crossq.max.partial: Partial Corss-Quantilogram upto a given lag order

In quantilogram: Cross-Quantilogram

Partial Corss-Quantilogram upto a given lag order

Description

The partial cross-quantilograms from 1 to a given lag order.

Usage

crossq.max.partial(DATA, vecA, Kmax)

Arguments

DATA	An input matrix
vecA	A vector of probability values at which sample quantiles are estimated
Kmax	The maximum lag order (integer)

Details

This function calculates the partial cross-quantilograms up to the lag order users specify.

Value

A vector of cross-quantilogram and a vector of partial cross-quantilograms

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 lags from 1 to 5
crossq.max.partial(D, vecA, 5)

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