lm.jumptest {yuima} | R Documentation |
Lee and Mykland's Test for the Presence of Jumps Using Normalized Returns
Description
Performs a test for the null hypothesis that the realized path has no jump following Lee and Mykland (2008).
Usage
lm.jumptest(yuima, K)
Arguments
yuima |
an object of |
K |
a positive integer indicating the window size to compute local variance estimates. It can be specified as a vector to use different window sizes for different components. The default value is |
Value
A list with the same length as dim(yuima)
. Each component of the list has class “htest
” and contains the following components:
statistic |
the value of the test statistic of the corresponding component of |
p.value |
an approximate p-value for the test of the corresponding component. |
method |
the character string “ |
data.name |
the character string “ |
Author(s)
Yuta Koike with YUIMA Project Team
References
Dumitru, A.-M. and Urga, G. (2012) Identifying jumps in financial assets: A comparison between nonparametric jump tests. Journal of Business and Economic Statistics, 30, 242–255.
Lee, S. S. and Mykland, P. A. (2008) Jumps in financial markets: A new nonparametric test and jump dynamics. Review of Financial Studies, 21, 2535–2563.
Maneesoonthorn, W., Martin, G. M. and Forbes, C. S. (2020) High-frequency jump tests: Which test should we use? Journal of Econometrics, 219, 478–487.
Theodosiou, M. and Zikes, F. (2011) A comprehensive comparison of alternative tests for jumps in asset prices. Central Bank of Cyprus Working Paper 2011-2.
See Also
bns.test
, minrv.test
, medrv.test
, pz.test
Examples
set.seed(123)
# One-dimensional case
## Model: dXt=t*dWt+t*dzt,
## where zt is a compound Poisson process with intensity 5 and jump sizes distribution N(0,1).
model <- setModel(drift=0,diffusion="t",jump.coeff="t",measure.type="CP",
measure=list(intensity=5,df=list("dnorm(z,0,sqrt(0.1))")),
time.variable="t")
yuima.samp <- setSampling(Terminal = 1, n = 390)
yuima <- setYuima(model = model, sampling = yuima.samp)
yuima <- simulate(yuima)
plot(yuima) # The path seems to involve some jumps
lm.jumptest(yuima) # p-value is very small, so the path would have a jump
lm.jumptest(yuima, K = floor(sqrt(390))) # different value of K
# Multi-dimensional case
## Model: Bivariate standard BM + CP
## Only the first component has jumps
mod <- setModel(drift = c(0, 0), diffusion = diag(2),
jump.coeff = diag(c(1, 0)),
measure = list(intensity = 5,
df = "dmvnorm(z,c(0,0),diag(2))"),
jump.variable = c("z"), measure.type=c("CP"),
solve.variable=c("x1","x2"))
samp <- setSampling(Terminal = 1, n = 390)
yuima <- setYuima(model = model, sampling = yuima.samp)
yuima <- simulate(object = mod, sampling = samp)
plot(yuima)
lm.jumptest(yuima) # test is performed component-wise