IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v196y2020ics0165176520303219.html
   My bibliography  Save this article

A new test of asset return predictability with an unstable predictor

Author

Listed:
  • Chang, Seong Yeon

Abstract

This study constructs predictive regressions in which the predictable variable exhibits a level shift at some unknown date. We establish novel procedures to test asset return predictability via empirical likelihood (EL) methods based on weighted score equations. Monte Carlo simulations confirm that the EL-based tests perform well in terms of size and power in finite samples.

Suggested Citation

  • Chang, Seong Yeon, 2020. "A new test of asset return predictability with an unstable predictor," Economics Letters, Elsevier, vol. 196(C).
  • Handle: RePEc:eee:ecolet:v:196:y:2020:i:c:s0165176520303219
    DOI: 10.1016/j.econlet.2020.109529
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176520303219
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2020.109529?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
    2. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    3. David E. Rapach & Mark E. Wohar, 2006. "Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(2), pages 238-274.
    4. Fukang Zhu & Zongwu Cai & Liang Peng, 2014. "Predictive regressions for macroeconomic data," Papers 1404.7642, arXiv.org.
    5. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    6. Alexandros Kostakis & Tassos Magdalinos & Michalis P. Stamatogiannis, 2015. "Robust Econometric Inference for Stock Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 28(5), pages 1506-1553.
    7. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    8. Chan, Ngai Hang & Li, Deyuan & Peng, Liang, 2012. "Toward A Unified Interval Estimation Of Autoregressions," Econometric Theory, Cambridge University Press, vol. 28(3), pages 705-717, June.
    9. Cavanagh, Christopher L. & Elliott, Graham & Stock, James H., 1995. "Inference in Models with Nearly Integrated Regressors," Econometric Theory, Cambridge University Press, vol. 11(5), pages 1131-1147, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Toloo, Mehdi & Tone, Kaoru & Izadikhah, Mohammad, 2023. "Selecting slacks-based data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1302-1318.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    2. Gungor, Sermin & Luger, Richard, 2020. "Small-sample tests for stock return predictability with possibly non-stationary regressors and GARCH-type effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 750-770.
    3. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
    4. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2017. "International stock return predictability: Evidence from new statistical tests," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 97-113.
    5. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    6. Pitarakis, Jean-Yves, 2019. "Predictive Regressions," UC3M Working papers. Economics 28554, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    8. Wachter, Jessica A. & Warusawitharana, Missaka, 2009. "Predictable returns and asset allocation: Should a skeptical investor time the market?," Journal of Econometrics, Elsevier, vol. 148(2), pages 162-178, February.
    9. Jean-Yves Pitarakis, 2017. "A Simple Approach for Diagnosing Instabilities in Predictive Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 851-874, October.
    10. Victoria Atanasov & Stig V. Møller & Richard Priestley, 2020. "Consumption Fluctuations and Expected Returns," Journal of Finance, American Finance Association, vol. 75(3), pages 1677-1713, June.
    11. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2019. "Structural instability and predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    12. Jennie Bai, 2010. "Equity premium predictions with adaptive macro indexes," Staff Reports 475, Federal Reserve Bank of New York.
    13. Jesús Gonzalo & Jean-Yves Pitarakis, 2011. "Regime-Specific Predictability in Predictive Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 229-241, June.
    14. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    15. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
    16. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    17. Chiquoine, Benjamin & Hjalmarsson, Erik, 2009. "Jackknifing stock return predictions," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 793-803, December.
    18. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    19. repec:wyi:journl:002108 is not listed on IDEAS
    20. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.
    21. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.

    More about this item

    Keywords

    Autoregressive process; Empirical likelihood; Level shift; Local-to-unity; Weighted estimation;
    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:196:y:2020:i:c:s0165176520303219. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.