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Coherent Measures of Risk
Open accessAbstract
In this paper we study both market risks and nonmarket risks, without complete markets assumption, and discuss methods of measurement of these risks. We present and justify a set of four desirable properties for measures of risk, and call the measures satisfying these properties “coherent.” We examine the measures of risk provided and the related actions required by SPAN, by the SEC/NASD rules, and by quantile‐based methods. We demonstrate the universality of scenario‐based methods for providing coherent measures. We offer suggestions concerning the SEC method. We also suggest a method to repair the failure of subadditivity of quantile‐based methods.
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Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997) · doi:10.1007/978-3-642-33483-2 · Cited by 4,402
Cited by
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Quantile cointegrating regression
Xiao, Z. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.12.005 · Cited by 431
AN OLD‐NEW CONCEPT OF CONVEX RISK MEASURES: THE OPTIMIZED CERTAINTY EQUIVALENT
Ben‐Tal, A., & Teboulle, M. (2007) · Mathematical Finance · doi:10.1111/j.1467-9965.2007.00311.x · Cited by 353
DRAWDOWN MEASURE IN PORTFOLIO OPTIMIZATION
CHEKHLOV, A., URYASEV, S., & ZABARANKIN, M. (2005) · International Journal of Theoretical and Applied Finance · doi:10.1142/s0219024905002767 · Cited by 350
Robust and Data-Driven Optimization: Modern Decision Making Under Uncertainty
Bertsimas, D., & Thiele, A. (2006) · Models, Methods, and Applications for Innovative Decision Making · doi:10.1287/educ.1063.0022 · Cited by 280
Metadata
- Authors
- Artzner, P., Delbaen, F., Eber, J., & Heath, D.
- Venue
- Mathematical Finance
- Publisher
- Wiley
- Published
- 1999-07
- Volume
- 9
- Issue
- 3
- Pages
- 203-228
- ISSN
- 0960-1627 (ISSN-L)
- 1467-9965
- OpenAlex
- W2019291268
- Cited by
- 8,806
- References
- 1
- FWCI
- 106.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Artzner, P., Delbaen, F., Eber, J., & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance. https://doi.org/10.1111/1467-9965.00068
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
