crossq.sb: Stationary Bootstrap for the Cross-Quantilogram

In quantilogram: Cross-Quantilogram

Stationary Bootstrap for the Cross-Quantilogram

Description

Returns critical values for the cross-quantilogram, based on the stationary bootstrap.

Usage

crossq.sb(DATA, vecA, k, gamma, Bsize, sigLev)

Arguments

DATA	The original data matrix
vecA	A pair of two probability values at which sample quantiles are estimated
k	A lag order
gamma	A parameter for the stationary bootstrap
Bsize	The number of repetition of bootstrap
sigLev	The statistical significance level

Details

This function generates critical values for for the cross-quantilogram, using the stationary bootstrap in Politis and Romano (1994).

Value

The boostrap critical values

Author(s)

Heejoon Han, Oliver Linton, Tatsushi Oka and Yoon-Jae Whang

References

Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series." Journal of Econometrics, 193(1), 251-270.

Politis, Dimitris N., and Joseph P. Romano. "The stationary bootstrap." Journal of the American Statistical Association 89.428 (1994): 1303-1313.

Examples

data("sys.risk") ## data source
D = sys.risk[,c("Market", "JPM")] ## data: 2 variables

# probability levels for the 2 variables
vecA = c(0.1, 0.5)

## setup for stationary bootstrap
gamma  = 1/10 ## bootstrap parameter depending on data
Bsize  = 5    ## small size, 5, for test 
sigLev = 0.05 ## significance level

## cross-quantilogram with the lag of 5
crossq.sb(D, vecA, 5, gamma, Bsize, sigLev)

quantilogram documentation built on March 18, 2022, 5:29 p.m.