Nonparametric estimation of 100(1 − p)% expected shortfall: p → 0 as sample size is increased

Dutta, Santanu; Biswas, Suparna

doi:10.1080/03610918.2016.1152370

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…We first discuss the nonparametric estimators of ES. There are numerous ES estimation methods available in the literature; see, for example, Broda and Paolella [17], Nadarajah et al [15], and Dutta and Biswas [16]. Before defining the RVaR estimators, we first define the ES nonparametric estimators.…”

Section: Nonparametric Rvar Estimatorsmentioning

confidence: 99%

“…The MSE of the seven estimators Em p,q , RVaR p,q , Ker p,q , Hill p,q , RVaR p,q,β , FH p,q and µ p,q,θ is estimated in order to compare the behavior of these estimators in finite samples by simulating observations from several models. Considered are three models (see [16]).…”

Section: Simulationmentioning

confidence: 99%

“…al. [15]; and Dutta and Biswas [16] for exemplary contributions. We also find papers in which the authors discuss how the choice of different VaR estimators affects critical investment decisions (see [11]).…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric Estimation of Range Value at Risk

Biswas

Sen

2023

Computation

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Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The estimation of RVaR is a critical issue in the financial sector. Several nonparametric RVaR estimators are described here. We examine these estimators’ accuracy in various scenarios using Monte Carlo simulations. Our simulations shed light on how changing p and q with respect to n affects the effectiveness of RVaR estimators that are nonparametric, with n representing the total number of samples. Finally, we perform a backtesting exercise of RVaR based on Acerbi and Szekely’s test.

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Section: Nonparametric Rvar Estimatorsmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

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Nonparametric Estimation of Range Value at Risk

Biswas

Sen

2023

Computation

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“…For instance, estimation procedures are only well-established in the literature for some of the new candidates. Beyond VaR, estimation methods for the ES were discussed, for instance, by Nadarajah et al (2014) or Dutta and Suparna (2018), whereas estimation of distorted risk measures relates to Tsukahara (2014) or Kim (2010) or Rassoul (2014). Similar to Tasche (2016) who advocates a so-called Quantile-ES matching, other combinations could be used to derive specific estimators with focus on the tail.…”

mentioning

confidence: 99%

A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

Fischer

Moser²,

Pfeuffer

2018

Risks

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In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler's allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that-in principle-all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures.

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A new non-parametric estimation of the expected shortfall for dependent financial losses

Moutanabbir,

Bouaddi

2024

Journal of Statistical Planning and Inference

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Nonparametric estimation of 100(1 − p)% expected shortfall: p → 0 as sample size is increased

Cited by 5 publications

References 11 publications

Nonparametric Estimation of Range Value at Risk

Nonparametric Estimation of Range Value at Risk

A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

A new non-parametric estimation of the expected shortfall for dependent financial losses

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