Second Order Stochastic Dominance Portfolio Optimization for an Electric Energy Company

Cheong, M.-P.; Sheblé, G.B.; Berleant, Daniel; Teoh, C.C.; Argaud, Jean-Philippe; Dancre, M.; Andrieu, Laetitia; Barjon, F.

doi:10.1109/pct.2007.4538421

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…For as-sessment purposes, this study uses the nonparametric SD test proposed by Davidson and Duclos (2000). As discussed earlier, this method offers a robust and distribution-free approach, while considering higher moments of the return distribution (Cheong et al, 2007) to evaluate portfolio performance:…”

Section: Stochastic Dominance (Sd) Analysis and Risk Preferencesmentioning

confidence: 99%

Examining the performance of Shari’ah‑compliant versus conventional stock indexes: A comparative analysis pre-, during, and post-COVID-19

Abu-Alkheil,

Alsharari,

Khan

et al. 2024

EBR

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This study aims to conduct an empirical comparative analysis of the performance of Shari’ah and conventional stock indexes during the period 2017–2023, which includes the COVID-19 pandemic. Additionally, it aims to investigate investors’ preferences and analyse the long-term relationship of these indexes, as well as exploring the potential diversification benefits. The research methodology incorporates stochastic dominance analysis, the VARMAX procedure, and Johansen’s co-integration approach. The data utilized consists of 31 conventional and 31 Islamic stock indexes, specifically from the FTSE, DJ, MSCI, and S&P series.The results show that there are no long-term co-integration links between 30 out of 31 pairs of Islamic and conventional indexes. While conventional indexes tend to outperform Islamic indexes, they also come with a higher risk. On the other hand, Islamic indexes are considered to be less risky, offering potential diversification opportunities that may be attractive for global portfolios, particularly during periods of financial distress.

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Section: Stochastic Dominance (Sd) Analysis and Risk Preferencesmentioning

confidence: 99%

Examining the performance of Shari’ah‑compliant versus conventional stock indexes: A comparative analysis pre-, during, and post-COVID-19

Abu-Alkheil,

Alsharari,

Khan

et al. 2024

EBR

View full text Add to dashboard Cite

show abstract

“…The second-order stochastic dominance constraint has been imposed in [54] to reduce the risk of low profit for an electricity retailer, and in [55] for self-scheduling large consumers. In [56] portfolio optimization has been carried out by setting up a minimum tolerable CDF used as the reference; then, the CDF of the portfolio model is accepted if it exhibits second-order stochastic dominance over the reference CDF.…”

Section: Stochastic Dominance Applications To Power and Energy Systemsmentioning

confidence: 99%

Heuristic optimization of electrical energy systems: Refined metrics to compare the solutions

Chicco

Mazza

2019

Sustainable Energy, Grids and Networks

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Many optimization problems admit a number of local optima, among which there is the global optimum. For these problems, various heuristic optimization methods have been proposed. Comparing the results of these solvers requires the definition of suitable metrics. In the electrical energy systems literature, simple metrics such as best value obtained, the mean value, the median or the standard deviation of the solutions are still used. However, the comparisons carried out with these metrics are rather weak, and on these bases a somehow uncontrolled proliferation of heuristic solvers is taking place. This paper addresses the overall issue of understanding the reasons of this proliferation, showing a conceptual scheme that indicates how the assessment of the best solver may result in the unlimited formulation of new solvers. Moreover, this paper shows how the use of more refined metrics defined to compare the optimization result, associated with the definition of appropriate benchmarks, may make the comparisons among the solvers more robust. The proposed metrics are based on the concept of firstorder stochastic dominance and are defined for the cases in which: (i) the globally optimal solution can be found (for testing purposes); and (ii) the number of possible solutions is so large that practically it cannot be guaranteed that the global optimum has been found. Illustrative examples are provided for a typical problem in the electrical energy systems area -distribution network reconfiguration. The conceptual results obtained are generally valid to compare the results of other optimization problems.2

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“…Cheong et al. (2007) and Nie et al. (2012) applied SSD ranking in electric energy problems and optimal path problems, respectively.…”

Section: Introductionmentioning

confidence: 99%

Deviation measure in second‐order stochastic dominance with an application to enhanced indexing

Goel

Sharma

2019

Int Trans Operational Res

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Deviation measures form a separate class of functionals applied to the difference of a random variable to its mean value. In this paper, we aim to introduce a deviation measure in the second‐order stochastic dominance (SSD) criterion to select an optimal portfolio having a higher utility of deviation from its mean value than that of the benchmark portfolio. A new strategy, called deviation SSD (DSSD), is proposed in portfolio optimization. Performance of the proposed model in an application to enhanced indexing is evaluated and compared to other two standard portfolio optimization models that employ SSD criterion on lower partial moments of order one (LSSD) and tail risk measure (TSSD). We use historical data of 16 global indices to assess the performance of the proposed model. In addition to this, we solved the models on simulated data, capturing the joint dependence between indices using historical data of two data sets, each data set comprising six indices. The simulated data are generated by first fitting the autoregressive moving average–Glosten–Jagannathan–Runkle–generalized autoregressive conditional heteroscedastic model on historical data to determine marginal distributions, and thereafter capture the dependence structure using the best fitted regular vine copula. The portfolios from DSSD model achieve higher excess mean return than TSSD model and lower variance, downside deviation, and conditional value‐at‐risk than LSSD model. Also, portfolios from DSSD model demonstrate higher values of information ratio, stable tail‐adjusted return ratio and Sortino ratio than the other two models. Lastly, DSSD model is observed to produce well‐diversified portfolios compared to LSSD model.

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Second Order Stochastic Dominance Portfolio Optimization for an Electric Energy Company

Cited by 10 publications

References 12 publications

Examining the performance of Shari’ah‑compliant versus conventional stock indexes: A comparative analysis pre-, during, and post-COVID-19

Examining the performance of Shari’ah‑compliant versus conventional stock indexes: A comparative analysis pre-, during, and post-COVID-19

Heuristic optimization of electrical energy systems: Refined metrics to compare the solutions

Deviation measure in second‐order stochastic dominance with an application to enhanced indexing

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