Optimal expected utility risk measures

Geissel, Sebastian; Sass, Jörn; Seifried, Frank Thomas

doi:10.1515/strm-2017-0027

Cited by 18 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feasible and desirable extensions for future research are (i) to re-examine the rationality of our axioms considering other risk measures in rankdependent expected utility theory such as the optimal expected utility risk measures as in the study by Geissel, Sass, and Seifried (2018) and the extreme risk aggregation approach as in the study by Chen and Hu (2019);…”

Section: Concluding Remarks and Future Researchmentioning

confidence: 99%

Determination of Quebec's Quarterly Real GDP and Analysis of the Business Cycle, 1948–1980

Fortin

Joanis²,

Kabore³

et al. 2022

J Bus Cycle Res

View full text Add to dashboard Cite

We provide an axiomatic foundation for the measurement of correlation diversification measures in a one-period portfolio model. We propose a set of nine desirable axioms for this class of diversification measures. We name the measures satisfying these axioms coherent correlation diversification measures which we distinguish from coherent risk measures. We provide the decision theoretic foundations of our axioms by studying their compatibility with investors' preference for diversification in rank-dependent expected utility theory. We examine some of the most frequently used methods for measuring correlation diversification in terms of our axioms. Lastly, we explore whether our axioms have a representation function.

show abstract

Section: Concluding Remarks and Future Researchmentioning

confidence: 99%

Determination of Quebec's Quarterly Real GDP and Analysis of the Business Cycle, 1948–1980

Fortin

Joanis²,

Kabore³

et al. 2022

J Bus Cycle Res

View full text Add to dashboard Cite

show abstract

“…The theoretical setup and notations are, with respect to risk measures, similar to Geissel et al (2017). With the filtration (F) 0≤t≤T , T > 0 that is fixed on the probability space ( , F , P) in such a way that F 0 = {∅, } and F T = F , we can specify a financial position X : → R as a random variable on the probability space ( , F , P).…”

Section: Setup and Notationsmentioning

confidence: 99%

“…Example 2 Given a utility function u, the optimal expected utility risk measure is introduced in Geissel et al (2017) as the map ρ u : X → R,…”

Section: Examplementioning

confidence: 99%

See 1 more Smart Citation

Portfolio optimization with optimal expected utility risk measures

et al. 2021

View full text Add to dashboard Cite

The purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the expected portfolio return is maximized. We compare the portfolio optimization with OEU constraint to a portfolio selection model using value at risk as constraint. The former is a coherent risk measure for utility functions with constant relative risk aversion and allows individual specifications to the investor’s risk attitude and time preference. In a case study with three indices, we investigate how these theoretical differences influence the performance of the portfolio selection strategies. A copula approach with univariate ARMA-GARCH models is used in a rolling forecast to simulate monthly future returns and calculate the derived measures for the optimization. The results of this study illustrate that both optimization strategies perform considerably better than an equally weighted portfolio and a buy and hold portfolio. Moreover, our results illustrate that portfolio optimization with OEU constraint experiences individualized effects, e.g., less risk-averse investors lose more portfolio value in the financial crises but outperform their more risk-averse counterparts in bull markets.

show abstract

“…Based on this extension, the authors of [ 7 ] develop a subclass of the entropic risk measure and contribute the connections with variational preferences by assuring the de-utility function

as linear or exponential. The authors of [ 9 ] extend to a more general form based on the utility theory, while the authors of [ 28 ] develop the optimal expected utility (OEU) risk measure by modifying the OCE, which benefits from the easy application defined on optimizing in the real field.…”

Section: Introductionmentioning

confidence: 99%

Divergence-Based Risk Measures: A Discussion on Sensitivities and Extensions

Angulo

2019

Entropy

View full text Add to dashboard Cite

This paper introduces a new family of the convex divergence-based risk measure by specifying ( h , ϕ ) -divergence, corresponding with the dual representation. First, the sensitivity characteristics of the modified divergence risk measure with respect to profit and loss (P&L) and the reference probability in the penalty term are discussed, in view of the certainty equivalent and robust statistics. Secondly, a similar sensitivity property of ( h , ϕ ) -divergence risk measure with respect to P&L is shown, and boundedness by the analytic risk measure is proved. Numerical studies designed for Rényi- and Tsallis-divergence risk measure are provided. This new family integrates a wide spectrum of divergence risk measures and relates to divergence preferences.

show abstract

Optimal expected utility risk measures

Cited by 18 publications

References 29 publications

Determination of Quebec's Quarterly Real GDP and Analysis of the Business Cycle, 1948–1980

Determination of Quebec's Quarterly Real GDP and Analysis of the Business Cycle, 1948–1980

Portfolio optimization with optimal expected utility risk measures

Divergence-Based Risk Measures: A Discussion on Sensitivities and Extensions

Contact Info

Product

Resources

About