A Better Test for Autocorrelation in Financial Time Series

This paper tests the accuracy of the commonly used cutoffs for determining the statistical significance of autocorrelations in time series. Monte Carlo simulations with 50,000 replicates were used to generate 95% confidence limits by varying sample size from 21 to 252 using both normally distributed and t-distributed data. The simulations show that the confidence limits derived from the commonly used formulas are biased at sample sizes of less than several hundred and should not be used.