Abstract

https://ssrn.com/abstract=2895360
 


 



A New Effective Spread Estimator Based on Price Range


Yang Gao


Beijing University of Technology

Mingjin Wang


Peking University - Guanghua School of Management

January 7, 2017


Abstract:     
This paper proposes an quasi maximum likelihood estimator (QMLE) for effective spread by approximating the distribution of the logarithm of price range with a normal distribution based on Roll’s price model and also investigates the statistical properties of the QMLE. Besides, Monte Carlo simulation studies have been conducted to make a comparison of the accuracy between the QMLE and other three estimators proposed in the early literature, namely Roll estimator (1984), the Bayesian estimator of Hasbrouck (2004) and the High-Low estimator of Corwin and Schultz (2012). Simulation results show that, both in the ideal case when the prices can be observed continuously and in the non-ideal case when trading inconsecutive, the QMLE and High-Low estimator are more accurate than the other two estimators. If the volatility is relatively smaller than the spread, the performance of QMLE will be superior to the High-Low method. Moreover, the QMLE is obviously more robust than the High-Low estimator in the non-ideal situation. Finally, an empirical study in Chinese stock markets also proves that the QMLEs performance is better than the other three estimators. Therefore, QMLE is an effective proxy for the transaction cost of financial assets.

Number of Pages in PDF File: 32

Keywords: Liquidity; Bid-Ask Spread; Price Range; QMLE; Genetic Algorithm

JEL Classification: C15; G12; G20


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Date posted: January 10, 2017  

Suggested Citation

Gao, Yang and Wang, Mingjin, A New Effective Spread Estimator Based on Price Range (January 7, 2017). Available at SSRN: https://ssrn.com/abstract=2895360 or http://dx.doi.org/10.2139/ssrn.2895360

Contact Information

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
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