Shortfall as a risk measure: properties, optimization and applications
By Dimitris J. Bertsimas, G. Lauprête, A. Samarov. 2004
296 citations recorded by the source corpus.
Subfields: Demography, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/12ba41987d9cde1bd87daf349e5b0fd61d7cae9a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Demography
In Finance
- Shouyang Wang
- Dirk Tasche
- Duan Li
- Shushang Zhu
- David Simchi-Levi
- Philip S. Thomas
- Dimitris J. Bertsimas
- Andrew Barto
In Management Science and Operations Research
- Dimitris J. Bertsimas
- Melvyn Sim
- Emma Brunskill
- David B. Brown
- Xin Chen
- Philip S. Thomas
- Bruno Castro da Silva
- Peng Sun
Citations in the corpus
- Risk Aversion in Inventory Management (2007, background): “VaR does not preserve the property of subadditivity (see [24, 3 ]).”
- ON A CONVEX MEASURE OF DRAWDOWN RISK (2014, unknown): “Using the fact that the partial derivative with respect to the quantile DTα is zero, as discussed by Bertsimas et al. (2004), Formula 4 simplifies to:”
- Robust portfolio selection under downside risk measures (2009, background): “…measure and Conditional VaR (CVaR) which has gained much attention in recent years due to its convenience in computation and its coherent properties for measuring risk (Artzner et al. 1999, Rockafellar and Uryasev 2000, 2002, Acerbi and Tasche 2002, Testuri and Uryasev 2003, Bertsimas et al. 2004).”
- Constructing Uncertainty Sets for Robust Linear Optimization (2009, background): “This is a very clear ambiguity interpretation and is the crucial idea as we attempt to construct uncertainty sets in a robust optimization framework from a given coherent risk measure.”
- Expected shortfall and beyond (2002, methodology): “Representation (4) was given by Bertsimas et al. (2000) for continuous”
- On Ensuring that Intelligent Machines Are Well-Behaved (2017, methodology): “, using soft constraints [Boyd and Vandenberghe, 2004] and robust and risk-sensitive approaches [Bertsimas et al., 2004]) requires extensive domain knowledge or additional data analysis to properly balance the relative importance of the primary objective function and the constraints.”