Let the data speak: a new approach to sovereign risk assessment

En bref

When dealing with credit risk both at the corporate and sov- ereign level, there is hardly any way around the major Credit Rating Agencies (CRAs), including e.g., Moody's, S&P, and Fitch. Their ratings are widely used and generally accepted. However, at least since the Enron crisis, the agencies have repeatedly come under criticism, not least for the lack of transparency in their approaches.

Highlights:

 

  • Insurance companies and asset managers need to rely on proprietary internal rating for sovereign bonds in order not to be exclusively dependent on rating agencies.
  • We introduce a new internal rating model that provides a purely quantitative assessment of sovereign credit risk, with-out resorting to potentially opaque subjective adjustments made by rating agencies. It uses a framework based on em-pirical evidence on the determinants of sovereign stress. The model relies on a limited set of parameters which are empirically validated and supported by economic theory. It can easily be applied to a large set of countries.
  • As no manual, qualitative adjustments are made, our model results – opposite to those of the rating agencies – cannot be criticised for subjective bias.
  • The model uses available macro data (including medium-term projections) in an efficient and transparent way, blending two approaches: a) a panel regression model, to perform out-of-sample projections, and b) a machine learning algorithm (k-means) to cluster countries according to credit risk indicators.
  • We currently cover 72 countries, with a focus on those most relevant for a liability-driven manager like Generali Insurance Asset Management (GIAM). The structure of the model is flexible enough to allow for a quick extension of the country coverage. In total, the model is based on a manageable set of roughly 20 economic time series variables.

 

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Core Matters I Let the data speak: a new approach to sovereign risk assessment
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