Portfolio optimization under lower partial moments in emerging electricity markets: Evidence from Turkey

Gökgöz, Fazıl; Atmaca, Mete Emin

doi:10.1016/j.rser.2016.09.029

Cited by 27 publications

(12 citation statements)

References 21 publications

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“…The existing finance literature also confirms efficiency and effectiveness of PGP in solving similar problems with various contradictory risk preferences by including asymmetry (third moment of risk) according to investors' preferences to select portfolios [1]. Being a flexible method of including higher moments of risk (variance, skewness and kurtosis), PGP is helpful for optimization of portfolio as per risk preferences of investors without explicit specific utility function though inclusion of higher moments should be defined explicitly.…”

Section: Literature Reviewmentioning

confidence: 77%

“…Therefore, it is essential to include these risk moments while constructing an optimized portfolio [33]. Previously, scholars stress on the inclusion of skewness and argue that incorporation of skewness not only improves the efficiency of mean-variance portfolio but also effect on optimization of portfolio and its selection [1,4,12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on this argument the inclusion of higher moments is vital for a sustainable optimized portfolio and efficient frontier. Many studies emphasize on the inclusion of skewness because the third moment of risk's inclusion enhances the efficiency in optimization as compared to traditional mean-variance optimization [1,10,11]. Over the last few decades the fourth moment of risk has gained the attention of finance academicians, professional and investors [39].…”

Section: Skewness and Kurtosis Inclusion For Sustainable Optimizationmentioning

confidence: 99%

“…In today's world the stock market is considered a gauge for economic development and growth of a country that makes the stock market a vital organ to look at an economy [1,2]. The stock market helps investors to predict the outcome of an economy, and whether it is sustainable or not [3,4], since the stock market is considered a barometer of economic growth, activities, and other economic functions [5].…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Portfolio Optimization with Higher-Order Moments of Risk

et al. 2020

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Sustainable economic growth and development of stock market plays an important role in diversifying the investment opportunities that can be assessed accordingly. However, a true diversification in portfolio is impossible without inclusion of higher-order moments, skewness and kurtosis. However, the risk-taking behavior of investors is modelled with the help of higher-order moments of risk. Therefore, this study is intended to construct optimal portfolios and efficient frontiers with the inclusion of higher-order moments of risk. The findings show that optimized portfolios with inclusion of skewness and kurtosis are sustainable and significantly different than those from mean-variance optimized portfolios which show asymmetric and fat-tail risk. Results further confirm its significance in balancing the additional risk dimensions and returns in Asian emerging stock markets for sustainable returns. The results also endorse that induction of skewness and kurtosis affects portfolio allocation weights and expected returns. Therefore, this study strongly recommends the inclusion of higher moments of risk for optimization to curtail their effect and sub-optimal decisions.

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Section: Literature Reviewmentioning

confidence: 77%

Section: Literature Reviewmentioning

confidence: 99%

Section: Skewness and Kurtosis Inclusion For Sustainable Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sustainable Portfolio Optimization with Higher-Order Moments of Risk

et al. 2020

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“…Nowadays, numerous scholars have carried out researches on various subjects relevant to the retail electricity market such as business model [3], pricing strategy [4], risk management [5,6] and electricity price prediction [7][8][9][10][11]. In the matter of energy demand, various forecasting models have been applied to predict the electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain

Qin

Nie

et al. 2018

Mathematical Problems in Engineering

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Due to the deregulation of retail electricity market, consumers can choose retail electric suppliers freely, and market entities are facing fierce competition because of the increasing number of new entrants. Under these circumstances, forecasting the changes in all market entities, when market share stabilized, is important for suppliers making marketing decisions. In this paper, a market share forecasting model was established based on Markov chain, and a system dynamics model was constructed to forecast the electricity consumption based on the analysis of five factors which are economic development, policy factors, environmental factors, power energy substitution, and power grid development. For a real application, the retail electricity market of Guangdong province in China was selected. The total, industrial, and commercial electricity consumption in Guangdong from 2016 to 2020 were predicted under different scenarios, and the market shares of the main market entities were analyzed using Markov chain model. Results indicated that the direct trading electricity would account for 70% to 90% of the total electricity consumption in the future. This provided valuable reference for the decision-making of suppliers and the development of electricity industry.

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Financial Optimization in the Renewable Energy Sector

Gökgöz

Erdoğan

2022

Springer Proceedings in Energy

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Portfolio optimization under lower partial moments in emerging electricity markets: Evidence from Turkey

Cited by 27 publications

References 21 publications

Sustainable Portfolio Optimization with Higher-Order Moments of Risk

Sustainable Portfolio Optimization with Higher-Order Moments of Risk

Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain

Financial Optimization in the Renewable Energy Sector

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