Accident settlement for avian influenza in travel health insurance

Among those infectious diseases that lead to insurance settlement for damages, we normally list those cases that do not involve person-to-person transmission, but rather those cases wherein the harmful agent enters the body without the intention of the person harmed, and which causes acute infectious disease or food poisoning. This is the way in which, for example, tick-borne encephalitis following an insect bite, rabies caused by animal bites, and acute food poisoning contracted in restaurants or from public catering arise. We can also list among the latter those cases of avian influenza illness, which arise through the involuntary consumption of infected animal meat (not ignoring occupational health and safety regulations). These insurance cases arise particularly when travel to countries with a high level of infection of livestock with avian influenza is involved. In these cases of illness, there is justification for including an accident indemnity payment--in addition to covering the costs related to health care--within the remit of travel health insurance.     

Strong random correlations in networks of heterogeneous agents

Correlations and other collective phenomena are considered in a schematic model of pairwise interacting, competing and collaborating agents facing a binary choice and situated at the nodes of the complete graph and a 2-dimensional regular lattice, respectively. The agents may be subjected to an idiosyncratic or common external influence and also some random noise. The system’s stochastic dynamics is studied by numerical simulations. It displays the characteristics of punctuated, multiple equilibria, sensitivity to small details, and path dependence. The dynamics is so slow that one can meaningfully speak of quasi-equilibrium states. Performing measurements of correlations between the agents choices we find that they are random both as to their sign and absolute value, but their distribution is very broad when the interaction dominates both the noise and the external field. This means that random but strong correlations appear with large probability. In the two dimensional model this also implies that the correlations on average fall off with distance very slowly: the system is essentially non-local, small changes at one end may have a strong impact at the other. The strong, random correlations tend to organize a large fraction of the agents into strongly correlated clusters that act together. If we think of this model as a metaphor of social or economic agents or bank networks, the systemic risk implications of this tendency are clear: any impact on even a single strongly correlated agent will not be confined to a small set but will spread, in an unforeseeable manner, to the whole system via the strong random correlations.     

Revealed Comparative Advantage and Competitiveness in Hungarian Agri–Food Sectors

We examine the competitiveness of Hungarian agriculture and food processing, in relation to that of the EU, based on four indices of revealed comparative advantage, using highly disaggregate data for the period 1992 to 1998. Consistency tests suggest that the indices are less satisfactory as cardinal measures, but are useful in identifying the demarcation between comparative advantage and comparative disadvantage. Hungary is shown to have a comparative advantage in a range of agri‐food products, including animals and meat. This complements the findings of those studies that have used price and cost based approaches in identifying competitiveness in cereals and crops. Results indicate that the RCA indices, when interpreted as a binary measure, have remained surprisingly stable during the period of transition, although there is evidence of a weakening in the level of comparative advantage as revealed in the Balassa index.     

Determinants of agro-food trade competition of Central European countries with the European Union

The two-way trade flows are classified into four competition categories: successful price competition when trade surplus is at lower export than import price, unsuccessful price competition when trade deficit is at higher export than import price, successful quality competition when trade surplus is at higher export than import price, and unsuccessful quality competition when trade deficit is at lower export than import price. Using a panel dataset of determinants of agro-food trade competition between the five Central European Countries (Czech Republic, Hungary, Poland, Slovakia, and Slovenia) and the European Union, we found the effect of trade balance on trade competition to be more significant than the effect of export–import unit values. Natural, and to a lesser extent human factor endowments increase price and quality competition and reduce unsuccessful price and quality competition. R&D expenditures improve quality competition and reduce price competition. Foreign direct investment reduces unsuccessful price competition and increases unsuccessful quality competition. The size of the economy improves price competition and reduces quality competition. Consumer demands associated with higher level of income per capita increase unsuccessful price and quality competition.     

Vertically Differentiated Trade and Differences in Factor Endowment: The Case of Agri‐Food Products between Hungary and the EU

The paper investigates the relationship between factor endowment and vertical intraindustry trade (IIT)in agri‐food products between Hungary and the EU. Intra‐industry trade is separated into its horizontal and vertical components on the basis of differences in unit values. Three different approaches to measuring IIT are employed and these are then tested using panel regression models. Results show vertical type trade is predominant in total IIT for agri‐food products. In order to achieve more general results, we consider different types of productive factors: land, human and physical capital. Using Flam and Helpman type vertically differentiated trade models, we find a positive relationship between factor endowment and vertical IIT. More importantly, using a measure of IIT that reflects the level of trade produces better regression results than those based on the degree or share of IIT.     

Testing Beta Stationarity across Bond Rating Changes

In this paper we examine the relationship between bond re-ratings and changes in systematic risk. Using both time series and cross-sectional regressions, we find that upgrades are not associated with a change in beta. Across the entire sample, downgrades are associated with an increase in beta. Further, the increase in beta is positively correlated with firm size. There is no evidence that movement within or across rating categories, the number of grades changed, or a change across the investment grade category have a differential impact on the change in beta.     

Statistical inference in multiperiod event studies

We examine several event-study test statistics that can be used to detect abnormal performance during amultiperiod event window. We demonstrate that one of the most commonly used test statistics does not, under the assumptions made, have the distribution claimed (standard normal), and thus tests using it will be biased. The magnitude of that bias is shown to increase with the length of the event window and can generally be expected to lead to excessive rejection of the null hypothesis. We also compare the relative power of alternative test statistics that are normally distributed and are straightforward to apply.     

Portfolio optimization under Expected Shortfall: contour maps of estimation error

The contour maps of the error of historical and parametric estimates of the global minimum risk for large random portfolios optimized under the Expected Shortfall (ES) risk measure are constructed. Similar maps for the VaR of the ES-optimized portfolio are also presented, along with results for the distribution of portfolio weights over the random samples and for the out-of-sample and in-sample estimates for ES. The contour maps allow one to quantitatively determine the sample size (the length of the time series) required by the optimization for a given number of different assets in the portfolio, at a given confidence level and a given level of relative estimation error. The necessary sample sizes invariably turn out to be unrealistically large for any reasonable choice of the number of assets and the confidence level. These results are obtained via analytical calculations based on methods borrowed from the statistical physics of random systems, supported by numerical simulations.