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Estimation and inference in low frequency factor model regressions with overlapping observations

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  • Dossani, Asad

Abstract

A low frequency factor model regression uses changes or returns computed at a lower frequency than data available. Using overlapping observations to estimate low frequency factor model regressions results in more efficient estimates of OLS coefficients and standard errors, relative to using independent observations or high frequency estimates. I derive the relevant inference and propose a new method to correct for the induced autocorrelation. I present a series of simulations and empirical examples to support the theoretical results. In tests of asset pricing models, using overlapping observations results in lower pricing errors, compared to existing alternatives.

Suggested Citation

  • Dossani, Asad, 2024. "Estimation and inference in low frequency factor model regressions with overlapping observations," Journal of Empirical Finance, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000719
    DOI: 10.1016/j.jempfin.2024.101536
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    Keywords

    Autocorrelation; Asset pricing;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets

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