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On the serial correlation in multi-horizon predictive quantile regression

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  • Xu, Ke-Li

Abstract

This note presents a result on the serial correlation of the score process of the predictive quantile regression with multi-horizon outcomes. The result indicates that inference of multi-horizon quantile (or median) regression is more robust to serial correlation induced by overlapping observations, than the standard multi-horizon mean regression. The finding is illustrated by stock return data.

Suggested Citation

  • Xu, Ke-Li, 2021. "On the serial correlation in multi-horizon predictive quantile regression," Economics Letters, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:ecolet:v:200:y:2021:i:c:s0165176521000136
    DOI: 10.1016/j.econlet.2021.109736
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    References listed on IDEAS

    as
    1. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    2. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    3. Ke-Li Xu & Lauren Cohen, 2020. "Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based," Review of Financial Studies, Society for Financial Studies, vol. 33(9), pages 4403-4443.
    4. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    5. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 541-559, October.
    6. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    7. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    8. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice Rejoinder," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 574-575, October.
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    More about this item

    Keywords

    HAR inference; Long horizons; Overlapping observations; Predictive regression; Quantile regression;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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