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A unified test for predictability of asset returns regardless of properties of predicting variables

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  • Liu, Xiaohui
  • Yang, Bingduo
  • Cai, Zongwu
  • Peng, Liang

Abstract

Some unified tests have been proposed recently in the literature for testing predictability of asset returns based on a simple linear predictive regression model, which has a drawback that predicted variable cannot be stationary if the predicting variable is nonstationary. To solve this issue, this paper includes the difference of the predicting variable into the simple linear predictive regression. Furthermore, a unified empirical likelihood inference is developed to test the predictability regardless of the properties of the predicting variable. A simulation study is conducted to confirm the efficiency of the proposed methods before applying to a real example.

Suggested Citation

  • Liu, Xiaohui & Yang, Bingduo & Cai, Zongwu & Peng, Liang, 2019. "A unified test for predictability of asset returns regardless of properties of predicting variables," Journal of Econometrics, Elsevier, vol. 208(1), pages 141-159.
    • Handle: RePEc:eee:econom:v:208:y:2019:i:1:p:141-159
    DOI: 10.1016/j.jeconom.2018.09.009
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Christis Katsouris, 2023. "Unified Inference for Dynamic Quantile Predictive Regression," Papers 2309.14160, arXiv.org, revised Nov 2023.
    2. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    3. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    4. Tu, Yundong & Xie, Xinling, 2023. "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, vol. 237(1).
    5. Serna, Gregorio, 2023. "On the predictive ability of conditional market skewness," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 186-191.
    6. Liu, Guannan & Yao, Shuang, 2020. "A robust test for predictability with unknown persistence," Economics Letters, Elsevier, vol. 189(C).
    7. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    8. Atance, David & Serna, Gregorio, 2024. "Time-varying expected returns, conditional skewness and Bitcoin return predictability," The Quarterly Review of Economics and Finance, Elsevier, vol. 96(C).
    9. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.

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    More about this item

    Keywords

    Empirical likelihood; Predictive regression; Weighted score;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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