Academic journal article Review of European Studies

Some Approaches to the Calibration of Internal Rating Models

Academic journal article Review of European Studies

Some Approaches to the Calibration of Internal Rating Models

Article excerpt


This article covers the peculiarities of calibration of internal rating models which are the most popular approach to assessing credit risks. The authors address the most common approaches and methods used for rating models calibration, as well as propose their own algorithm for calibration, the main feature of which is taking into account the forecasted probability of default on the portfolio. Research methods include regression analysis, time series analysis (ARIMA models development). Reliability of the proposed approach has been verified on the basis of the portfolio, based on the debt obligations of Russia financial institutions. The advantage of the proposed approach towards determination of the average default probability of credit portfolio is that one gets a tool that allows you to construct a rating model that is "forward looking", respectively, appear to more quickly adapt to the changing patterns of rating environment.

Keywords: Basel II, credit risk, the probability of default, scoring points, correlation analysis

(ProQuest: ... denotes formulae omitted.)

1. Introduction

Recent global crisis indicated high interrelation between both financial institutions and even regional financial systems with the global systematic risk. Though fiscal policy of regional regulators can reduce the impact of negative trends in the local market, absence of such regulating authority on the global scale stipulates that reduction of "regional systematic" risk does not mean proper mitigation of the "world systematic" risk. Accordingly, a crisis in one region or country can cause the "domino effect" and consume all other regions and countries, despite the futile efforts of local regulators. This effect is especially material for developing countries. The impact could be observed via the so-called "flight to quality" effect, e.g. the size of the foreign investment in a particular country. Being the economic growth driver of any country, credit institutions are at the same time are the most susceptible to systematic risk: hence only proper risk mitigation policies can be viewed as pillars for survival and successful doing business for credit institutions. Traditional banks are by default exposed to credit risks (which is the nature of their business) and hence proper evaluation of those risks is the cornerstone of their profitability and survival.

It should be noted that the main study of statistical approaches towards rating model development are associated with the work of Altman E., Englmanna B., Erlenmeyer W., Hayden E., Tasha D. and others. For the validation of rating models should be noted works Kohavi R., Cook D., Picard R., Rauhmaer R. and others. The Russian researchers on these issues should be noted works Ayvazian S. A., Bukhtin M. A., Halavan S. V., Karminsky A. M., Lobanov A. A., Peresetsky A. A, Pomazanov M. V., Putilovsky V. A. et al.

However, the works of these authors largely contain general description of the problem, or are considering the individual stages of the development of rating models. Little relates to the issues of development of rating models for sub-portfolios with low or no defaults. (Burakov, 2014b; 2014c) Most of existing research relates also to the allocation of economic capital and implication of the risk weighting calculation as proposed by the Basel Committee.

To remain a going concern a credit institution must have adequate methods and tools to differentiate borrowers by level of credit risk. In accordance with Basel II internal rating models can be viewed as such instrument, as providing for the best way to estimate the level of capital adequacy to cover credit risks because rating models providing representative result.

Usually the rating model set up includes the following steps:

1) Development of the scoring model,

2) Calibration of the rating model.

The 1st step is to setup the following equation:

... (1)


Score is defined as value assigned to the borrower and reflecting its relative creditworthiness (Nurlybayeva & Balakayeva, 2013);

x1,. …

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