The pricing of correlated default risk: evidence from the credit derivatives market

In order to analyze the pricing of portfolio credit risk – as revealed by tranche spreads of a popular credit default swap (CDS) index – we extract risk-neutral probabilities of default (PDs) and physical asset return correlations from single-name CDS spreads. The time profile and overall level of index spreads validate our PD measures. At the same time, the physical asset return correlations are too low to account for the spreads of index tranches and, thus, point to a large correlation risk premium. This premium, which covaries negatively with current realized correlations and positively with future realized correlations, sheds light on market perceptions of and attitude towards correlation risk. -- Das Portfoliokreditrisiko setzt sich aus drei Hauptkomponenten zusammen: der Ausfallwahrscheinlichkeit (probability of default, PD), der Verlustquote (loss given default, LGD) und der Wahrscheinlichkeitsverteilung für gemeinsame Ausfälle. Mit der rasanten Entwicklung innovativer Produkte im Bereich der strukturierten Finanzierung ist die Bedeutung der dritten Komponente zusehends gestiegen. Allerdings herrscht keine Einigkeit darüber, wie die Marktteilnehmer diese schätzen. Im vorliegenden Arbeitspapier schlagen wir zunächst einen auf CDSMarktdaten beruhenden Ansatz zur Ableitung der Wahrscheinlichkeitsverteilung für gemeinsame Ausfälle vor. Mit diesem Ansatz werden risikoneutrale PDs und physische Asset-Return-Korrelationen aus der Höhe der Preise und dem Gleichlauf (Co-movement) von Single-name-CDS-Spreads abgeleitet. Anschließend benutzen wir diese Schätzungen in einer konkreten Anwendung unseres Ansatzes zur Berechnung von Prognosen für Tranchenspreads eines bekannten CDS-Index (Dow Jones CDX North America Investment Grade Index) und vergleichen diese mit empirischen Spreads am CDS-Indexmarkt.Portfolio credit risk,Correlation risk premium,CDS index,Tranche spread,Copula

Optimal Bank Runs without Self-Fulfilling Prophecies

This paper extends the standard Diamond-Dybvig model for a general equilibrium in which depositors make their withdrawal decisions sequentially and banks strategically choose their contracts. There is a unique Subgame Perfect Nash Equilibrium (SPNE) in the decentralized economy. Bank runs can occur when depositors perceive a low return on bank assets. When information is imperfect, bank runs can happen even when the economy is in a good state. A representative bank can earn positive profits in equilibrium due to the sequential service constraint. When there are several risky projects available, the high-risk technology may be chosen as a socially efficient solution.

An Object Model for Collaborative Systems and a Toolkit to Support Collaborative Activities

The goal of a collaborative system is to provide a platform for group discussion so that the ideas of the majority can be captured and categorized. Such a platform would incorporate functionality to allow a group of experts to thoroughly explore and analyze a problem domain by following a discourse structure they could design, maintain and evolve as the knowledge structure for that particular domain. However, there are very few practical tools in current systems to support coordination strategy such as voting and scaling, and collaborative model building for learning. Any practical tool is better than an excellent theory. Our recent work is to design and implement a toolkit for collaboration. This toolkit supports the general tools for collaborative activities, and is easily accessed on the Web. In this paper, we first illustrate the object model for collaborative systems; then, we discuss the basic requirements for collaborative systems that should be supported in the toolkit. The key problems of collaborative systems are also analyzed. Our proposal solution is to provide a collaborative toolkit. At last, we give the descriptions of this toolkit

Nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter and Interfering Extended Target Tracking

Extended target tracking estimates the centroid and shape of the target in space and time. In various situations where extended target tracking is applicable, the presence of multiple targets can lead to interference, particularly when they maneuver behind one another in a sensor like a camera. Nonetheless, when dealing with multiple extended targets, there's a tendency for them to share similar shapes within a group, which can enhance their detectability. For instance, the coordinated movement of a cluster of aerial vehicles might cause radar misdetections during their convergence or divergence. Similarly, in the context of a self-driving car, lane markings might split or converge, resulting in inaccurate lane tracking detections. A well-known joint probabilistic data association coupled (JPDAC) filter can address this problem in only a single-point target tracking. A variation of JPDACF was developed by introducing a nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter (ST-JPDACF) to address the problem for extended targets. Using different kernel functions, we manage the dependency of measurements in space (inside a frame) and time (between frames). Kernel functions are able to be learned using a limited number of training data. This extension can be used for tracking the shape and dynamics of nonparametric dependent extended targets in clutter when targets share measurements. The proposed algorithm was compared with other well-known supervised methods in the interfering case and achieved promising results.Comment: 12 pages, 8 figures, Journa

PRIN: a predicted rice interactome network

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in <it>Oryza sativa</it>.</p> <p>Results</p> <p>To better understand the interactions of proteins in <it>Oryza sativa</it>, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (<it>Saccharomyces cerevisiae</it>), worm (<it>Caenorhabditis elegans</it>), fruit fly (<it>Drosophila melanogaster</it>), human (<it>Homo sapiens</it>), <it>Escherichia coli </it>K12 and <it>Arabidopsis thaliana</it>. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization.</p> <p>Conclusions</p> <p>PRIN is the first well annotated protein interaction database for the important model plant <it>Oryza sativa</it>. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology.</p> <p>PRIN is available online at <url>http://bis.zju.edu.cn/prin/</url>.</p