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Jurnal

Journal of Banking and Finance

Keyword

Loss given default,Downturn,Quantile regreesion,Recovery,Validation

Pengarang

  1. Steffen Krüger
  2. Daniel Rösch

    Subject

    1. FINANCIAL
    2. BANKING

      [Abstrak]

      Literature on Losses Given Default (LGD) usually
      focuses on mean predictions, even though losses are extremely skewed and
      bimodal. This paper proposes a Quantile Regression (QR) approach to get a
      comprehensive view on the entire probability distribution of losses. The method
      allows new insights on covariate effects over the whole LGD spectrum. In
      particular, middle quantiles are explainable by observable covariates while
      tail events, e.g., extremely high LGDs, seem to be rather driven by
      unobservable random events. A comparison of the QR approach with several
      alternatives from recent literature reveals advantages when evaluating downturn
      and unexpected credit losses. In addition, we identify limitations of classical
      mean prediction comparisons and propose alternative goodness of fit measures
      for the validation of forecasts for the entire LGD distribution.

      Periode

      Vol 79, Tahun 2017

      [Berkas]

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