scholarly journals Worst-Case Expected Shortfall with Univariate and Bivariate Marginals

2020 ◽  
Author(s):  
Anulekha Dhara ◽  
Bikramjit Das ◽  
Karthik Natarajan
Keyword(s):  
Risk Management ◽  
Portfolio Optimization ◽  
Polynomial Time ◽  
Expected Shortfall ◽  
Tree Structure ◽  
Discrete Distributions ◽  
Worst Case ◽  
Vine Copula ◽  
Multiple Experts ◽  
Vine Copulas

Computing and minimizing the worst-case bound on the expected shortfall risk of a portfolio given partial information on the distribution of the asset returns is an important problem in risk management. One such bound that been proposed is for the worst-case distribution that is “close” to a reference distribution where closeness in distance among distributions is measured using [Formula: see text]-divergence. In this paper, we advocate the use of such ambiguity sets with a tree structure on the univariate and bivariate marginal distributions. Such an approach has attractive modeling and computational properties. From a modeling perspective, this provides flexibility for risk management applications where there are many more choices for bivariate copulas in comparison with multivariate copulas. Bivariate copulas form the basis of the nested tree structure that is found in vine copulas. Because estimating a vine copula is fairly challenging, our approach provides robust bounds that are valid for the tree structure that is obtained by truncating the vine copula at the top level. The model also provides flexibility in tackling instances when the lower dimensional marginal information is inconsistent that might arise when multiple experts provide information. From a computational perspective, under the assumption of a tree structure on the bivariate marginals, we show that the worst-case expected shortfall is computable in polynomial time in the input size when the distributions are discrete. The corresponding distributionally robust portfolio optimization problem is also solvable in polynomial time. In contrast, under the assumption of independence, the expected shortfall is shown to be #P-hard to compute for discrete distributions. We provide numerical examples with simulated and real data to illustrate the quality of the worst-case bounds in risk management and portfolio optimization and compare it with alternate probabilistic models such as vine copulas and Markov tree distributions.

On Worst-Case Portfolio Optimization

2007 ◽  
Vol 46 (6) ◽  
pp. 2013-2030 ◽  
Author(s):  
Ralf Korn ◽  
Mogens Steffensen
Keyword(s):  
Portfolio Optimization ◽  
Worst Case

scholarly journals The Rising Frequency of IT Blackouts Indicates the Increasing Relevance of IT Emergency Concepts to Ensure Patient Safety

2016 ◽  
Vol 25 (01) ◽  
pp. 130-137 ◽  
Author(s):  
U. Sax ◽  
M. Lipprandt ◽  
R. Röhrig
Keyword(s):  
Risk Management ◽  
Worst Case ◽  
Counter Measures ◽  
It Systems ◽  
Healthcare It ◽  
Ensure Patient Safety ◽  
Total Data ◽  
It Support ◽  
Management Approaches ◽  
Clinical Systems

Summary Introduction: As many medical workflows depend vastly on IT support, great demands are placed on the availability and accuracy of the applications involved. The cases of IT failure through ransomware at the beginning of 2016 are impressive examples of the dependence of clinical processes on IT. Although IT risk management attempts to reduce the risk of IT blackouts, the probability of partial/total data loss, or even worse, data falsification, is not zero. The objective of this paper is to present the state of the art with respect to strategies, processes, and governance to deal with the failure of IT systems. Methods: This article is conducted as a narrative review. Results: Worst case scenarios are needed, dealing with methods as to how to survive the downtime of clinical systems, for example through alternative workflows. These workflows have to be trained regularly. We categorize the most important types of IT system failure, assess the usefulness of classic counter measures, and state that most risk management approaches fall short on exactly this matter. Conclusion: To ensure that continuous, evidence-based improvements to the recommendations for IT emergency concepts are made, it is essential that IT blackouts and IT disasters are reported, analyzed, and critically discussed. This requires changing from a culture of shame and blame to one of error and safety in healthcare IT. This change is finding its way into other disciplines in medicine. In addition, systematically planned and analyzed simulations of IT disaster may assist in IT emergency concept development.

scholarly journals Streamflow estimation at partially gaged sites using multiple dependence conditions via vine copulas

2020 ◽  
Author(s):  
Kuk-Hyun Ahn
Keyword(s):  
Missing Values ◽  
The Other ◽  
Dependence Structure ◽  
Proposed Model ◽  
Pee Dee ◽  
Streamflow Estimation ◽  
Vine Copula ◽  
Bivariate Copula ◽  
Pee Dee River ◽  
Vine Copulas

Abstract. Reliable estimates of missing streamflow values are relevant for water resources planning and management. This study proposes a multiple dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin-Pee Dee River Basin, the eastern U. S. Both results inform that the proposed model is better suited for infilling missing values. After that, the performance of the vine copula is compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

Risk management strategies via minimax portfolio optimization

2010 ◽  
Vol 207 (1) ◽  
pp. 409-419 ◽  
Author(s):  
George G. Polak ◽  
David F. Rogers ◽  
Dennis J. Sweeney
Keyword(s):  
Risk Management ◽  
Portfolio Optimization ◽  
Management Strategies ◽  
Risk Management Strategies

scholarly journals Assessing the vulnerability of buildings to tsunami in Sydney

2009 ◽  
Vol 9 (6) ◽  
pp. 2015-2026 ◽  
Author(s):  
F. Dall'Osso ◽  
M. Gonella ◽  
G. Gabbianelli ◽  
G. Withycombe ◽  
D. Dominey-Howes
Keyword(s):  
Risk Management ◽  
Local Government ◽  
Management Strategies ◽  
Significant Proportion ◽  
Building Design ◽  
Building Vulnerability ◽  
Worst Case ◽  
Risk Management Strategies ◽  
Vulnerability Of Buildings ◽  
Relative Vulnerability

Abstract. Australia is vulnerable to the impacts of tsunamis and exposure along the SE coast of New South Wales is especially high. Significantly, this is the same area reported to have been affected by repeated large magnitude tsunamis during the Holocene. Efforts are under way to complete probabilistic risk assessments for the region but local government planners and emergency risk managers need information now about building vulnerability in order to develop appropriate risk management strategies. We use the newly revised PTVA-3 Model (Dall'Osso et al., 2009) to assess the relative vulnerability of buildings to damage from a "worst case tsunami" defined by our latest understanding of regional risk – something never before undertaken in Australia. We present selected results from an investigation of building vulnerability within the local government area of Manly – an iconic coastal area of Sydney. We show that a significant proportion of buildings (in particular, residential structures) are classified as having "High" and "Very High" Relative Vulnerability Index scores. Furthermore, other important buildings (e.g., schools, nursing homes and transport structures) are also vulnerable to damage. Our results have serious implications for immediate emergency risk management, longer-term land-use zoning and development, and building design and construction standards. Based on the work undertaken here, we recommend further detailed assessment of the vulnerability of coastal buildings in at risk areas, development of appropriate risk management strategies and a detailed program of community engagement to increase overall resilience.

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