rankJumpTest {highfrequency} | R Documentation |
Rank jump test
Description
Calculate the rank jump test of Li et al. (2019). The procedure tests for the rank of the jump matrix at simultaneous jump events in market returns as well as individual assets.
Usage
rankJumpTest(
marketPrice,
stockPrices,
alpha = c(5, 3),
coarseFreq = 10,
localWindow = 30,
rank = 1,
BoxCox = 1,
quantiles = c(0.9, 0.95, 0.99),
nBoot = 1000,
dontTestAtBoundaries = TRUE,
alignBy = "minutes",
alignPeriod = 5,
marketOpen = "09:30:00",
marketClose = "16:00:00",
tz = NULL
)
Arguments
marketPrice |
data.table or |
stockPrices |
list containing the individual stock prices in either data.table or |
alpha |
significance level (in standard deviations) to use for the jump detections. Default is |
coarseFreq |
numeric denoting the coarse sampling frequency. Default is |
localWindow |
numeric denoting the local window for the bootstrap algorithm. Default is |
rank |
rank of the jump matrix under the null hypothesis. Default is |
BoxCox |
numeric of exponents for the Box-Cox transformation, default is |
quantiles |
numeric denoting which quantiles of the bootstrapped critical values to return and compare against. Default is |
nBoot |
numeric denoting how many replications to be used for the bootstrap algorithm. Default is |
dontTestAtBoundaries |
logical determining whether to exclude data across different days. Default is |
alignBy |
character, indicating the time scale in which |
alignPeriod |
positive numeric, indicating the number of periods to aggregate over. E.g. to aggregate
based on a 5 minute frequency, set |
marketOpen |
the market opening time, by default: |
marketClose |
the market closing time, by default: |
tz |
fallback time zone used in case we we are unable to identify the timezone of the data, by default: |
Details
Let the jump times be defined as:
{\cal I}_{n} = \left\{ i:\left|\Delta_{i}^{n}Z\right|>u_{n}\right\}
Then the estimated jump matrix is:
\hat{\boldsymbol{J}_{n}}=\left[\Delta_{i,k}^{n}\boldsymbol{X}\right]_{i\in{\cal I}_{n}}
Let \hat{\lambda}_{n,1}^{2}\geq\hat{\lambda}_{n,2}^{2}\geq\cdots\geq\hat{\lambda}_{n,d}^{2}
be the ordered eigenvalues of \hat{\boldsymbol{J}}_{n}\hat{\boldsymbol{J}}_{n}^{\prime}
, then test statistic is
\hat{S}_{n,t}=\sum_{j=r+1}^{d}\hat{\lambda}_{n,j}^{2}.
The critical values are computed by applying a bootstrapping method
The singular value decomposition of the jump matrix \hat{\boldsymbol{J}}_{n}
is:
\hat{\boldsymbol{J}}=\hat{\boldsymbol{U}}_{n}\hat{\boldsymbol{D}}_{n}\hat{\boldsymbol{V}}_{n}^{\prime}
then \hat{\boldsymbol{U}}_{n}=\left[\hat{\boldsymbol{U}}_{1n}:\hat{\boldsymbol{U}}_{2n}\right]
and \hat{\boldsymbol{V}}_{n}=\left[\hat{\boldsymbol{V}}_{1n}:\hat{\boldsymbol{V}}_{2n}\right]
\boldsymbol{\upsilon}_{n}=\left(\upsilon_{j,n}\right)_{1\leq j\leq d}
such that \upsilon_{j,n}\asymp\Delta_{n}^{\varpi} for \varpi\in\left(0,1/2\right)
which is used to trim jumps. The bootstrapping method is calculated by the following algorithm
-
Step 1.
For each
i\in{\cal I}_{n}
, draw\kappa_{i}^{\star}\sim\textrm{Uniform}\left[0,1\right]
and draw with equal probability,\boldsymbol{\xi}_{n,i-}^{\star} \textrm{from}\left\{ \min\left(\max\left(\Delta_{i-j}^{n}\boldsymbol{X},-\boldsymbol{\upsilon}_{n}\right),\boldsymbol{\upsilon}_{n}\right):1\leq j\leq k_{n}\right\},
\boldsymbol{\xi}_{n,i+}^{\star} \textrm{from}\left\{ \min\left(\max\left(\Delta_{i+j}^{n}\boldsymbol{X},-\boldsymbol{\upsilon}_{n}\right),\boldsymbol{\upsilon}_{n}\right):1\leq j\leq k_{n}\right\},
and set
\boldsymbol{\zeta}_{n,i}^{\star}=\sqrt{\kappa_{i}^{\star}}\boldsymbol{\xi}_{n,i-}^{\star}+\sqrt{k-\kappa_{i}^{\star}}\boldsymbol{\xi}_{n,i+}^{\star}
and\boldsymbol{\zeta}_{n}^{\star}=\left[\boldsymbol{\zeta}_{n,i}^{\star}\right]_{i\in{\cal I}_{n}}
-
Step 2.
Repeat 1 for a large number of iterations. Set
c\upsilon_{n,\alpha}
as as the1-\alpha
quantile of\left\Vert \hat{\boldsymbol{U}}_{2n}^{\prime}\boldsymbol{\xi}_{n}^{\star}\hat{\boldsymbol{V}}_{2n}\right\Vert ^{2}
in the simulated sample.
Value
A list containing criticalValues
which are the bootstrapped critical values, testStatistic
the test statistic of the jump test, dimensions
which are the dimensions of the jump matrix
marketJumpDetections
the jumps detected in the market prices, stockJumpDetections
the co-jumps detected in the individual stock prices, and jumpIndices
which are the indices of the detected jumps.
Author(s)
Emil Sjoerup, based on Matlab code provided by Li et al. (2019)
References
Li, j., Todorov, V., Tauchen, G., and Lin, H. (2019). Rank Tests at Jump Events. Journal of Business & Economic Statistics, 37, 312-321.