IDEAS home Printed from https://ideas.repec.org/p/zbw/eibwps/201805.html
   My bibliography  Save this paper

Sovereign risk and corporate cost of borrowing: Evidence from a counterfactual study

Author

Listed:
  • Wolski, Marcin

Abstract

We assess the impact of the sovereign risk spill-overs onto corporate cost of borrowing in selected euro area countries. We utilize a novel nonparametric dependence filtering frame- work to remove the effects of sovereign risk in the interest rate pass-through context. The main findings confirm the heterogeneity in sovereign risk spill-overs. We also find divergence in sovereign risk transmission between core and peripheral Member States during financial and sovereign debt crises. We discover that the standard linear models may underestimate the underlying pass-through distortions, suggesting the existence of nonlinear sovereign risk effects.

Suggested Citation

  • Wolski, Marcin, 2018. "Sovereign risk and corporate cost of borrowing: Evidence from a counterfactual study," EIB Working Papers 2018/05, European Investment Bank (EIB).
  • Handle: RePEc:zbw:eibwps:201805
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/180375/1/1026168651.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    2. Christoph Rothe, 2012. "Partial Distributional Policy Effects," Econometrica, Econometric Society, vol. 80(5), pages 2269-2301, September.
    3. Kitamura, Tomiyuki & Muto, Ichiro & Takei, Ikuo, 2016. "Loan interest rate pass-through and changes after the financial crisis: Japan’s evidence," Journal of the Japanese and International Economies, Elsevier, vol. 42(C), pages 10-30.
    4. Krylova, Elizaveta & Darracq Pariès, Matthieu & Moccero, Diego & Marchini, Claudia, 2014. "The retail bank interest rate pass-through: The case of the euro area during the financial and sovereign debt crisis," Occasional Paper Series 155, European Central Bank.
    5. Apergis, Nicholas & Cooray, Arusha, 2015. "Asymmetric interest rate pass-through in the U.S., the U.K. and Australia: New evidence from selected individual banks," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 155-172.
    6. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504, Enero.
    7. Qi Li & Juan Lin & Jeffrey S. Racine, 2013. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 57-65, January.
    8. Peter Hall & Michael C. Minnotte, 2002. "High order data sharpening for density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(1), pages 141-157, January.
    9. M. Jones, 1992. "Estimating densities, quantiles, quantile densities and density quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(4), pages 721-727, December.
    10. repec:wly:japmet:v:31:y:2016:i:7:p:1333-1351 is not listed on IDEAS
    11. Li, Qi & Racine, Jeffrey S, 2008. "Nonparametric Estimation of Conditional CDF and Quantile Functions With Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 423-434.
    12. Pierfederico Asdrubali & Simone Signore, 2015. "The Economic Impact of EU Guarantees on Credit to SMEs Evidence from CESEE Countries," European Economy - Discussion Papers 2015 - 002, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    13. Cees Diks & Marcin Wolski, 2016. "Nonlinear Granger Causality: Guidelines for Multivariate Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1333-1351, November.
    14. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    15. Arnold, Ivo J.M. & van Ewijk, Saskia E., 2014. "A state space approach to measuring the impact of sovereign and credit risk on interest rate convergence in the euro area," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 340-357.
    16. Hiemstra, Craig & Jones, Jonathan D, 1994. " Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    17. Rothe, Christoph, 2010. "Nonparametric estimation of distributional policy effects," Journal of Econometrics, Elsevier, vol. 155(1), pages 56-70, March.
    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. Costas Milas & Theodore Panagiotidis & Theologos Dergiades, 2018. "Twitter versus Traditional News Media: Evidence for the Sovereign Bond Markets," Working Paper series 18-42, Rimini Centre for Economic Analysis.

    More about this item

    Keywords

    counterfactual distributions; nonparametric methods; sovereign risk; cost of borrowing; pass-through;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:eibwps:201805. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - Leibniz Information Centre for Economics). General contact details of provider: http://edirc.repec.org/data/ceeiblu.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.