The Estimation Risk and the IRB Supervisory Formula

23 Pages Posted: 26 Jan 2021

See all articles by Simone Casellina

Simone Casellina

European Banking Authority

Simone Landini

IRES Piemonte, Socio-Economic Research Institute of Piedmont

Mariacristina Uberti

University of Turin

Date Written: January 14, 2021

Abstract

In many standard derivation and presentations of risk measures like the Value-at-Risk or the Expected Shortfall, it is assumed that all the model’s parameters are known. In practice, however, the parameters must be estimated and this introduces an additional source of uncertainty that is usually not accounted for. The Prudential Regulators have formally raised the issue of errors stemming from the internal model estimation process in the context of credit risk, calling for margins of conservatism to cover possible underestimation in capital. Notwithstanding this requirement, to date, a solution shared by banks and regulators/supervisors has not yet been found. In our paper, we investigate the effect of the estimation error in the framework of the Asymptotic Single Risk Factor model that represents the baseline for the derivation of the credit risk measures under the IRB approach. We exploit Monte Carlo simulations to quantify the bias induced by the estimation error and we explore an approach to correct for this bias. Our approach involves only the estimation of the long run average probability of default and not the estimation of the asset correlation given that, in practice, banks are not allowed to modify this parameter. We study the stochastic characteristics of the probability of default estimator that can be derived from the Asymptotic Single Risk Factor framework and we show how to introduce a correction to control for the estimation error. Our approach does not require introducing in the Asymptotic Single Risk Factor model additional elements like the prior distributions or other parameters which, having to be estimated, would introduce another source of estimation error. This simple and easily implemented correction ensures that the probability of observing an exception (i.e. a default rate higher than the estimated quantile of the default rate distribution) is equal to the desired confidence level. We show a practical application of our approach relying on real data.

Keywords: Bank Capital, Regulation, Basel 2, Margin of Conservatism, Value-at-Risk

JEL Classification: C15, G21, G32

Suggested Citation

Casellina, Simone and Landini, Simone and Uberti, Mariacristina, The Estimation Risk and the IRB Supervisory Formula (January 14, 2021). European Banking Authority Research Paper No. 11, Available at SSRN: https://ssrn.com/abstract=3773406 or http://dx.doi.org/10.2139/ssrn.3773406

Simone Casellina (Contact Author)

European Banking Authority ( email )

20 avenue André Prothin CS 30154
One Canada Square, Canary Wharf
92927 Paris, La Défense CEDEX E14 5AA
France

Simone Landini

IRES Piemonte, Socio-Economic Research Institute of Piedmont ( email )

Via Nizza 18
Turin, Turin 10125
Italy
+390116666404 (Phone)
+390116666469 (Fax)

Mariacristina Uberti

University of Turin ( email )

Via Po 53
Torino, Turin - Piedmont 10100
Italy

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