Publication detail

Modeling a Distribution of Mortgage Credit Losses

Author(s): PhDr. Petr Gapko Ph.D.,
RNDr. Martin Šmíd Ph.D.,
Type: IES Working Papers
Year: 2010
Number: 23
Published in: IES Working Papers 23/2010
Publishing place: Prague
Keywords: Credit Risk, Mortgage, Delinquency Rate, Generalized Hyperbolic Distribution, Normal Distribution
JEL codes: G21
Suggested Citation: Gapko, P., Šmíd, M. (2010). “Modeling a Distribution of Mortgage Credit Losses” IES Working Paper 23/2010. IES FSV. Charles University.
Grants: 402/09/0965: New Approaches for monitoring and prediction of capital markets 402/09/H045 - Nelineární dynamika v peněžní ekonomii a financích. Teorie a empirické modely GAUK 46108: New Nonlinear Capital Markets Theories: Fractal, Bifurcational and Behavioral Approach
Abstract: One of the biggest risks arising from financial operations is the risk of counterparty
default, commonly known as a “credit risk”. Leaving unmanaged, the credit risk
would, with a high probability, result in a crash of a bank. In our paper, we will
focus on the credit risk quantification methodology. We will demonstrate that the
current regulatory standards for credit risk management are at least not perfect,
despite the fact that the regulatory framework for credit risk measurement is more
developed than systems for measuring other risks, e.g. market risks or operational
risk. Generalizing the well known KMV model, standing behind Basel II, we build a
model of a loan portfolio involving a dynamics of the common factor, influencing
the borrowers’ assets, which we allow to be non-normal. We show how the
parameters of our model may be estimated by means of past mortgage deliquency
rates. We give a statistical evidence that the non-normal model is much more
suitable than the one assuming the normal distribution of the risk factors. We point
out how the assumption that risk factors follow a normal distribution can be
dangerous. Especially during volatile periods comparable to the current crisis, the
normal distribution based methodology can underestimate the impact of change in
tail losses caused by underlying risk factors.
Downloadable: WP 2010_23_Gapko, Šmíd


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