Boot.test: Bootstrap Variance Ratio Tests

In vrtest: Variance Ratio Tests and Other Tests for Martingale Difference Hypothesis

Bootstrap Variance Ratio Tests

Description

This function returns bootstrap p-values of the Lo-MacKilay (1988) and Chow-Denning (1993) tests.

Users can choose between iid bootstrap and wild bootstrap

Usage

Boot.test(y, kvec, nboot, wild, prob=c(0.025,0.975))

Arguments

y	a vector of time series, typically financial return
kvec	a vector of holding periods
nboot	the number of bootstrap iterations
wild	"No" for iid bootstrap, "Normal" for the wild bootstrap using the standard normal distribution, "Mammen" for the wild bootstrap using Mammen's two point distribution, "Rademacher" for the wild bootstrap using Rademacher's two point distribution
prob	probability limits for confidence intervals

Value

Holding.Period	holding periods used
LM.pval	Bootstrap p-values for the Lo-MacKinlay tests
CD.pval	Bootstrap p-value for the Chow-Denning test
CI	Confidence Intervals for Lo-Mackinlay tests from Bootstrap distribution

Author(s)

Jae H. Kim

References

Kim, J.H., 2006, Wild Bootstrapping Variance Ratio Tests. Economics Letters, 92, 38-43.

Examples

data(exrates)
y <- exrates$ca                   
nob <- length(y)
r <- log(y[2:nob])-log(y[1:(nob-1)])    
kvec <- c(2,5,10)
Boot.test(r,kvec,nboot=500,wild="Normal")

vrtest documentation built on Aug. 31, 2023, 9:08 a.m.