Value-at-Risk Applications to 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. Hoti and McAleer (2005a) used various univariate and multivariate conditional volatility models to analyse the dynamics of the conditional volatility associated with country risk returns for 120 countries across eight geographical regions. This extensive analysis classified the countries according the persistence of shocks to risk returns and the correlation coefficients of the conditional shocks to risk returns. Similarly, Hoti (2005a) provided an analysis of economic, financial, political and composite risk ratings using univariate and multivariate volatility models for nine Eastern European countries. The empirical results enabled a comparative assessment of the conditional means and volatilities associated with country risk returns, defined as the rate of change in country risk ratings, across the countries. Moreover the estimated constant conditional correlation coefficients provided useful information as to whether these countries are similar in terms of shocks to the four risk returns. Hoti and McAleer (2005b) estimated and tested the constant conditional correlation asymmetric VARMA-GARCH models for four countries. The paper analysed the conditional means and volatilities of economic, financial, political and composite risk returns and evaluated the multivariate spillover effects of the four risk returns for a country. Indeed, significant multivariate spillover effects were found in the rate of change of country risk ratings (or risk returns) across economic, financial, political and composite risk returns. Moreover, Hoti (2005b) was the first attempt to model spillover effects for risk returns across different countries. The paper provided a novel analysis of four risk returns using multivariate conditional volatility models for six countries situated in the Balkan Peninsula. The empirical results showed that these models are able to capture the existence of country spillover effects in the country risk returns. The purpose of this paper is to adapt the popular Value-at-Risk (VaR) approach in forecasting the conditional variance and Country Risk Bounds (CRBs) for the rate of change of risk ratings for ten representative countries. This paper demonstrates how this approach can be used not only by the countries wishing to attract foreign investments (or borrowing money), but also by the parties considering making such investments (or loans). Empirical results suggest that the country risk ratings of Switzerland, Japan and Australia are much more 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 in evaluating the attractiveness of lending/investing in alternative countries. The plan of the paper is as follows. Section 1 describes country risk and country risk ratings. Section 2 extends the traditional VaR framework and introduces a new risk measure called Country Risk Bounds that is more useful in analysing country risk ratings. The models used are discussed in Section 3 while the data is described in Section 4. Section 5 presents the forecasting exercise and discusses the policy implications. Finally, some concluding remarks are given in Section 6