IDEAS home Printed from https://ideas.repec.org/a/taf/irapec/v26y2012i1p125-143.html
   My bibliography  Save this article

Does non-linearity help us understand, model and forecast UK stock and bond returns: evidence from the BEYR

Author

Listed:
  • David G. McMillan

Abstract

The usefulness of non-linear models to provide accurate estimates and forecasts remains an open empirical debate. This paper examines the nature of the estimated relationships and forecasting power of smooth-transition models for UK stock and bond returns using a range of financial and macroeconomic variables as predictors. Notably, evidence of non-linearity is stronger when the bond-equity yield ratio is used as the transition variable. This ratio measures whether stocks are over (under)-valued relative to bonds and can act as a signal for portfolio managers. In-sample results reveal noticeable differences regarding the nature of relationships between the linear and non-linear setting, while results of a recursive forecasting exercise reveal both statistical and economic improvement over a linear model. Overall, these results support the view that non-linear estimates and forecasts can provide useful information for stock market traders, portfolio managers and policy-makers.

Suggested Citation

  • David G. McMillan, 2012. "Does non-linearity help us understand, model and forecast UK stock and bond returns: evidence from the BEYR," International Review of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 125-143, March.
    • Handle: RePEc:taf:irapec:v:26:y:2012:i:1:p:125-143
    DOI: 10.1080/02692171.2011.580268
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02692171.2011.580268
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02692171.2011.580268?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. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
    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. McMillan, David G., 2019. "Predicting firm level stock returns: Implications for asset pricing and economic links," The British Accounting Review, Elsevier, vol. 51(4), pages 333-351.
    2. Andreas Humpe & David G. McMillan, 2018. "Equity/bond yield correlation and the FED model: evidence of switching behaviour from the G7 markets," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 413-428, October.
    3. McMillan, David G., 2019. "Stock return predictability: Using the cyclical component of the price ratio," Research in International Business and Finance, Elsevier, vol. 48(C), pages 228-242.

    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. George Kapetanios & James Mitchell & Yongcheol Shin, 2010. "A Nonlinear Panel Model of Cross-sectional Dependence," Working Papers 673, Queen Mary University of London, School of Economics and Finance.
    2. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    3. Bruno Breyer Caldas & João Frois Caldeira & Guilherme Vale Moura, 2016. "Is Pairs Trading Performance Sensitive To The Methodologies?: A Comparison," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 130, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    4. Jan G. De Gooijer & Cees G. H. Diks & Łukasz T. Gątarek, 2012. "Information Flows Around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(1), pages 23-44, March.
    5. Guidolin, Massimo & Hyde, Stuart, 2012. "Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 695-716.
    6. Massimo Guidolin, 2013. "Markov switching models in asset pricing research," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 1, pages 3-44, Edward Elgar Publishing.
    7. Tania Morris & Jules Comeau, 2020. "Portfolio creation using artificial neural networks and classification probabilities: a Canadian study," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 133-163, June.
    8. Álvarez-Díaz, Marcos & Hammoudeh, Shawkat & Gupta, Rangan, 2014. "Detecting predictable non-linear dynamics in Dow Jones Islamic Market and Dow Jones Industrial Average indices using nonparametric regressions," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 22-35.
    9. Kapetanios, George & Mitchell, James & Shin, Yongcheol, 2014. "A nonlinear panel data model of cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 179(2), pages 134-157.
    10. Avino, Davide & Nneji, Ogonna, 2014. "Are CDS spreads predictable? An analysis of linear and non-linear forecasting models," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 262-274.
    11. Shiu-Sheng Chen, 2012. "Consumer confidence and stock returns over market fluctuations," Quantitative Finance, Taylor & Francis Journals, vol. 12(10), pages 1585-1597, October.
    12. Thomas Q. Pedersen, 2015. "Predictable Return Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 114-132, March.
    13. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    14. Yi Wei, 2021. "Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction," Papers 2101.10942, arXiv.org, revised Mar 2021.
    15. Giulia Dal Pra & Massimo Guidolin & Manuela Pedio & Fabiola Vasile, 2016. "Do Regimes in Excess Stock Return Predictability Create Economic Value? An Out-of-Sample Portfolio Analysis," BAFFI CAREFIN Working Papers 1637, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    16. Andrés Felipe Galeano Zurbaran, 2018. "Distribuciones no normales para la selección de activos en el mercado Colombiano," Documentos de Trabajo 17208, Quantil.
    17. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Advances in Econometrics, in: Missing Data Methods: Time-Series Methods and Applications, pages 1-86, Emerald Group Publishing Limited.
    18. Massacci, Daniele, 2013. "A switching model with flexible threshold variable: With an application to nonlinear dynamics in stock returns," Economics Letters, Elsevier, vol. 119(2), pages 199-203.
    19. Focardi, Sergio M. & Fabozzi, Frank J. & Mazza, Davide, 2019. "Modeling local trends with regime shifting models with time-varying probabilities," International Review of Financial Analysis, Elsevier, vol. 66(C).
    20. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2014. "Level shifts in stock returns driven by large shocks," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 41-51.

    More about this item

    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:taf:irapec:v:26:y:2012:i:1:p:125-143. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CIRA20 .

    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.