"Structure and Asymptotic Theory for Multivariate Asymmetric Volatility: Empirical Evidence for Country Risk Ratings"

Following the rapid growth in the international debt of less developed countries in the 1970s and the increasing incidence of debt rescheduling in the early 1980s, country risk has become a topic of major concern for the international financial community. A critical assessment of country risk is essential because it reflects the ability and willingness of a country to service its financial obligations. Various risk rating agencies employ different methods to determine country risk ratings, combining a range of qualitative and quantitative information regarding alternative measures of economic, financial and political risk into associated composite risk ratings. This paper provides an international comparison of country risk ratings compiled by the International Country Risk Guide (ICRG), which is the only international rating agency to provide detailed and consistent monthly data over an extended period for a large number of countries. As risk ratings can be treated as indexes, their rate of change, or returns, merits attention in the same manner as financial returns. For this reason, a constant correlation multivariate asymmetric ARMA-GARCH model is presented and its underlying structure is established, including the unique, strictly stationary and ergodic solution of the model, its causal expansion, and convenient sufficient conditions for the existence of moments. Alternative empirically verifiable sufficient conditions for the consistency and asymptotic normality of the quasi-maximum likelihood estimator are established under non-normality of the conditional (or standardized) shocks. The empirical results provide a comparative assessment of the conditional means and volatilities associated with international country risk returns across countries and over time, enable a validation of the regularity conditions underlying the models, highlight the importance of economic, financial and political risk ratings as components of a composite risk rating, evaluate the multivariate effects of alternative risk returns and different countries, and evaluate the usefulness of the ICRG risk ratings in modelling risk returns.

Modelling Environmental Risk

As environmental issues have become increasingly important in economic research and policy for sustainable development, firms in the private sector have introduced environmental and social issues in conducting their business activities. Such behaviour is tracked by the Dow Jones Sustainable Indexes (DJSI) through financial market indexes that are derived from the Dow Jones Global Indexes. The sustainability activities of firms are assessed using criteria in three areas, namely economic, environmental and social. Risk (or uncertainty) is analysed empirically through the use of conditional volatility models of investment in sustainability-driven firms that are selected through the DJSI. The empirical analysis is based on financial econometric models to determine the underlying conditional volatility, with the estimates showing that there is strong evidence of volatility clustering, short and long run persistence of shocks to the index returns, and asymmetric leverage between positive and negative shocks to returns.Environmental sustainability index, environmental risk, conditional volatility, Dow Jones Sustainability Indexes, GARCH, GJR, persistence, shocks, asymmetry, moment condition, log-moment condition.

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.Country risk; risk ratings; value-at-risk; risk bounds; risk management

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 provides policy makers and industry stakeholders with a more accurate method of forecasting future changes in the risks and returns associated with country risk ratings.

"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 provides policy makers and industry stakeholders with a more accurate method of forecasting future changes in the risks and returns associated with country risk ratings.

1 Modelling Environmental Risk

As environmental issues have become increasingly important in economic research and policy for sustainable development, firms in the private sector have introduced environmental and social issues in conducting their business activities. Such behaviour is tracked by the Dow Jones Sustainable Indexes (DJSI) through financial market indexes that are derived from the Dow Jones Global Indexes. The sustainability activities of firms are assessed using criteria in three areas, namely economic, environmental and social. Risk (or uncertainty) is analysed empirically through the use of conditional volatility models of investment in sustainability-driven firms that are selected through the DJSI. The empirical analysis is based on financial econometric models to determine the underlying conditional volatility, with the estimates showing that there is strong evidence of volatility clustering, short and long run persistence of shocks to the index returns, and asymmetric leverage between positive and negative shocks to returns

Structure and Asymptotic Theory for Multivariate Asymmetric Conditional Volatility

Various univariate and multivariate models of volatility have been used to evaluate market risk, asymmetric shocks, thresholds, leverage effects, and Value-at-Risk in economics and finance. This article is concerned with market risk, and develops a constant conditional correlation vector ARMA-asymmetric GARCH (VARMA-AGARCH) model, as an extension of the widely used univariate asymmetric (or threshold) GJR model of Glosten et al. (1992), and establishes its underlying structure, including the unique, strictly stationary, and ergodic solution of the model, its causal expansion, and convenient sufficient conditions for the existence of moments. Alternative empirically verifiable sufficient conditions for the consistency and asymptotic normality of the quasi-maximum likelihood estimator are established under non-normality of the standardized shocks.Asymmetric effects, Asymptotic theory, Conditional volatility, Multivariate structure, Regularity conditions,

International Tourism Demand and Volatility Models for the Canary Islands

International tourism is an important source of service exports to Spain and its regions, particularly the Canary Islands. Tourism is the major industry in the Canary Islands, accounting for about 22% of GDP. This paper examines time series of international tourism demand to the Canary Islands collected by the National Airport Administration (AENA) at airports from information regarding the number of tourist arrivals from abroad. The data set comprises monthly figures for the Canary Islands from 14 leading tourist source countries, as well as total tourist arrivals, from 1990(1)-2003(12). Tourist arrivals and associated volatilities for the monthly tourism data are estimated for the 14 source countries, as well as total tourist arrivals, using univariate and multivariate volatility models for the 15 data series. The univariate estimates suggest that the GARCH(1,1) model provides an accurate measure of conditional volatility in international monthly tourist arrivals for the 14 leading source countries, and total monthly tourist arrivals. The estimated conditional correlation coefficients provide useful information as to whether tourist source markets are similar in terms of shocks to international tourism demand. At the multivariate level, the conditional correlations in the shocks to monthly tourist arrivals are generally positive, varying from small negative to large positive correlations