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Model uncertainty and aggregated default probabilities: new evidence from Austria

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  • Paul Hofmarcher
  • Stefan Kerbl
  • Bettina Grün
  • Michael Sigmund
  • Kurt Hornik

Abstract

Understanding the determinants of aggregated corporate default probabilities (PDs) has attracted substantial research interest over the past decades. This study addresses two major difficulties in understanding the determinants of aggregate PDs: model uncertainty and multicollinearity among the regressors. We present Bayesian model averaging (BMA) as a powerful tool that overcomes model uncertainty. Furthermore, we supplement BMA with ridge regression to mitigate multicollinearity. We apply our approach to an Austrian data set. Our findings suggest that factor prices like short-term interest rates (STIs) and energy prices constitute major drivers of default rates, while firms' profits reduce the expected number of failures. Finally, we show that the results of our model are fairly robust with respect to the choice of the BMA parameters.

Suggested Citation

  • Paul Hofmarcher & Stefan Kerbl & Bettina Grün & Michael Sigmund & Kurt Hornik, 2014. "Model uncertainty and aggregated default probabilities: new evidence from Austria," Applied Economics, Taylor & Francis Journals, vol. 46(8), pages 871-879, March.
    • Handle: RePEc:taf:applec:v:46:y:2014:i:8:p:871-879
    DOI: 10.1080/00036846.2013.859378
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    Cited by:

    1. Marat Z. Kurbangaleev & Victor A. Lapshin & Zinaida V. Seleznyova, 2018. "Studying The Replicability Of Aggregate External Credit Assessments Using Public Information," HSE Working papers WP BRP 71/FE/2018, National Research University Higher School of Economics.
    2. Carvalho, Jaimilton & Orrillo, Jaime & da Silva, Fernanda Rocha Gomes, 2020. "Probability of default in collateralized credit operations for small business," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

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