Risultati dell’indagine sul Rischio Paese condotta con le metodologie MCDM e SOM

1. Risultati dell’indagine sul Rischio Paesecondotta con le metodologie MCDM e SOM PhD Francesca Bernè, ing. Mattia Ciprian francescab@econ.units.it mciprian@units.it

2. Introduction - Generation Models - Country data - Ratios • An overview of country risk • Modelling financial crisis: the generation approach • Financial crises: an historical perspective • Country risk assessment methodologies • Country data and ratios

3. Introduction - GenerationModels - Country data - Ratios First-generation model (Krugman, 1979) Fixed exchange rate + budgetdeficit + monetary expansion = drop in reserves financial crisis + devaluation Second-generation crisis model (Obstfeld, 1985) Unsustainable fixed parity + current account deficit Portofolio deficit = capital outflows = reserves exhaustion Exchange rate depreciation

4. Introduction - GenerationModels - Country data - Ratios Third-generation crisis model (Krugman, 1997; Radelet and Sachs, 1998) Weak financial intermediation institutions + bad governance Speculative short-term capital flows = debt overhang and official reserve drop Financial panic and bank liquidations

5. Introduction – GenerationModels – Country data - Ratios Second-generation adjusted model of self-fulfilling crisis (Williamson, 2002) Macroeconomic fundamentals in intermediate situation (growth, inflation, current account, budget, debt) “Multiple equilibria” depending on: • regional contanimation + speculative attacks = “bad equilibrium” = default • robust adjustement credibility = “good” equilibrium = sustainable debt servicing = capital market access

6. Introduction – GenerationModels - Country data - Ratios Other models • Maturity mismatch • Currency mismatch

7. Introduction – GenerationModels - Country data - Ratios Historical perspective Mexican crisis(1994) Russian crisis (1998) Asian crisis (1997-1998) Argentina crisis (2001-2002)

8. Introduction - Generation Models - Country data - Ratios Country data Sources UNDP WORLD BANK COFACE OECD UNCTAD ICRG IMF CIA ISAE

9. Country data • Countries (all the world): • Europa and CSI, Americas, Asia and Oceania, Nord Africa and Middle East, Sub-saharian Africa 52 Countries, 22 ratios, year 2004, Argentina 2001-2002 Source: CIA, COFACE • Emerging Market Countries, Developing Countries, Least Development Countries: 27 Countries, 18 ratios, period 1983 - 2000 (18 years) Source: ISAE Introduction – Generation Models – Country data - Ratios

10. Ratios birth rate, death rate, debt external, exports (variation %), imports (variation%), GDP purchasing power parity, GDP no purchasing power parity, inflation rate, public balance/GDP, growth, net migration rate, investment, reserve foreign exchange & gold, infantility mortality rate, life expectancy at birth, fertility rate, labour force, internet users, industrial production growth rate electricity consumption, electricity production, oil consumption Introduction – Generation Models – Country data - Ratios

11. Ratios default S&P’s, default state t-1 S&P’s, GDP billion $, GDP growth rate, rate of inflation, exchange rate in purchasing power parity, average interest rate, exports, foreign direct investment, imports, total interest payment, international reserve, total external debt, short term external debt, interest on short term external debt, total debt service, long term debt service, current account balance Introduction – Generation Models – Country data - Ratios

12. SUMMARY • Introduction; • Tools: MCDM; SOM; • Results; • Conclusions.

13. Introduction – Tools (MCDM; SOM) -Results– Conclusions INTRODUCTION The increasing complexity of financial problems over the past decades has driven analysts to develop and adopt more sophisticated quantitative analysis techniques: furthermore, in the last years, is growing the opinion that the criterion to guide financial decisions has to be multidimensional.

14. MCDM SOM Examples • M. Doumpos, C. Zopounidis : Assessing financial risks using a multicriteria sorting procedure: the case of country risk assessment. OMEGA, vol.29, no. 1, 97-109, April 2000 • C. Zopounidis, M. Doumpos : Multi-criteria decision aid in financial decision making: methodologies and literature review. Journal Of Multi-Criteria Decision Analysis, 2002 • C. Zopounidis, M. Doumpos : On the Use of a Multi-Criteria Hierarchical Discrimination Approach for Country Risk Assessment. Journal Of Multi-Criteria Decision Analysis, 2002 • G. Deboeck & T. Kohonen : Visual Explorations in Finance with self organizing maps, Springer-Verlag, 1998 • G. Deboeck : Data mining with Self Organizing Maps: a practical application, internet, 1998 Introduction – Tools (MCDM; SOM) -Results– Conclusions

