The Garman-Klass volatility estimator revisited

Abstract

The Garman-Klass unbiased estimator of the variance per unit time of a zero-drift Brownian Motion B, based on the usual financial data that reports for time windows of equal length the open (OPEN), minimum (MIN), maximum (MAX) and close (CLOSE) values, is quadratic in the statistic S1=(CLOSE-OPEN, OPEN-MIN, MAX-OPEN). This estimator, with efficiency 7.4 with respect to the classical estimator (CLOSE-OPEN)^2, is widely believed to be of minimal variance. The current report disproves this belief by exhibiting an unbiased estimator with slightly but strictly higher efficiency 7.7322. The essence of the improvement lies in the observation that the data should be compressed to the statistic S2 defined on W(t)= B(0)+[B(t)-B(0)]sign[(B(1)-B(0)] as S1 was defined on the Brownian path B(t). The best S2-based quadratic unbiased estimator is presented explicitly. The Cramer-Rao upper bound for the efficiency of unbiased estimators, corresponding to the efficiency of large-sample Maximum Likelihood estimators, is 8.471. This bound cannot be attained because the distribution is not of exponential type. Regression-fitted quadratic functions of S2 (with mean 1) markedly out-perform those of S1 when applied to random walks with heavy-tail-distributed increments. Performance is empirically studied in terms of the tail parameter