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Empirical Analysis Of Real Credit Risk Data

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  • Giuseppe Di Biase

Abstract

One important issue related to credit risk is the analysis of rating transitions and default rates. This consists of examining changes in the rating that international organizations give to firms that agree to be inspected. In this paper real credit risk data from the historical Standard & Poor’s database are used to calculate the actual cumulative default rate. I calculate the indicator considering both the starting rating class assigned to the firms and the elapsed time in the state before the default. The first part the paper points out that essentials of the credit rating and presents some descriptive statistics of the S&P historical database. Next, the paper shows the cumulative default rates of the financial instruments recorded by means an empirical model. The model considers two fundamental facts that standard reports do not contain. First, I consider the time elapsed in a given class before a rating change. I also consider the rating assessment named NR (No Rating) which represents the biggest class of ratings in the database

Suggested Citation

  • Giuseppe Di Biase, 2017. "Empirical Analysis Of Real Credit Risk Data," Accounting & Taxation, The Institute for Business and Finance Research, vol. 9(1), pages 97-108.
    • Handle: RePEc:ibf:acttax:v:9:y:2017:i:1:p:97-108
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    More about this item

    Keywords

    Standard and Poor's Rating Data; Empirical Model; Default Rate; Rating Transitions;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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