crossq.partial: Paritial Cross-Quantilogram

In quantilogram: Cross-Quantilogram

Paritial Cross-Quantilogram

Description

Returns the partial cross-quantilogram

Usage

crossq.partial(DATA, vecA, k)

Arguments

DATA	A matrix
vecA	A vector of probability values at which sample quantiles are estiamted
k	The lag order

Details

This function obtains the partial corss-quantilogram and the cross-quantilogram. To obtain the partial cross-correlation given an input matrix, this function interacts the values of the first column and the k-lagged values of the rest of the matrix.

Value

The partial corss-quantilogram and the cross-quantilogram

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.

Examples

## data source 
data("sys.risk") 

## data with 3 variables 
D = sys.risk[,c("Market", "JPM", "VIX")]

## probablity levels for the 3 variables 
vecA = c(0.1, 0.1, 0.1)

## partial cross-quantilogram with the lag of 5
crossq.max.partial(D, vecA, 5)

quantilogram documentation built on Sept. 11, 2024, 5:56 p.m.