The Robustness of Estimators in Structural Credit Loss Distributions

32 Pages Posted: 15 Jun 2016

See all articles by Enrique Batiz-Zuk

Enrique Batiz-Zuk

Banco de México

George A. Christodoulakis

Manchester Business School

Ser-Huang Poon

University of Manchester - Manchester Business School

Date Written: June 08, 2015

Abstract

This paper provides Monte Carlo results for the performance of the method of moments (MM), maximum likelihood (ML) and ordinary least squares (OLS) estimators of the credit loss distribution implied by the Merton (1974) and Vasicek (1987, 2002) framework when the common or idiosyncratic asset-return factor is non-Gaussian and, thus, the true credit loss distribution deviates from the theoretical one. We find that OLS and ML outperform MM in small samples when the true data-generating process comprises a non-Gaussian common factor. This result intensifies as the sample size increases and holds in all cases. We also find that all three estimators present a large bias and variance when the true data-generating process comprises a non-Gaussian idiosyncratic factor. This last result holds independently of the sample size, across different asset correlation levels, and it intensifies for positive shape parameter values.

Keywords: Basel III, credit risk, Monte Carlo, non-Gaussian distribution, skewed Student-t distribution, Vasicek loan loss distribution

Suggested Citation

Batiz-Zuk, Enrique and Christodoulakis, George A. and Poon, Ser-Huang, The Robustness of Estimators in Structural Credit Loss Distributions (June 08, 2015). Journal of Credit Risk, Vol. 11, No. 2, 2015. Available at SSRN: https://ssrn.com/abstract=2795521

Enrique Batiz-Zuk

Banco de México ( email )

Av. 5 de Mayo No. 6
Col. Centro, Deleg. Cuauhtémoc
Ciudad de México, DF, 06059
Mexico

George A. Christodoulakis (Contact Author)

Manchester Business School ( email )

Crawford House
Oxford Road
Manchester, Lancashire M15 6PB
United Kingdom
00447954487777 (Phone)

Ser-Huang Poon

University of Manchester - Manchester Business School ( email )

Crawford House
Oxford Road
Manchester, Manchester M13 9PL
United Kingdom
+44 161 275 4031 (Phone)
+44 161 275 4023 (Fax)

HOME PAGE: http://www.manchester.ac.uk/research/Ser-huang.poon/

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