Can Equity Volatility Explain the Global Loan Pricing Puzzle?

We examine whether equity volatility can explain the difference in syndicated corporate loan spreads paid by U.S. and European borrowers first documented by Carey and Nini (2007). We argue that OLS estimates of the association between equity volatility and loan spreads are biased and inconsistent. We suggest instrumental variables that potentially identify consistent estimates. Our instrumental variable results indicate that there is no statistically significant difference in loan spreads paid by U.S. and European borrowers, and that OLS estimates of the association between idiosyncratic equity volatility and corporate loan spreads are biased downward by about a factor of 5

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

<p>Abstract</p> <p>Background</p> <p>In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called <it>biclustering</it>. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed.</p> <p>Methods</p> <p>We introduce <it>BiMine</it>, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, <it>BiMine </it>relies on a new evaluation function called <it>Average Spearman's rho </it>(ASR). Second, <it>BiMine </it>uses a new tree structure, called <it>Bicluster Enumeration Tree </it>(BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, <it>BiMine </it>introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters.</p> <p>Results</p> <p>The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that <it>BiMine </it>competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users.</p

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research

EFSA Panel on Biological Hazards (BIOHAZ) Panel; Scientific Opinion on the risk posed by pathogens in food of non-animal origin. Part 1 (outbreak data analysis and risk ranking of food/pathogen combinations)

Food of non-animal origin (FoNAO) is consumed in a variety of forms, and a major component of almost all meals. These food types have the potential to be associated with large outbreaks as seen in 2011 associated with VTEC O104. A comparison of the incidence of human cases linked to consumption of FoNAO and of food of animal origin (FoAO) was carried out to provide an indication of the proportionality between these two groups of foods. It was concluded that outbreak data reported as part of EU Zoonoses Monitoring is currently the only option for EU-wide comparative estimates. Using this data from 2007 to 2011, FoNAO were associated with 10% of the outbreaks, 26% of the cases, 35% of the hospitalisations and 46% of the deaths. If the data from the 2011VTEC O104 outbreak is excluded, FoNAO was associated with 10% of the outbreaks, 18% of cases, but only 8% of the hospitalisations and 5% of the deaths. From 2008 to 2011 there was an increase in the numbers of reported outbreaks, cases, hospitalisations and deaths associated with food of non-animal origin. In order to identify and rank specific food/pathogen combinations most often linked to human cases originating from FoNAO in the EU, a model was developed using seven criteria: strength of associations between food and pathogen based on the foodborne outbreak data from EU Zoonoses Monitoring (2007-11), incidence of illness, burden of disease, dose-response relationship, consumption, prevalence of contamination and pathogen growth potential during shelf life. Shortcomings in the approach using outbreak data were discussed. The top ranking food/pathogen combination was Salmonellaspp. and leafy greens eaten raw followed by (in equal rank) Salmonellaspp. and bulb and stem vegetables, Salmonellaspp. and tomatoes, Salmonellaspp. and melons, and pathogenic Escherichia coli and fresh pods, legumes or grain

Can Equity Volatility Explain the Global Loan Pricing Puzzle?

This paper examines whether unobservable differences in firm volatility are responsible for the global loan pricing puzzle, which is the observation that corporate loan interest rates appear to be lower in Europe than in the United States. We analyze whether equity volatility, an error prone measure of firm volatility, can explain this difference in loan spreads. We show that using equity volatility in OLS regressions will result in biased and inconsistent estimates of the difference in U.S. and European loan spreads. We use instrumental variable methods to identify consistent estimates and find no difference in U.S. and European loan spreads