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# Country Risk Classification and Multiriteria Decision Aid - PowerPoint PPT Presentation

Country Risk Classification and Multiriteria Decision Aid. Xijun Wang January 26, 2004. Outline. Country Risk Classification Country Risk Classification Methods Utilities Additive Discrimination Multigroup Hierarchical Discrimination Dealing with Complex Factors Future Works.

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### Country Risk Classification and Multiriteria Decision Aid

Xijun Wang

January 26, 2004

• Country Risk Classification

• Country Risk Classification Methods

• Multigroup Hierarchical Discrimination

• Dealing with Complex Factors

• Future Works

• The overall risk of loaning money to foreign companies.

• How much is debt delayed and how much is the return?

• Help financial institutions in decision-making

• Measurements

• Risk levels C1, C2 ,…, Cq,

• Evaluation factors

• Population structure, education, political and social status, economics, financial status

• Determine the risk level of a country based on various factors

• Early used statistical methods: Bayesian discrimination,

• Simple to implement

• Not widely used due to unrealistic statistics assumptions

• Recent approaches based on optimization: Multicriteria decision-aid methods

• No statistics assumption

• Background knowledge incorporated

Cq

Ck

C1

U(c)

μq-1

μk

μk-1

μ1

Utility Function

• Utility function U(c) is an indicator of the risk level of a country

• Risk level of country a is higher than of b, then U(a)<U(b)

• Borderlines to separate different risk levels

Cq

Ck

C1

σ+(c)

U(c)

μq-1

μk

μk-1

μ1

Cq

Ck

C1

σ-(c)

U(c)

μq-1

μk

μk-1

μ1

• Learning the utility function and the thresholds in the function space.

• But, in practice, we might not find threshholds and utility functions that can predict all the country risk levels correctly

Piecewise linear marginal utility function

• Learning model: minimizing total training classification error

• Estimated Marginal Utility functions

C¬k

Ck

Uk(c)

U¬k(c)

Multigroup Hierarchical Discrimination (1)

• Hierarchical classification process

• Is it in level C1?

• If not, is it in level C2?

• Suppose we have

• Uk(c): similarity measure of c to countries in Ck

• U¬k(c): similarity measure of c to countries in C¬k=Ck+1…Cq

• Is c in Ck or C¬k?  Is Uk(c)> U¬k(c) or not?

• Learning Uk(c) and U¬k (c)

• Minimizing the number of misclassifications?

• First, minimize total classification error, like in UTADIS

• Second, further minimize number of misclassifications

• Finally, make Uk and U¬k most distinguished on training examples, without changing the correctness of any training example

• Non-monotone factors exists, such as birthrate, military expenditure

• Allow unimodal utility function

• Leave one out test

Conclusion and Future Works military expenditure

• Discussed two MCDA methods for country risk classification

• MHDIS

• Discussed an extension of MCDA models

• Unimodal factors

• Future work

• Trade-off between correctness and computation effort for models with unimodal factors

Thank You for Your Attention military expenditure

Birthrate military expenditure