15. Introduction – Tools (MCDM; SOM) -Results– Conclusions MCDM Decision has inspired reflection of many thinkers since the ancient times. The great philosophers Aristotle, Plato, and Thomas Aquinas, to mention only a few names, discussed the capacity of humans to decide and in some manners claimed that this possibility is what distinguishes humans from animals. Classically, for example in economics, it is supposed that preference can be represented by a utility function assigning a numerical value to each action such that the more preferable an action, the larger its numerical value. Moreover, it is very often assumed that the comprehensive evaluation of an action can be seen as the sum of its numerical values for the considered criteria. Let us call this the classical model. It is very simple but not too realistic [J. Figueira, S. Greco, M. Ehrgott “Multiple Criteria Decision Analysis: STATE OF THE ART SURVEYS” Kluwer, 2005].

16. Introduction – Tools (MCDM; SOM) -Results– Conclusions MCDM • Interactive methods; • Multi Attribute Utility Theory (MAUT); • Outranking Methods. • CODASID

17. Introduction – Tools (MCDM; SOM) -Results– Conclusions CODASID This method attempts to generate a clear preference order for alternative designs. The basic concept is that the best action should have the shortest distance from an ideal design and the greatest from a negative ideal design. The inputs required by CODASID are • a matrix containing the objects to be explored during the decision procedure; • a vectorof weights expressing the relative importance of one attribute with respect to the others.

18. Introduction – Tools (MCDM; SOM) -Results– Conclusions SOM The self-organizing map (SOM) is a new, effective software tool for the visualization of high-dimensional data. It implements an orderly mapping of a high-dimensional distribution onto a regular low-dimensional grid. Thereby it is able to convert complex, non-linear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. As it compresses information while preserving the most important topological and metric relationships of the primary data items on the display, it may also be thought to produce some kind of abstractions. [“The self-organizing map”, T. Kohonen, Neurocomputing 21, 1998].

19. Self Organizing Maps Introduction – Tools (MCDM; SOM) -Results– Conclusions

20. Results Evolution of developing countries We have obtained very encouraging results: some of they are represented in figure. It is worth to note that every important fallout, historically occurred to a country, has been detected as a rank variation. Introduction – Tools (MCDM; SOM) - Results(MCDM) – Conclusions

21. 1989, China: the Tienanmen massacre; 1991 - 1992, Philippines: recession; 1993, Phil.: the Ramos administration benefits 1994 -1995, China: political repression in Tibet; 1997 -1998: the asian economic crisis Argentina: the intricate economic situation culminated to the crisis during 2001 -2002 Results Evolution of developing countries Introduction – Tools (MCDM; SOM) - Results(MCDM) – Conclusions

22. Results Ranking obtained from CODASID Introduction – Tools (MCDM; SOM) - Results(MCDM) – Conclusions

23. Results SOM We have conducted two kinds of analysis on data: • factor study and individuation of local correlations in order to understand the logics joining the economics and social aspects of countries: we trained a rectangular map with 20x10 hexagonal nodes during 15000 cycles; • a clustering analysis based on credit risks, to produce a visual representation, and to verify the World Bank's classification according to the income level. Introduction – Tools (MCDM; SOM) - Results(SOM) – Conclusions

24. Factor study • We have obtained, as foreseen by us, some obvious relations as between Energy Consumption and Energy Production. • The correlation is 0,963 Introduction – Tools (MCDM; SOM) - Results(SOM) – Conclusions

25. Factor study - Local correlation • After all SOMs are very powerful in showing, in certain areas, the non-linear dependencies as in fig. The statistical correlation between the factors GDP Growth Rate and Industrial Production Growth Rate is very low: 0,347. Introduction – Tools (MCDM; SOM) - Results(SOM) – Conclusions

26. It is worth to note that HI and LI economies are easier to identify than intermediate cases such as LM and UM income economies, as reported earlier. Cluster analysis Introduction – Tools (MCDM; SOM) - Results(SOM) – Conclusions

27. Conclusions As Meldrum said in a recent work “a company needs to examine the relationship between risk and its businesses to make sure risk measures actually help the company improve its business decisions”; an additional risk is represented by country risk. In our paper we show how SOM and MCDM could be used as well tools to analyze this risk: both proved efficient in handling many data together and producing a consistent classification of countries according to their risk level. The results have been compared with other recent works. Introduction – Tools (MCDM; SOM) - Results – Conclusions