Entropic risk measures and their comparative statics in portfolio selection: Coherence vs. convexity

Brandtner, Mario; Kürsten, Wolfgang; Rischau, Robert

doi:10.1016/j.ejor.2017.07.007

Cited by 16 publications

(4 citation statements)

References 24 publications

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“…Above that, extensions and alteratives (beyond the scope of our analysis) can be found in the current literature: First, Assa et al (2016) put forward the idea of using a cumulative risk measure based on the Entropic Value at Risk (CEVaR). Application to portfolio optimization was provided by Brandtner et al (2018). Second, φ-EVaR as an extension is discussed by Pichler and Schlotter (2018) by replacing the relative entropy in the dual representation with different divergences as suggested in Ahmadi-Javid (2012c) first.…”

Section: Risk Measures Beyond Varmentioning

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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Section: Risk Measures Beyond Varmentioning

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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“…They are based on the concept of relative entropy, which is a measure of divergence between two probability distributions 1 . Two classes of entropic risk measures have been compared in Brandtner et al (2018), namely coherent or convex. These properties of risk measures are discussed largely in the literature and express some nice properties in terms of decision making strategies or solving complex portfolio optimization problems, see Cheridito et al (2005), Detlefsen and Scandolo (2005), Ben-Tal and Teboulle (2007), Ruszczynski and Shapiro (2004), Föllmer and Penner (2006), Seck et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Regime Switching Entropic Risk Measures on Crude Oil Pricing

Seck¹,

Elliott²

2021

Preprint

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This paper introduces a new type of risk measures, namely regime switching entropic risk measures, and study their applicability through simulations. The state of the economy is incorporated into the entropic risk formulation by using a Markov chain. Closed formulae of the risk measure are obtained for futures on crude oil derivatives. The applicability of these new types of risk measures is based on the study of the risk aversion parameter and the convenience yield. The numerical results show a term structure and a mean-reverting behavior of the convenience yield.

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“…The extended forms and models can deal with different types of portfolio selection issues according to the different aims. Second, to optimize the existing portfolio selection methods, some improved models have been presented, such as the mean absolute deviation portfolio model (Simaan, 1997), the Bayesian framework portfolio model (P astor, 2000), the mean-VaR portfolio model (Alexander & Baptista, 2002), the chance constrained portfolio model (Abdelaziz et al, 2007), the constrained fuzzy analytic hierarchy portfolio model (Nguyen & Gordon-Brown, 2012), the risk-return portfolio model (Brandtner et al, 2018), and the mean-risk portfolio model (Mehralizade et al, 2020). Third, to deal with different real problems, some new models have been designed.…”

Section: Introductionmentioning

confidence: 99%

Investment decision making based on the probabilistic hesitant financial data: model and empirical study

Zhou

Liu

Zhang

et al. 2020

Economic Research-Ekonomska Istraživanja

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This paper proposes a portfolio selection model from the perspective of probabilistic hesitant financial data (PHFD). PHFD can be interpreted as the new form of information presentation that is obtained by transforming real financial data into probabilistic hesitant fuzzy elements. Based on the above data and model, we can derive the optimal investment ratios and give suggestions for investors. Specifically, this paper first develops a transformation algorithm to transform the general share returns into PHFD. The transformed data can directly show all the returns and their occurrence probabilities. Then, the portfolio selection and risk portfolio selection models based on PHFD, namely the probabilistic hesitant portfolio selection (PHPS) model and the risk probabilistic hesitant portfolio selection (RPHPS) model, are proposed. Furthermore, the investment decision-making methods are provided to show their practical application in financial markets. It is pointed out that the PHPS model for general investors is constructed based on the maximum-score or minimum-deviation principles to get the optimal investment ratios, and the RPHPS model provides the optimal investment ratios for three types of risk investors with the aim of obtaining the maximum return or taking the minimum risk. Finally, an empirical study based on the real data of China's stock markets is shown in detail. The results verify the effectiveness and practicability of the proposed methods.

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Entropic risk measures and their comparative statics in portfolio selection: Coherence vs. convexity

Cited by 16 publications

References 24 publications

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

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

Regime Switching Entropic Risk Measures on Crude Oil Pricing

Investment decision making based on the probabilistic hesitant financial data: model and empirical study

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