Detail publikace

Modeling a Distribution of Mortgage Credit Losses

Autor: PhDr. Petr Gapko Ph.D.,
RNDr. Martin Šmíd Ph.D.,
Typ: IES Working Papers
Rok: 2010
Číslo: 23
Publikováno v: IES Working Papers 23/2010
Místo vydání: Prague
Klíčová slova: Credit Risk, Mortgage, Delinquency Rate, Generalized Hyperbolic Distribution, Normal Distribution
JEL kódy: G21
Citace: Gapko, P., Šmíd, M. (2010). “Modeling a Distribution of Mortgage Credit Losses” IES Working Paper 23/2010. IES FSV. Charles University.
Granty: 402/09/0965: Nové přístupy pro monitorování a predikci na kapitálových trzích 402/09/H045 - Nelineární dynamika v peněžní ekonomii a financích. Teorie a empirické modely GAUK 46108: Nové nelineární teorie kapitálových trhů: fraktální, bifurkační a behaviorální přístup
Abstrakt: 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.
Ke stažení: WP 2010_23_Gapko, Šmíd


McKinsey & Company