Robustness and sensitivity analysis of risk measurement procedures

By Rama Cont, Romain Deguest, Giacomo Scandolo. 2010

397 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/08b33232dd5e71519e44993a3af851bc09ed285f

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Citations in the corpus

Higher order elicitability and Osband’s principle (2015, background): “ better alternative as it is a coherent and comonotonically additive, that is, a spectral risk measure. However, with respect to robustness, some authors argue that VaRα should be preferred over ESα (Cont et al., 2010; Kou et al., 2013), whereas others argue that the classical statistical notions of”
Entropic Value-at-Risk: A New Coherent Risk Measure (2012, background): “[16] examined risk measures from a statistical point of view.”
Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall (2016, background): “It has been shown that estimators of ES generally lack robustness (Cont et al., 2010) so stable series of ES estimates are more difficult to obtain than VaR estimates. However, Emmer et al. (2015) point our that the concept of robustness, which was introduced in statistics in the context of measurem”
Robust distortion risk measures (2022, neutral): “Many risk measures are, however, highly sensitive to changes in the underlying loss distribution, and this vulnerability can contribute to adverse decisions; see Cont et al. (2010), Kou et al. (2013), Embrechts et al. (2015), and Pesenti et al. (2016).”
On Optimal Reinsurance Policy with Distortion Risk Measures and Premiums (2014, neutral): “…is equal to the Conditional Value at Risk (CVaR), which will be introduced later in this paper. the family of generalized spectral risk measures (Cont et al. (2010)), the generalized distortion measures of risk (Wang (1995)), Wang’s risk premiums (Wang (1995) and Wang et al. (1997)), Expected…”
COHERENCE AND ELICITABILITY (2013, background): “Cont, Deguest, and Scandolo (2010) show that there is a fundamental theoretical conflict between subadditivity and robustness of risk measurement procedures for spectral risk measures; see also the related discussion in Kou, Peng, and Heyde (2013, section 5).”
An extreme value approach for modeling Operational Risk losses depending on covariates (2016, neutral): “which is orthogonal to ξ in this sense; see Cox and Reid (1987) for the general theory and Chavez-Demoulin and Davison (2005) for a similar reparameterization in an EVT-POT context.”
Statistics and Quantitative Risk Management for Banking and Insurance (2014, background): “In Cont et al. (2010) the authors conclude that “. . . historical Value-at-Risk, while failing to be subadditive, leads to a more robust procedure than alternatives such as Expected Shortfall”.”