Decimalization, Realized Volatility, and Market Microstructure Noise

Abstract

This paper carefully examines the effect of decimalization on volatility and market microstructure noise. We apply several nonparametric estimators in order to accurately measure volatility and market microstructure noise variance before and after the final stage of decimalization which, on the NYSE, took place in January, 2001. We findn that decimalization decreased observed volatility by decreasing noise variance and, consequently, increased the significance of the true signal especially in trade price data for the highly active Dow Jones stocks. This study also reveals some differences between volatility and noise variance estimators’ capability to handle changes in tick size and strategic order placing that are relevant in the evaluation of the decimalization effects. The ability of the realized kernel estimator to adapt to more complex data dependency than the traditional realized volatility estimator is useful. The two-scale and multi-scale realized volatility estimates turn out to be more variable especially with midquote data where a couple of outlying dates affect their volatility estimates and consequently the test results in a significant way.

Keywords: Decimalization, Market microstructure noise, Realized volatility, Realized variance, Tick size, Ultra-high-frequency data

JEL Classification: C14, C19