5 research outputs found

    Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression

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    Measuring interdependence between probabilities of default (PDs) in different industry sectors of an economy plays a crucial role in financial stress testing. Thereby, regression approaches may be employed to model the impact of stressed industry sectors as covariates on other response sectors. We identify vine copula based quantile regression as an eligible tool for conducting such stress tests as this method has good robustness properties, takes into account potential nonlinearities of conditional quantile functions and ensures that no quantile crossing effects occur. We illustrate its performance by a data set of sector specific PDs for the German economy. Empirical results are provided for a rough and a fine-grained industry sector classification scheme. Amongst others, we confirm that a stressed automobile industry has a severe impact on the German economy as a whole at different quantile levels whereas e.g., for a stressed financial sector the impact is rather moderate. Moreover, the vine copula based quantile regression approach is benchmarked against both classical linear quantile regression and expectile regression in order to illustrate its methodological effectiveness in the scenarios evaluated.Comment: 12 page

    Idiopathic pulmonary arterial hypertension phenotypes determined by cluster analysis from the COMPERA registry

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    Funding Information: Marius M. Hoeper has received fees for lectures and/or consultations from Acceleron, Actelion, Bayer, MSD, and Pfizer. Nicola Benjamin has received fees for lectures and/or consultations from Actelion. Ekkehard Gr√ľnig has received fees for lectures and/or consultations from Actelion, Bayer, GSK, MSD, United Therapeutics, and Pfizer. Karen M. Olsson has received fees for lectures and/or consultations from Actelion, Bayer, United Therapeutics, GSK, and Pfizer. C. Dario Vizza has received fees from Actelion, Bayer, GSK, MSD, Pfizer, and United Therapeutics Europe. Anton Vonk-Noordegraaf has received fees for lectures and/or consultation from Actelion, Bayer, GSK, and MSD. Oliver Distler has/had a consultancy relationship with and/or has received research funding from 4-D Science, Actelion, Active Biotec, Bayer, Biogen Idec, Boehringer Ingelheim Pharma, BMS, ChemoAb, EpiPharm, Ergonex, espeRare foundation, GSK, Genentech/Roche, Inventiva, Lilly, medac, MedImmune, Mitsubishi Tanabe, Pharmacyclics, Pfizer, Sanofi, Serodapharm, and Sinoxa in the area of potential treatments of scleroderma and its complications including pulmonary arterial hypertension. In addition, Prof Distler has a patent for mir-29 for the treatment of systemic sclerosis licensed. Christian Opitz has received fees from Actelion, Bayer, GSK, Pfizer, and Novartis. J. Simon R. Gibbs has received fees for lectures and/or consultations from Actelion, Bayer, Bellerophon, GSK, MSD, and Pfizer. Marion Delcroix has received fees from Actelion, Bayer, GSK, and MSD. H. Ardeschir Ghofrani has received fees from Actelion, Bayer, Gilead, GSK, MSD, Pfizer, and United Therapeutics. Doerte Huscher has received fees for lectures and consultations from Actelion. David Pittrow has received fees for consultations from Actelion, Biogen, Aspen, Bayer, Boehringer Ingelheim, Daiichi Sankyo, and Sanofi. Stephan Rosenkranz has received fees for lectures and/or consultations from Actelion, Bayer, GSK, Pfizer, Novartis, Gilead, MSD, and United Therapeutics. Martin Claussen reports honoraria for lectures from Boehringer Ingelheim Pharma GmbH and Roche Pharma and for serving on advisory boards from Boehringer Ingelheim, outside the submitted work. Heinrike Wilkens reports personal fees from Boehringer and Roche during the conduct of the study and personal fees from Bayer, Biotest, Actelion, GSK, and Pfizer outside the submitted work. Juergen Behr received grants from Boehringer Ingelheim and personal fees for consultation or lectures from Actelion, Bayer, Boehringer Ingelheim, and Roche. Hubert Wirtz reports personal fees from Boehringer Ingelheim and Roche outside the submitted work. Hening Gall reports personal fees from