Predicting LGD distributions with mixed continuous and discrete ordinal outcomes

Ruey-Ching Hwang, Chih-Kang Chu and Kaizhi Yu

International Journal of Forecasting, 2020, vol. 36, issue 3, 1003-1022

Abstract: The loss given default (LGD) distribution is known to have a complex structure. Consequently, the parametric approach for its prediction by fitting a density function may suffer a loss of predictive power. To overcome this potential drawback, we use the cumulative probability model (CPM) to predict the LGD distribution. The CPM applies a transformed variable to model the LGD distribution. This transformed variable has a semiparametric structure. It models the predictor effects parametrically. The functional form of the transformation is unspecified. Thus, CPM provides more flexibility and simplicity in modeling the LGD distribution. To implement CPM, we collect a sample of defaulted debts from Moody’s Default and Recovery Database. Given this sample, we use an expanding rolling window approach to investigate the out-of-time performance of CPM and its alternatives. Our results confirm that CPM is better than its alternatives, in the sense of yielding more accurate LGD distribution predictions.

Keywords: Conditional distribution; Cumulative probability model; Logistic regression; Loss given default; Semiparametric transformation; Unconditional distribution (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:3:p:1003-1022

DOI: 10.1016/j.ijforecast.2019.10.005

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