Weighted Least Squares Realized Covariation Estimation☆

https://doi.org/10.1016/j.jbankfin.2022.106420Get rights and content
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Abstract

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.

Keywords

Market Microstructure Noise
Realized Volatility
Realized Covariation
Weighted Least Squares
Volatility Forecasting
Asset Allocation

JEL classification

C13
C22
G10

Cited by (0)

☆

We would like to thank Carol Alexander (the editor), two anonymous referees, Yacine Ait-Sahalia, Torben Andersen, Michael Brennan, Jianqing Fan, Peter Hansen, Ilze Kalnina, Asger Lunde, Nikolaus Hautsch, Sandra Nolte, Mark Podolskij, Yoann Potiron, Mark Salmon, Kevin Sheppard, Stephen Taylor, Rasmus Varneskov, Almut Veraart, Dacheng Xiu, and Jun Yu as well as participants of the Frontiers of Finance 2012, the International Finance and Banking Society 2013, the Statistical Week 2013 conferences, International Association of Applied Econometrics 2015, Asian Meeting of Econometric Society 2017, China Meeting of Econometric Society 2017, European Finance Association 2017, Financial Econometrics and New Finance Conference 2018, and faculty seminar participants at Göttingen, Karlsruhe, Kiel, King’s College London, Lancaster, Manchester, Nottingham, and Zhejiang for helpful comments and discussions. We would like to acknowledge financial support from the ESRC-FWF bilateral grant titled “Bilateral Austria: Order Book Foundations of Price Risks and Liquidity: An Integrated Equity and Derivatives Markets Perspective”, Grant Ref:ES/N014588/1. Earlier versions of the paper were circulated under the title “A Least Squares Regression Realized Covariation Estimation”,“Estimating High-Frequency Based (Co-) Variances: A Unified Approach” and “A Least Squares Regression Realized Covariation Estimation under MMS Noise and Non-synchronous Trading”. The views expressed in this paper are those of the authors and not necessarily those of European Securities and Markets Authority, and Michalis Vasios has worked on this paper while he was at the University of Warwick. All remaining errors are ours.

© 2022 The Authors. Published by Elsevier B.V.