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Long-Horizon Return Regressions With Historical Volatility and Other Long-Memory Variables

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  • Natalia Sizova

Abstract

The predictability of long-term asset returns increases with the time horizon as estimated in regressions of aggregated-forward returns on aggregated-backward predictive variables. This previously established evidence is consistent with the presence of common slow-moving components that are extracted upon aggregation from returns and predictive variables. Long memory is an appropriate econometric framework for modeling this phenomenon. We apply this framework to explain the results from regressions of returns on risk measures. We introduce suitable econometric methods for construction of confidence intervals and apply them to test the predictability of NYSE/AMEX returns.

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  • Natalia Sizova, 2013. "Long-Horizon Return Regressions With Historical Volatility and Other Long-Memory Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 546-559, October.
    • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:4:p:546-559
    DOI: 10.1080/07350015.2013.827985
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    Cited by:

    1. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. Ke, Shuyao & Phillips, Peter C.B. & Su, Liangjun, 2024. "Robust inference of panel data models with interactive fixed effects under long memory: A frequency domain approach," Journal of Econometrics, Elsevier, vol. 241(2).
    3. Ilze Kalnina, 2023. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 538-549, April.
    4. Coqueret, Guillaume & Deguest, Romain, 2024. "Unexpected opportunities in misspecified predictive regressions," European Journal of Operational Research, Elsevier, vol. 318(2), pages 686-700.
    5. Meng-Chen Hsieh & Clifford Hurvich & Philippe Soulier, 2022. "Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes," Papers 2202.00793, arXiv.org.
    6. Andersen, Torben G. & Varneskov, Rasmus T., 2021. "Consistent inference for predictive regressions in persistent economic systems," Journal of Econometrics, Elsevier, vol. 224(1), pages 215-244.
    7. Bandi, F.M. & Perron, B. & Tamoni, A. & Tebaldi, C., 2019. "The scale of predictability," Journal of Econometrics, Elsevier, vol. 208(1), pages 120-140.
    8. Golinski, Adam & Madeira, Joao & Rambaccussing, Dooruj, 2014. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model of Stock Prices," SIRE Discussion Papers 2015-79, Scottish Institute for Research in Economics (SIRE).
    9. Lin, Qi, 2021. "The q5 model and its consistency with the intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 127(C).
    10. Golinski, Adam & Madeira, Joao & Rambaccussing, Dooruj, 2014. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model," MPRA Paper 58554, University Library of Munich, Germany.
    11. Cedric Okou & Eric Jacquier, 2014. "Horizon Effect in the Term Structure of Long-Run Risk-Return Trade-Offs," CIRANO Working Papers 2014s-36, CIRANO.
    12. Okou, Cédric & Jacquier, Éric, 2016. "Horizon effect in the term structure of long-run risk-return trade-offs," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 445-466.
    13. Guillaume Coqueret & Romain Deguest, 2024. "Unexpected opportunities in misspecified predictive regressions," Post-Print hal-04595355, HAL.
    14. Ilan Cooper & Paulo Maio, 2019. "Asset Growth, Profitability, and Investment Opportunities," Management Science, INFORMS, vol. 65(9), pages 3988-4010, September.

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