Loss-based risk measures

Cont, Rama; Deguest, Romain; He, Xue Dong

doi:10.1524/strm.2013.1132

Cited by 48 publications

(74 citation statements)

References 26 publications

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“…Academically, Artzner et al (1999) proposed coherent risk measures, including conditional value-at-risk (CVaR) as a special case. Each of these two types of risk measures has its own advantages and disadvantages; overall, the former is suitable for external use and the latter for internal use (Kou, Peng, and Heyde, 2013;Cont, Deguest, and He, 2013).…”

Section: The Risk Of the Fundmentioning

confidence: 99%

Profit Sharing in Hedge Funds

Kou

2016

Mathematical Finance

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In a new scheme for hedge fund managerial compensation known as the first‐loss scheme, a fund manager uses her investment in the fund to cover any fund losses first; by contrast, in the traditional scheme currently used in most US funds, the manager does not cover investors' losses in the fund. We propose a framework based on cumulative prospect theory to compute and compare the trading strategies, fund risk, and managers' and investors' utilities in these two schemes analytically. The model is calibrated to the historical attrition rates of US hedge funds. We find that with reasonable parameter values, both fund managers' and investors' utilities can be improved and fund risk can be reduced simultaneously by replacing the traditional scheme (with 10% internal capital and 20% performance fee) with a first‐loss scheme (with 10% first‐loss capital and 30% performance fee). When the performance fee in the first‐loss scheme is 40% (a current market practice), however, such substitution renders investors worse off.

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Section: The Risk Of the Fundmentioning

confidence: 99%

Profit Sharing in Hedge Funds

Kou

2016

Mathematical Finance

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“…We refer the reader for definitions and the various variants of monetary risk measures to [19,20]. Important for us is the contribution by [21], who introduced natural risk statistics.…”

Section: A Suitable Distance For Risk Managementmentioning

confidence: 99%

The Wasserstein Metric and Robustness in Risk Management

Kiesel

Rühlicke

Stahl³

et al. 2016

Risks

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Abstract:In the aftermath of the financial crisis, it was realized that the mathematical models used for the valuation of financial instruments and the quantification of risk inherent in portfolios consisting of these financial instruments exhibit a substantial model risk. Consequently, regulators and other stakeholders have started to require that the internal models used by financial institutions are robust. We present an approach to consistently incorporate the robustness requirements into the quantitative risk management process of a financial institution, with a special focus on insurance. We advocate the Wasserstein metric as the canonical metric for approximations in robust risk management and present supporting arguments. Representing risk measures as statistical functionals, we relate risk measures with the concept of robustness and hence continuity with respect to the Wasserstein metric. This allows us to use results from robust statistics concerning continuity and differentiability of functionals. Finally, we illustrate our approach via practical applications.

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“…Cont et al [4] also study risk measures that are excess-invariant, which they call ''loss-dependent''. Their class of loss-based risk measures has a somewhat different set of axioms than the class of shortfall risk measures.…”

Section: Introductionmentioning

confidence: 99%