ASSESSING COUNTRY RISK USING MULTICRITERIA CLASSIFICATION APPROACHES

E. Gjonca, M. Doumpos, G. Baourakis and C. Zopounidis
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E. Gjonca: Mediterranean Agronomic Institute of Chania, Dept. of Economics, Marketing and Finance, 73100 Chania, Greece
M. Doumpos: Mediterranean Agronomic Institute of Chania, Dept. of Economics, Marketing and Finance, 73100 Chania, Greece
G. Baourakis: Mediterranean Agronomic Institute of Chania, Dept. of Economics, Marketing and Finance, 73100 Chania, Greece
C. Zopounidis: Technical University of Crete, Dept. of Production Engineering and Management, Financial Engineering Laboratory, University Campus, 73100 Chania, Greece

Chapter 4 in Supply Chain and Finance, 2004, pp 49-67 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractCountry risk evaluation is an important component of the investment and capital budgeting decisions of banks, international lending institutions and international investors. The increased internationalization of the global economy in recent decades has raised the exposure to risks associated with events in different countries. Consequently, substantial resources are now being devoted to country risk analysis by international organizations and investors who realize the importance of identifying, evaluating and managing the risks they face. This study presents the contribution of multicriteria decision aid in country risk assessment. The proposed approach is based on multicriteria decision aid classification methods, namely the UTADIS method (UTilités Additives DIScriminantes) and the MHDIS method (Multi-group Hierarchical DIScrimination). Both methods lead to the development of country risk classification models in the form of additive utility functions that classify a set of countries into predefined risk classes. The efficiency of the proposed methods is illustrated through a case study using data derived by the World Bank. The two multicriteria methods are employed to develop appropriate models for the classification of countries into five risk groups, according to their creditworthiness and risk level. Several validation tests are performed in order to compare the classification results of the two methods with the corresponding results obtained from statistical and econometric analysis techniques.

Keywords: Finance; Supply Chain; E-Commerce; Optimization; Mathematical Modeling; Operations Research (search for similar items in EconPapers)
Date: 2004
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