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Determinants of corporate default: a BMA approach

Author

Listed:
  • Carlos González-Aguado

    (BLUECAP)

  • Enrique Moral-Benito

    (Banco de España)

Abstract

Model uncertainty hampers consensus on the main determinants of corporate default. We employ Bayesian model averaging (BMA) techniques in order to shed light on this issue. Empirical findings suggest that the most robust determinants of corporate default are firm-specific variables such as the ratio of working capital to total assets, the ratio of retained earnings to total assets, the ratio of total liabilities to total assets and the standard deviation of the firm’s stock return. In contrast, aggregate variables do not seem to play a relevant role once firm-specific characteristics (observable and unobservable) are taken into consideration

Suggested Citation

  • Carlos González-Aguado & Enrique Moral-Benito, 2012. "Determinants of corporate default: a BMA approach," Working Papers 1221, Banco de España.
    • Handle: RePEc:bde:wpaper:1221
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    References listed on IDEAS

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    1. Winford H. Masanjala & Chris Papageorgiou, 2008. "Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 671-682.
    2. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    3. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    4. Theo S. Eicher & Chris Papageorgiou & Adrian E. Raftery, 2011. "Default priors and predictive performance in Bayesian model averaging, with application to growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 30-55, January/F.
    5. Enrique Moral-Benito, 2012. "Determinants of Economic Growth: A Bayesian Panel Data Approach," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 566-579, May.
    6. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2013. "Firm Default And Aggregate Fluctuations," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 945-972, August.
    7. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
    8. Joseph Simonian, 2011. "A Bayesian approach to building robust structural credit default models," Applied Economics Letters, Taylor & Francis Journals, vol. 18(14), pages 1397-1400.
    9. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    10. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    11. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    12. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    13. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    14. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
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    Cited by:

    1. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    2. Joseph, Andreas & Osbat, Chiara, 2016. "How you export matters: the disassortative structure of international trade," Working Paper Series 1958, European Central Bank.
    3. Bulusu, Narayan & Guérin, Pierre, 2019. "What drives interbank loans? Evidence from Canada," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 427-444.
    4. Tan Khee Giap & Sasidaran Gopalan & Nursyahida Ahmad, 2018. "Growth Slowdown Analysis for Indonesia’s Subnational Economies: An Empirical Investigation," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 1-36, September.
    5. Karol Szafranek & Marek Kwas & Grzegorz Szafrański & Zuzanna Wośko, 2020. "Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach," Energies, MDPI, vol. 13(23), pages 1-23, November.
    6. Wang, Shengquan & Chen, Langnan, 2019. "Driving factors of equity bubbles," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 304-317.

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    More about this item

    Keywords

    Default probabilities; Bayesian model averaging; Credit Risk;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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