Adjusted Expected Shortfall

Burzoni, Matteo; Munari, Cosimo; Wang, Ruodu

doi:10.1016/j.jbankfin.2021.106297

Cited by 20 publications

(9 citation statements)

References 39 publications

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“…Let g : (0, 1] → [0, ∞] be decreasing with g(1) = 0. The adjusted Expected Shortfall of X ∈ L 1 with risk profile g is defined by (see [15] 2 )…”

Section: Adjusted Expected Shortfallmentioning

confidence: 99%

“…Note that because we work on L 1 , we have changed the domain of g so that it does not include 0. Nevertheless all results in[15] carry over.…”

mentioning

confidence: 95%

“…On a more theoretical level, there have been several attempts to study alternative tail risk measures and describe a general framework for tail risk measures with capital adequacy in mind, see, e.g., [7,11,15,21,40,41,46]. In particular, the defining property of a tail risk measure in [41,46] is that it should preserve the ranking of random variables under addition of comonotonic risk or that the risk measure does not discriminate between random variables whose quantiles beyond a certain level coincide.…”

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confidence: 99%

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Sensitivity to large losses and ρ-arbitrage for convex risk measures

Herdegen

Khan

2021

SSRN Journal

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“…Let g : (0, 1] → [0, ∞] be decreasing with g(1) = 0. The adjusted Expected Shortfall of X ∈ L 1 with risk profile g is defined by (see [15] 2 )…”

Section: Adjusted Expected Shortfallmentioning

confidence: 99%

“…Note that because we work on L 1 , we have changed the domain of g so that it does not include 0. Nevertheless all results in[15] carry over.…”

mentioning

confidence: 95%

mentioning

confidence: 99%

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Sensitivity to large losses and ρ-arbitrage for convex risk measures

Herdegen

Khan

2021

SSRN Journal

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“…As noted by Liu and Wang [24], the consideration of tail risk, i.e., the risk beyond a given threshold, is crucial in today's financial regulation. We also refer to Bignozzi et al [6] for a generalization of the value at risk that depends on the size of potential losses in the form of quantile-based risk measures, to Fadina et al [16] for an axiomatic study of quantiles, and to Burzoni et al [9] for a study of adjusted expected shortfall. In Section 3, we show that each default risk measure induces its own notion of a value at risk, and establish a ono-to-one relation between default risk measures and so-called generalized quantile functions.…”

Section: Introductionmentioning

confidence: 99%

An axiomatic approach to default risk and model uncertainty in rating systems

Nendel¹,

Streicher²

2023

Preprint

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In this paper, we deal with an axiomatic approach to default risk. We introduce the notion of a default risk measure, which generalizes the classical probability of default (PD), and allows to incorporate model risk in various forms. We discuss different properties and representations of default risk measures via monetary risk measures, families of related tail risk measures, and Choquet capacities. In a second step, we turn our focus on default risk measures, which are given as worst-case PDs and distorted PDs. The latter are frequently used in order to take into account model risk for the computation of capital requirements through risk-weighted assets (RWAs), as demanded by the Capital Requirement Regulation (CRR). In this context, we discuss the impact of different default risk measures and margins of conservatism on the amount of risk-weighted assets.

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“…Since then, it has been becoming increasingly popular for risk measurement and has been widely used by actuaries, insurance companies, and so on. For more information, see Acerbi and Tasche (2002), Scaillet (2004), Embrechts et al (2014), Nadarajah et al (2014), Patton et al (2019), Wang and Zitikis (2021), Burzoni et al (2022), and so forth. However, in practice, estimating ES is often challenging, partially because ES is a tail risk measure.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric estimation of expected shortfall and its application in finance

Yan

Liu

et al. 2022

Journal of Forecasting

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Measuring risk effectively is crucial for managing risk in financial markets. The expected shortfall has become an increasingly popular metric for risk in recent years. How to estimate it is important in statistics and financial econometrics. Based on the single index quantile regression, we introduce a new semiparametric approach, namely, weighted single index quantile regression. We assess the performance of the proposed expected shortfall estimator with backtesting. Our simulation results indicate that the estimator has a good finite sample performance and often outperforms existing methods. By applying the new method to both a market index and individual stocks, we show that it not only exhibits the best performance but also reveals an insight about the effect of the COVID pandemic, that is, the pandemic increases the market risk.

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Adjusted Expected Shortfall

Cited by 20 publications

References 39 publications

Sensitivity to large losses and ρ-arbitrage for convex risk measures

Sensitivity to large losses and ρ-arbitrage for convex risk measures

An axiomatic approach to default risk and model uncertainty in rating systems

Semiparametric estimation of expected shortfall and its application in finance

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