Suchergebnisse

PurposeThe article develops a model to interpret the 2010–2018 financial crisis in Greece, Portugal, Ireland, Spain and Cyprus, and the loan programs from the IL (International Lender; i.e. the European Union, the European Commission, and the International Monetary Fund).Design/methodology/approachFor each country, an isoelastic utility with constant relative risk aversion is assumed. For the IL a Cobb Douglas utility is assumed with consumption, the GDP (Gross Domestic Product) to debt ratio, and market stability as inputs, accounting for time discounting. This article applies two methods to assess the empirics. The first method considers the IL's strategy as a whole over the 2010–2018 period. The second method assumes that the IL maximizes its utility in one period to determine its optimal loan, accounting for the empirics in that period, and the debt in the previous period.FindingsFor the first method, when the output elasticity in the IL's Cobb Douglas utility is high favoring consumption, the IL prefers offering a higher loan than its actual loan. Otherwise the IL prefers to offer no loan. The output elasticity at which the IL prefers to offer a loan is lowest for Greece, second lowest for Cyprus, third lowest for Portugal, and highest for Ireland and Spain. A high loan to Greece over a larger range of the output elasticity for Greece's consumption is supported by Greece being prioritized through the loan program. For the second method, the IL prefers to offer no loan to Greece which is too burdened with debt. Thus, the first method seems preferable, considering the entire duration of the crisis holistically.Originality/valueThe article offers a novel perspective of how to assess debt crises, enabling the IL to weigh various factors such as consumption, GDP, loan offered, and each country's debt to credit markets.

AbstractWith some of the largest cyber attacks occurring in recent years—from 2010 to 2019—we are only beginning to understand the full extent of cyber risk. As businesses grapple with the risks of cyber‐incidents and their imperfect ability to prevent them, attention has shifted toward risk management and insurance. While there have been efforts to understand the costs of cyber attacks, the systemic risk—a result of risks spreading across interdependent systems—associated with cyber attacks remains a critical and problem in need of further study. We contribute a theoretical framework that describes systemic cyber risk as the result of cascading, common cause, or independent failures following a cyber incident. We construct a quantitative model of cascading failures to estimate the potential economic damage associated with a given cyber incident. We present an interdisciplinary approach for extending standard sector‐level input–output analyses to the cyber domain, which has not been done. We estimate the aggregate losses associated with firm‐level incidents, a contribution to risk analysis and computational economic modeling. We use this model to estimate the impact of potential cyber incidents and compare model results to a case with known damages. Finally, we use the model of systemic cyber failure to consider the implications on the growing cyber insurance market and the need for broader cyber policy. While we discuss the topic of systemic cyber risk, our contribution of using I/O analysis to estimate the aggregate losses from firm‐level incidents is applicable across a variety of risk analysis applications from environment to health.

We use data on food import violations from the FDA Operational and Administrative System for Import Support (OASIS) to address rising concerns associated with imported food, quantify import risks by product and by country of origin, and explore the usefulness of OASIS data for risk assessment. In particular, we assess whether there are significant trends in violations, whether import violations can be used to quantify risks by country and by product, and how import risks depend on economic factors of the country of origin. The results show that normalizing import violations by volume of imports provides a meaningful indicator of risk. We then use regression analysis to characterize import risks. Using this model, we analyze import risks by product type, violation type, and economic factors of the country of origin. We find that OASIS data are useful in quantifying food import risks, and that the rate of refusals provides a useful decision tool for risk management. Furthermore, we find that some economic factors are significant indicators of food import risk by country.