IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v21y2017icp144-150.html
   My bibliography  Save this article

Do liquidity variables improve out-of-sample prediction of sovereign spreads during crisis periods?

Author

Listed:
  • Kinateder, Harald
  • Hofstetter, Benedikt
  • Wagner, Niklas

Abstract

This paper addresses the out-of-sample prediction of European Monetary Union yield spread changes. We extend the Longstaff and Schwartz (1995) approach by using liquidity variables, namely funding liquidity as measured by European Central Bank’s unconventional monetary policy as well as a commonly used market liquidity proxy. Our out-of-sample results highlight that the economic forecasting models outperform the autoregressive moving average benchmark during times of crisis, when liquidity-based models yield superior predictions. However, the economic models do not yield forecasting gains during the pre-crisis period. Hence, our results provide evidence for the usefulness of economic models in predicting sovereign spreads during crisis periods.

Suggested Citation

  • Kinateder, Harald & Hofstetter, Benedikt & Wagner, Niklas, 2017. "Do liquidity variables improve out-of-sample prediction of sovereign spreads during crisis periods?," Finance Research Letters, Elsevier, vol. 21(C), pages 144-150.
  • Handle: RePEc:eee:finlet:v:21:y:2017:i:c:p:144-150
    DOI: 10.1016/j.frl.2016.11.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612316302896
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2016.11.006?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. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    2. Keim, Donald B. & Stambaugh, Robert F., 1986. "Predicting returns in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 17(2), pages 357-390, December.
    3. Francis A. Longstaff & Jun Pan & Lasse H. Pedersen & Kenneth J. Singleton, 2011. "How Sovereign Is Sovereign Credit Risk?," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 75-103, April.
    4. Batten, Jonathan A. & Fetherston, Thomas A. & Hoontrakul, Pongsak, 2006. "Factors affecting the yields of emerging market issuers: Evidence from the Asia-Pacific region," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(1), pages 57-70, February.
    5. Favero, Carlo A., 2013. "Modelling and forecasting government bond spreads in the euro area: A GVAR model," Journal of Econometrics, Elsevier, vol. 177(2), pages 343-356.
    6. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    7. António Afonso & Michael G. Arghyrou & Alexandros Kontonikas, 2014. "Pricing Sovereign Bond Risk In The European Monetary Union Area: An Empirical Investigation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 49-56, January.
    8. Grace Xing Hu & Jun Pan & Jiang Wang, 2013. "Noise as Information for Illiquidity," Journal of Finance, American Finance Association, vol. 68(6), pages 2341-2382, December.
    9. Longstaff, Francis A & Schwartz, Eduardo S, 1995. "A Simple Approach to Valuing Risky Fixed and Floating Rate Debt," Journal of Finance, American Finance Association, vol. 50(3), pages 789-819, July.
    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. Kinateder, Harald & Papavassiliou, Vassilios G., 2019. "Sovereign bond return prediction with realized higher moments," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 53-73.
    2. Apergis, Nicholas, 2022. "COVID-19 and cryptocurrency volatility: Evidence from asymmetric modelling," Finance Research Letters, Elsevier, vol. 47(PA).
    3. Narayan, Paresh Kumar & Ahmed, Huson Ali & Narayan, Seema, 2017. "Can investors gain from investing in certain sectors?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 160-177.
    4. Fan He & Xuansen He, 2019. "A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 729-761, August.
    5. Shahzad, Syed Jawad Hussain & Mensi, Walid & Hammoudeh, Shawkat & Balcilar, Mehmet & Shahbaz, Muhammad, 2018. "Distribution specific dependence and causality between industry-level U.S. credit and stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 114-133.

    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. Kinateder, Harald & Wagner, Niklas, 2017. "Quantitative easing and the pricing of EMU sovereign debt," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 1-12.
    2. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    3. Eleonora Cutrini & Giorgio Galeazzi, 2017. "External Public Debt, Trade Linkages and Contagion During the Eurozone Crisis," The World Economy, Wiley Blackwell, vol. 40(9), pages 1718-1749, September.
    4. Daniel Mantilla-García & Vijay Vaidyanathan, 2017. "Predicting stock returns in the presence of uncertain structural changes and sample noise," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(3), pages 357-391, August.
    5. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
    6. Sharma, Susan Sunila & Thuraisamy, Kannan, 2013. "Oil price uncertainty and sovereign risk: Evidence from Asian economies," Journal of Asian Economics, Elsevier, vol. 28(C), pages 51-57.
    7. Hai Lin & Daniel Quill & Henk Berkman, 2016. "Information diffusion and the predictability of New Zealand stock market returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 56(3), pages 749-785, September.
    8. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
    9. Tom Engsted & Stig V. Møller & Magnus Sander, 2013. "Bond return predictability in expansions and recessions," CREATES Research Papers 2013-13, Department of Economics and Business Economics, Aarhus University.
    10. Eleonora Cutrini and Giorgio Galeazzi, 2014. "Contagion in the Euro crisis: capital flows and trade linkages," Working Papers 44-2014, Macerata University, Department of Studies on Economic Development (DiSSE), revised Nov 2014.
    11. Chava, Sudheer & Gallmeyer, Michael & Park, Heungju, 2015. "Credit conditions and stock return predictability," Journal of Monetary Economics, Elsevier, vol. 74(C), pages 117-132.
    12. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    13. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    14. Shi, Yukun & Stasinakis, Charalampos & Xu, Yaofei & Yan, Cheng, 2022. "Market co-movement between credit default swap curves and option volatility surfaces," International Review of Financial Analysis, Elsevier, vol. 82(C).
    15. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    16. Atanasov, Victoria, 2018. "World output gap and global stock returns," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 181-197.
    17. Ekaterini Panopoulou & Sotiria Plastira, 2014. "Fama French factors and US stock return predictability," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 110-128, April.
    18. Wegener, Christian & von Nitzsch, Rüdiger & Cengiz, Cetin, 2010. "An advanced perspective on the predictability in hedge fund returns," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2694-2708, November.
    19. Hai Lin & Xinyuan Tao & Junbo Wang & Chunchi Wu, 2020. "Credit Spreads, Business Conditions, and Expected Corporate Bond Returns," JRFM, MDPI, vol. 13(2), pages 1-34, January.
    20. Pan, Zheyao & Chan, Kam Fong, 2018. "A new government bond volatility index predictor for the U.S. equity premium," Pacific-Basin Finance Journal, Elsevier, vol. 50(C), pages 200-215.

    More about this item

    Keywords

    EMU sovereign debt; Market liquidity; Out-of-sample prediction; Predictability of yield spread changes;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:finlet:v:21:y:2017:i:c:p:144-150. 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/frl .

    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.