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The measure of model risk in credit capital requirements

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  • Roberto Baviera

Abstract

Credit capital requirements in Internal Rating Based approaches require the calibration of two key parameters: the probability of default and the loss-given-default. This letter considers the uncertainty about these two parameters and models this uncertainty in an elementary way: it shows how this estimation risk can be computed and properly taken into account in regulatory capital. We analyse two standard real datasets: one composed by all corporates rated by Moody's and one limited only to the speculative grade ones. We statistically test model hypotheses on both marginal distributions and parameter dependency. We compute the estimation risk impact and observe that parameter dependency raises substantially the tail risk in capital requirements. The results are striking with a required increase in regulatory capital in the range $38\%$-$66\%$.

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  • Roberto Baviera, 2020. "The measure of model risk in credit capital requirements," Papers 2010.08028, arXiv.org.
    • Handle: RePEc:arx:papers:2010.08028
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