Building Confidence Intervals with Block Bootstraps for the Variance Ratio Test of Predictability

This paper compares different versions of the multiple variance ratio test based on bootstrap techniques for the construction of empirical distributions. It also analyzes the crucial issue of selecting optimal block sizes when block bootstrap procedures are used, by applying the methods developed by Hall et al. (1995) and by Politis and White (2004). By comparing the results of the different methods using Monte Carlo simulations, we conclude that methodologies using block bootstrap methods present better performance for the construction of empirical distributions of the variance ratio test. Moreover, the results are highly sensitive to methods employed to test the null hypothesis of random walk.