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Value-at-Risk for Country Risk Ratings

The country risk literature argues that country risk ratings have a direct impact on the cost of borrowings as they reflect the probability of debt default by a country. An improvement in country risk ratings, or country creditworthiness, will lower a country’s cost of borrowing and debt servicing obligations, and vice-versa. In this context, it is useful to analyse country risk ratings data, much like financial data, in terms of the time series patterns, as such an analysis would provide policy makers and the industry stakeholders with a more accurate method of forecasting future changes in the risks and returns of country risk ratings. This paper considered an extension of the Value-at-Risk (VaR) framework where both the upper and lower thresholds are considered. The purpose of the paper was to forecast the conditional variance and Country Risk Bounds (CRBs) for the rate of change of risk ratings for ten countries. The conditional variance of composite risk returns for the ten countries were forecasted using the Single Index (SI) and Portfolio Methods (PM) of McAleer and da Veiga [10,11]. The results suggested that the country risk ratings of Switzerland, Japan and Australia are much mode likely to remain close to current levels than the country risk ratings of Argentina, Brazil and Mexico. This type of analysis would be useful to lenders/investors evaluating the attractiveness of lending/investing in alternative countries.

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File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1029.pdf
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Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 10/29.

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Length: 22 pages
Date of creation: 01 May 2010
Date of revision:
Handle: RePEc:cbt:econwp:10/29
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  1. Michael Mcaleer & Bernardo da Veiga, 2008. "Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 1-19.
  2. Suhejla Hoti & Michael McAleer, 2004. "An Empirical Assessment of Country Risk Ratings and Associated Models," Journal of Economic Surveys, Wiley Blackwell, vol. 18(4), pages 539-588, 09.
  3. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
  4. Michael McAleer & Bernardo da Veiga, 2008. "Single-index and portfolio models for forecasting value-at-risk thresholds," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 217-235.
  5. Ashok Vir Bhatia, 2002. "Sovereign Credit Ratings Methodology; An Evaluation," IMF Working Papers 02/170, International Monetary Fund.
  6. Hoti, Suhejla, 2005. "Modelling country spillover effects in country risk ratings," Emerging Markets Review, Elsevier, vol. 6(4), pages 324-345, December.
  7. C. H. Furfine & Jeffery D. Amato, 2003. "Are credit ratings procyclical?," BIS Working Papers 129, Bank for International Settlements.
  8. Hoti, Suhejla, 2005. "Comparative analysis of risk ratings for the East European region," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(5), pages 449-462.
  9. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
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