Modified expected shortfall: a new robust coherent risk measure

Jadhav, Deepak; Ramanathan, T. V.; Naik‐Nimbalkar, U. V.

doi:10.21314/jor.2013.269

Cited by 16 publications

(16 citation statements)

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“…Concerning the risk measure, many possibilities can be considered (Emmer et al, 2015). For example it could be the standard deviation as well as the value-at-risk (VaR) or the conditional value-at-risk (CVaR), to name the most popular ones, but also more a more advanced risk measure such as the modified expected shortfall (Jadhav et al, 2013). In the theory of risk measures some possible properties are well-known.…”

Section: Output-oriented Viewmentioning

confidence: 99%

Valuing streams of risky cashflows with risk-value models

Dorfleitner¹,

Gleißner²

2018

JOR

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Based on risk-value models we introduce a multi-period approach to the valuation of streams of risky cash flows. The valuation is based on the (expected) value of the output's or input's magnitude and the risk of the output cash flow, as captured by a risk measure. We derive three formulae for valuing single cash flows and utilize the principles of separate valuation and of cumulating the cash flows to derive a multi-period valuation method. In an axiomatic way, the article sets the foundations for a new approach and suggests several directions for its further development.

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Section: Output-oriented Viewmentioning

confidence: 99%

Valuing streams of risky cashflows with risk-value models

Dorfleitner¹,

Gleißner²

2018

JOR

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“…The commonly used coherent risk measure is the Conditional VaR (CoVaR), defined as mean of losses 1 beyond VaR, see e.g. Artzner et al ( 1999 ), McNeil et al (2005) , Jadhav et al ( 2009 , 2013 ), Righi and Ceretta ( 2015 ), and Brahim et al (2018) . Several extensions of CoVaR have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Dependent conditional value-at-risk for aggregate risk models

Josaphat

Syuhada

2021

Heliyon

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Risk measure forecast and model have been developed in order to not only provide better forecast but also preserve its (empirical) property especially coherent property. Whilst the widely used risk measure of Value-at-Risk (VaR) has shown its performance and benefit in many applications, it is in fact not a coherent risk measure. Conditional VaR (CoVaR), defined as mean of losses beyond VaR, is one of alternative risk measures that satisfies coherent property. There have been several extensions of CoVaR such as Modified CoVaR (MCoVaR) and Copula CoVaR (CCoVaR). In this paper, we propose another risk measure, called Dependent CoVaR (DCoVaR), for a target loss that depends on another random loss, including model parameter treated as random loss. It is found that our DCoVaR provides better forecast than both MCoVaR and CCoVaR. Numerical simulation is carried out to illustrate the proposed DCoVaR. In addition, we do an empirical study of financial returns data to compute the DCoVaR forecast for heteroscedastic process of GARCH(1,1). The empirical results show that the Gumbel Copula describes the dependence structure of the returns quite nicely and the forecast of DCoVaR using Gumbel Copula is more accurate than that of using Clayton Copula. The DCoVaR is superior than MCoVaR, CCoVaR and CoVaR to comprehend the connection between bivariate losses and to help us exceedingly about how optimum to position our investments and elevate our financial risk protection. In other words, putting on the suggested risk measure will enable us to avoid non-essential extra capital allocation while not neglecting other risks associated with the target risk. Moreover, in actuarial context, DCoVaR can be applied to determine insurance premiums while reducing the risk of insurance company.

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“…Furthermore, TVaR is a coherent risk measure. On the extensions of TVaR is Modified CoVaR of Jadhav et al [8] in which this proposed paper relies on loosely. In particular, our aim is to find an upper bound for TVaR applied for aggregate risk model.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is organized as follows. Section 2 describes of Adjusted TVaR (Adj-TVaR) of Jadhav et al [8]. The upper bound of Adj-TVaR for aggregate risk and its comonotonic counterpart is given in Section 3.…”

Section: Introductionmentioning

confidence: 99%

Bounds of Adj-TVaR Prediction for Aggregate Risk

Syuhada

2019

InPrime:Ind.Jour.Pure.Applied.Math

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In ﬁnancial and insurance industries, risks may come from several sources. It is therefore important to predict future risk by using the concept of aggregate risk. Risk measure prediction plays important role in allocating capital as well as in controlling (and avoiding) worse risk. In this paper, we consider several risk measures such as Value-at-Risk (VaR), Tail VaR (TVaR) and its extension namely Adjusted TVaR (Adj-TVaR). Speciﬁcally, we perform an upper bound for such risk measure applied for aggregate risk models. The concept and property of comonotonicity and convex order are utilized to obtain such upper bound.Keywords: Coherent property, comonotonic rv, convex order, tail property, Value-at-Risk (VaR).

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Modified expected shortfall: a new robust coherent risk measure

Cited by 16 publications

References 0 publications

Valuing streams of risky cashflows with risk-value models

Valuing streams of risky cashflows with risk-value models

Dependent conditional value-at-risk for aggregate risk models

Bounds of Adj-TVaR Prediction for Aggregate Risk

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