Key factors affecting the hospital performance: a qualitative study using fuzzy logic

Identifying the key factors affecting the hospital performance helps better planning for hospital high performance. The purpose of this study is to provide a combination of qualitative and quantitative methods to investigate the hospital performance. In the qualitative section of this study, factors associated with hospital performance were detected using literature review, interviews, and expert panels. The findings analyzed by one sample t test and categorized by framework analysis method. In the quantitative section of this study, both direct and indirect relationships between factors were measured by using fuzzy Decision Making Trial and Evaluation Laboratory technique to detect influencing and influenced factors. Finally the key factors affecting the hospital performance were detected.     

HAZARD PROCESSES AND MARTINGALE HAZARD PROCESSES

In this paper, we build a bridge between different reduced‐form approaches to pricing defaultable claims. In particular, we show how the well‐known formulas by Duffie, Schroder, and Skiadas and by Elliott, Jeanblanc, and Yor are related. Moreover, in the spirit of Collin Dufresne, Hugonnier, and Goldstein, we propose a simple pricing formula under an equivalent change of measure. Two processes will play a central role: the hazard process and the martingale hazard process attached to a default time. The crucial step is to understand the difference between them, which has been an open question in the literature so far. We show that pseudo‐stopping times appear as the most general class of random times for which these two processes are equal. We also show that these two processes always differ when τ is an honest time, providing an explicit expression for the difference. Eventually we provide a solution to another open problem: we show that if τ is an arbitrary random (default) time such that its Azéma's supermartingale is continuous, then τ avoids stopping times.     

A reading guide for last passage times with financial applications in view

In this survey on last passage times, we propose a new viewpoint which provides a unified approach to many different results which appear in the mathematical finance literature and in the theory of stochastic processes. In particular, we are able to improve the assumptions under which some well-known results are usually stated. Moreover we give some new and detailed calculations for the computation of the distribution of some large classes of last passage times. We have kept in this survey only the aspects of the theory which we expect potentially to be relevant for financial applications.     

Default times, no-arbitrage conditions and changes of probability measures

In this paper, we give a financial justification, based on no-arbitrage conditions, of the (H)-hypothesis in default time modeling. We also show how the (H)-hypothesis is affected by an equivalent change of probability measure. The main technique used here is the theory of progressive enlargements of filtrations.     

Debunking the myth of general consumer rejection of green genetic engineering: Empirical evidence from Germany

The emergence of a more sustainable economy in Europe was accompanied by a range of bio‐based products and technologies. As a prominent example, green genetic engineering opens up multiple options to increase agricultural production, but its public acceptance seems to vary by application area. Risk perception explains consumer acceptance of green genetic engineering, which is a necessary precondition for wider technology adoption. This study investigates risk perceptions for four major sources of risk: health related, environmental, socioeconomic and ethical. Data were collected in a laboratory experiment in Germany, with a total of 439 participants. A between‐subject design was employed. The four experimental treatment groups comprised two policy scenarios, namely one only permitting research and development and the other allowing full commercialization of genetically modified products, and two product end‐uses, bioenergy and food. The study shows significant end‐use differences in both the type of policy scenario and the risk dimension in question. In particular, health risks were generally perceived to be lower for bioenergy than food whenever full commercialization was pursued. Furthermore, full commercialization of genetically modified food prompted higher concerns about personal health, whereas use of crops for bioenergy production was broadly related to higher levels of socioeconomic risk. Finally, although the majority of consumers identified health risks as being most relevant, the consequences for the environment evoked the greatest degree of risk perception. In general, our findings lend support for the notion that the policy regime is the most important determinant for risk perception, followed by the type of risk dimension and level of trust in industry.