Actelion, AstraZeneca, Bayer, BMS, GSK, Janssen-Cilag, Lilly, MSD, Novartis, OMT, Pfizer, and United Therapeutics outside the submitted work. Elena Pfeuffer-Jovic reports personal fees from Actelion, Boehringer Ingelheim, Novartis, and OMT outside the submitted work. Laura Scelsi reports personal fees from Actelion, Bayer, and MSD outside the submitted work. Siliva Ulrich reports grants from Swiss National Science Foundation, Zurich Lung, Swiss Lung, and Orpha Swiss, and grants and personal fees from Actelion SA/Johnson & Johnson Switzerland and MSD Switzerland outside the submitted work. The remaining authors have no conflicts of interest to disclose. Funding Information: This work was supported by the German Centre of Lung Research (DZL). COMPERA is funded by unrestricted grants from Acceleron , Actelion Pharmaceuticals , Bayer , OMT , and GSK . These companies were not involved in data analysis or the writing of this manuscript. Publisher Copyright: ¬© 2020 The Authors Copyright: Copyright 2020 Elsevier B.V., All rights reserved.The term idiopathic pulmonary arterial hypertension (IPAH) is used to categorize patients with pre-capillary pulmonary hypertension of unknown origin. There is considerable variability in the clinical presentation of these patients. Using data from the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension, we performed a cluster analysis of 841 patients with IPAH based on age, sex, diffusion capacity of the lung for carbon monoxide (DLCO; <45% vs ‚Č•45% predicted), smoking status, and presence of comorbidities (obesity, hypertension, coronary heart disease, and diabetes mellitus). A hierarchical agglomerative clustering algorithm was performed using Ward's minimum variance method. The clusters were analyzed in terms of baseline characteristics; survival; and response to pulmonary arterial hypertension (PAH) therapy, expressed as changes from baseline to follow-up in functional class, 6-minute walking distance, cardiac biomarkers, and risk. Three clusters were identified: Cluster 1 (n = 106; 12.6%): median age 45 years, 76% females, no comorbidities, mostly never smokers, DLCO ‚Č•45%; Cluster 2 (n = 301; 35.8%): median age 75 years, 98% females, frequent comorbidities, no smoking history, DLCO mostly ‚Č•45%; and Cluster 3 (n = 434; 51.6%): median age 72 years, 72% males, frequent comorbidities, history of smoking, and low DLCO. Patients in Cluster 1 had a better response to PAH treatment than patients in the 2 other clusters. Survival over 5 years was 84.6% in Cluster 1, 59.2% in Cluster 2, and 42.2% in Cluster 3 (unadjusted p < 0.001 for comparison between all groups). The population of patients diagnosed with IPAH is heterogenous. This cluster analysis identified distinct phenotypes, which differed in clinical presentation, response to therapy, and survival.publishersversionPeer reviewe

    A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

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    In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler&#8217;s allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that&#8212;in principle&#8212;all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures

    Parameter estimation, bias correction and uncertainty quantification in the Vasicek credit portfolio model

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    This paper is devoted to the parameterization of correlations in the Vasicek credit portfolio model. First, we analytically approximate standard errors for value-at-risk and expected shortfall based on the standard errors of intra-cohort correlations. Second, we introduce a novel copula-based maximum likelihood estimator for inter-cohort correlations and derive an analytical expression of the standard errors. Our new approach enhances current methods in terms of both computing time and, most importantly, direct uncertainty quantification. Both contributions can be used to quantify a margin of conservatism, which is required by regulators. Third, we illustrate powerful procedures that reduce the well-known bias of current estimators, showing their favorable properties. Further, an open-source implementation of all estimators in the novel R package AssetCorr is provided and selected estimators are applied to Moody’s Default & Recovery Database