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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    9. 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.
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    Cited by:

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    2. 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.
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    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

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