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Weighted Least Squares Realized Covariation Estimation

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  • Li, Yifan
  • Nolte, Ingmar
  • Vasios, Michalis
  • Voev, Valeri
  • Xu, Qi

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.

Suggested Citation

  • Li, Yifan & Nolte, Ingmar & Vasios, Michalis & Voev, Valeri & Xu, Qi, 2022. "Weighted Least Squares Realized Covariation Estimation," Journal of Banking & Finance, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:jbfina:v:137:y:2022:i:c:s0378426622000206
    DOI: 10.1016/j.jbankfin.2022.106420
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    More about this item

    Keywords

    Market Microstructure Noise; Realized Volatility; Realized Covariation; Weighted Least Squares; Volatility Forecasting; Asset Allocation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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