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Cited by 350Risk and Portfolio OptimizationFinancial Risk and Volatility ModelingProbability and Risk Models

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

DRAWDOWN MEASURE IN PORTFOLIO OPTIMIZATION

Open access
CHEKHLOV, ALEXEI, URYASEV, STANISLAV, ZABARANKIN, MICHAEL·2005·International Journal of Theoretical and Applied Finance
DOI 10.1142/s0219024905002767
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Abstract

A new one-parameter family of risk measures called Conditional Drawdown (CDD) has been proposed. These measures of risk are functionals of the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter Alpha, in the case of a single sample path, drawdown functional is defined as the mean of the worst (1 - Alpha) 100% drawdowns. The CDD measure generalizes the notion of the drawdown functional to a multi-scenario case and can be considered as a generalization of deviation measure to a dynamic case. The CDD measure includes the Maximal Drawdown and Average Drawdown as its limiting cases. Mathematical properties of the CDD measure have been studied and efficient optimization techniques for CDD computation and solving asset-allocation problems with a CDD measure have been developed. The CDD family of risk functionals is similar to Conditional Value-at-Risk (CVaR), which is also called Mean Shortfall, Mean Excess Loss, or Tail Value-at-Risk. Some recommendations on how to select the optimal risk functionals for getting practically stable portfolios have been provided. A real-life asset-allocation problem has been solved using the proposed measures. For this particular example, the optimal portfolios for cases of Maximal Drawdown, Average Drawdown, and several intermediate cases between these two have been found.

Overview

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Crossref · 10.1142/s0219024905002767

Cites

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Citations

Cites

Research works printed but not linked: 5

  1. Cvitanic J., IMA Lecture Notes in Mathematics & Applications, 65, 77
  2. Jorion P., 1996, Value at Risk: A New Benchmark for Measuring Derivatives Risk
  3. Markowitz H. M., Journal of Finance, 7, 77
  4. Mausser H., ALGO Research Quarterly, 1, 5
  5. G. C. Pflug, Probabilistic Constrainted Optimization: Methodology and Applications, ed. S. P. Uryasev (Kluwer, 2000) pp. 278–287.

Cited by

See all 350

Metadata

Authors
CHEKHLOV, A., URYASEV, S., & ZABARANKIN, M.
Affiliations
  • Thor Asset Management, Inc., 551 Fifth Ave., Suite 601, 6th Floor, New York, NY 10017, USA
  • Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, 303 Weil Hall, Gainesville, FL 32611-6595, USA
  • University of Florida (US)
ORCID
Venue
International Journal of Theoretical and Applied Finance
Publisher
World Scientific Pub Co Pte Ltd
Published
2005-01
Volume
08
Issue
01
Pages
13-58
ISSN
  • 0219-0249 (ISSN-L)
  • 1793-6322
DOI
10.1142/s0219024905002767
OpenAlex
W3123675609
Cited by
350
References
33
Citations by year
2026: 34 · 2025: 24 · 2024: 20 · 2023: 31 · 2022: 15 · 2021: 16 · 2020: 19 · 2019: 14 · 2018: 10 · 2017: 16 · 2016: 20 · 2015: 18 · 2014: 15 · 2013: 30 · 2012: 17
FWCI
9.00
Type
journal article
Language
en
Open access
Locations
  • International Journal of Theoretical and Applied Finance (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Risk and Portfolio Optimization
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
Keywords
risk measures, portfolio optimization, asset allocation, conditional value at risk, deviation measures, active portfolio management

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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.