dSigma)
  vEpsy = rnorm(iT)
  vEps[30001:32000] = rnorm(2000 ,sd =seq(from = dSigma , 
                                          to = 2*dSigma , length.out = 2000)) 
  vEps[32001:34000] = rnorm(2000 ,sd =seq(from = 2*dSigma , 
                                          to = dSigma , length.out = 2000))
  vEpsy[30001:32000] = -rnorm(2000 ,mean =seq(from = 0,
                                              to = 0.3 , length.out = 2000)) 
  vEpsy[32001:34000] = -rnorm(2000 ,mean =seq(from = 0.3,
                                              to = 0 , length.out = 2000))
  
  
  vEps[60001:63000] = rnorm(3000,sd = seq(from = dSigma , 
                                          to = 2*dSigma , length.out = 3000))
  vEps[63001:66000] = rnorm(3000,  sd = seq(from = 2*dSigma , 
                                            to =  dSigma, length.out = 3000))
  
  vEpsy[60001:63000] = rnorm(3000 ,mean =seq(from = 0,
                                             to = 0.2 , length.out = 3000))
  vEpsy[63001:66000] = rnorm(3000 ,mean =seq(from = 0.2,
                                             to = 0 , length.out = 3000))
  vSim[1] = dMu + dPhi *rnorm(1 , mean = dMu , sd = dSigma /sqrt(1-dPhi^2))
  for (i in 2:iT) {
    vSim[i] = dMu + dPhi * (vSim[(i-1)] - dMu) + vEps[i]
  }
  vY = exp(vSim/2) * vEpsy
  return(vY)
}
#Set parameter values of the simulation
iT = 66500; dSigma = 0.3; dPhi = 0.98; dMu = -10;
#set seed for reproducibility
set.seed(123)
#Simulate the series
y = 500+cumsum(flashCrashSim(iT, dSigma, dPhi, dMu))

#insert an outlier to illustrate robustness.
y[50000] = 500

#Here, the observations are equidistant, but the code can handle unevenly spaced observations.
timestamps = seq(34200 , 57600 , length.out = iT)
testTimes = seq(34260, 57600, 60)
logPrices = log(y)

library("DriftBurstHypothesis")

#calculating drift burst test statistic

DBH = driftBursts(timestamps,  logPrices,
                        testTimes, preAverage = 5, ACLag = -1L,
                        meanBandwidth = 300L, varianceBandwidth = 900L,
                        parallelize = FALSE)


#plot test statistic
plot(DBH)
#plot both test statistic and price
plot(DBH, price = y, timestamps = timestamps)
#Plot the mu series
plot(DBH, which = "Mu")
#plot the sigma series
plot(DBH, which = "Sigma")
#plot both
plot(DBH, which = c("Mu", "Sigma"))


#Means of the tstat, sigma, and mu series.
mean(getDB(DBH))
mean(getSigma(DBH))
mean(getMu(DBH))



# }
# NOT RUN {
################## same example with xts object:
library("xts")
#Set parameter values of the simulation
iT = 66500; dSigma = 0.3; dPhi = 0.98; dMu = -10;
#set seed for reproducibility
set.seed(123)
#Simulate the series
y = 500+cumsum(flashCrashSim(iT, dSigma, dPhi, dMu))

#insert an outlier to illustrate robustness.
y[50000] = 500

#Here, the observations are equidistant, but the code can handle unevenly spaced observations.
timestamps = seq(34200 , 57600 , length.out = iT)
startTime = strptime("1970-01-01 00:00:00.0000", "%Y-%m-%d %H:%M:%OS", tz = "GMT")
testTimes = seq(34260, 57600, 60)


price = xts(vY, startTime + timestamps)


DBHxts = driftBursts(timestamps = NULL,  log(price), 
                   testTimes, preAverage = 5, ACLag = -1L,
                   meanBandwidth = 300L, varianceBandwidth = 900L, 
                   parallelize = FALSE)

#check for equality
all.equal(as.numeric(getDB(DBH)), as.numeric(getDB(DBHxts)))
# }