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ETF Basket-Adjusted Covariance estimation

Kris Boudt, Kirill Dragun, Orimar Sauri and Steven Vanduffel ()

Journal of Econometrics, 2023, vol. 235, issue 2, 1144-1171

Abstract: The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for financial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of pre-estimators of the integrated covariance of the stocks included in the ETF basket. The proposed ETF Basket-Adjusted Covariance (BAC) equals the pre-estimator plus a minimal adjustment matrix such that the covariance-implied stock-ETF covariation equals a target value. We focus on a truncated pre-averaged version of the (Hayashi and Yoshida, 2005) pre-estimator and derive the asymptotic properties of its implied stock-ETF covariation. The simulation study confirms that the accuracy gains are substantial in all cases considered. In the empirical part of the paper, we show the gains in tracking error efficiency when using the BAC adjustment to construct portfolios that replicate a broad index using a subset of stocks.

Keywords: High-frequency data; Realized covariances; ETF; Asynchronicity; Stock-ETF covariation; Localized Hayashi–Yoshida; Index tracking (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1144-1171

DOI: 10.1016/j.jeconom.2022.10.002

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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