Area variance estimators for simulation using folded standardized time series

We estimate the variance parameter of a stationary simulation-generated process using "folded" versions of standardized time series area estimators. Asymptotically as the sample size increases, different folding levels yield unbiased estimators that are independent scaled chi-squared variates, each with one degree of freedom. This result is exploited to formulate improved variance estimators based on the combination of multiple levels as well as the use of batching. The improved estimators preserve the asymptotic bias properties of their predecessors, but have substantially lower asymptotic variances. The performance of the new variance estimators is demonstrated in a first-order autoregressive process with autoregressive parameter 0.9 and in the queue-waiting-time process for an M/M/1 queue with server utilization 0.8.