Download this Paper Open PDF in Browser

New Moment Estimators of the Effective Spread Based on Daily High and Low Prices

40 Pages Posted: 10 Jan 2017  

Yang Gao

Beijing University of Technology

Mingjin Wang

Peking University - Guanghua School of Management

Date Written: January 7, 2015

Abstract

In the paper we propose five new moment estimators for effective spread based on the covariance estimator of Roll (1984) and the High-Low estimator recently developed by Corwin and Schultz (2012, \textit{J. Finance}, Vol.67, 719-760), and further investigate theoretically the statistical properties of six bid-ask spread estimators including Corwin and Schultz's estimator. The biases and mean squared errors (MSE) of these six estimators have been derived and compared with each other asymptotically, which, together with the subsequent simulation studies and empirical examples, reveal explicitly the superior performance of new developed High-Low estimators over Corwin and Schultz's estimator. Furthermore this paper also puts forward GMM estimators constructed by three or more moment conditions, which also perform well compared with the six High-Low estimators. The method discussed here is different to the existing literatures which usually resort to the correlation between the bid-ask spread estimators with a benchmark calculated from high-frequency data as they compare the performance of different estimators.

Keywords: Liquidity, bid-ask spread, volatility, price range, asymptotic property

JEL Classification: C15, G12, G20

Suggested Citation

Gao, Yang and Wang, Mingjin, New Moment Estimators of the Effective Spread Based on Daily High and Low Prices (January 7, 2015). Available at SSRN: https://ssrn.com/abstract=2895504 or http://dx.doi.org/10.2139/ssrn.2895504

Yang Gao (Contact Author)

Beijing University of Technology ( email )

100 Ping Le Yuan
Chaoyang District
Beijing, Beijing 100020
China

Mingjin Wang

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Paper statistics

Downloads
34
Abstract Views
220