Analysis of Risk Transmission from Generation Right Trading to Generation Company Profit

2011 ◽  
Vol 403-408 ◽  
pp. 2856-2860 ◽  
Author(s):  
Xian Li ◽  
Cun Bin Li ◽  
Gong Shu Lu

With the booming of generation right trade(GRT) market, the GRT risk will exert an increasingly important impact on profit of power plants. Hence, Two conditional value at risk (CVaR) models are built for generation rights sellers and buyers respectively. Then, different proportion constraints are set to discuss the influence of GRT. A conclusion can be drawn that the variances of the ratio of generation right power amount may lead to different amount of distribution in different markets as well as the changes of the efficient frontier curves.

Author(s):  
TUNCER ŞAKAR CEREN ◽  
MURAT KÖKSALAN

We study the effects of considering different criteria simultaneously on portfolio optimization. Using a single-period optimization setting, we use various combinations of expected return, variance, liquidity and Conditional Value at Risk criteria. With stocks from Borsa Istanbul, we make computational studies to show the effects of these criteria on objective and decision spaces. We also consider cardinality and weight constraints and study their effects on the results. In general, we observe that considering alternative criteria results in enlarged regions in the efficient frontier that may be of interest to the decision maker. We discuss the results of our experiments and provide insights.


Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1677
Author(s):  
Zdravka Aljinović ◽  
Branka Marasović ◽  
Tea Šestanović

This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.


2014 ◽  
Vol 16 (6) ◽  
pp. 3-29 ◽  
Author(s):  
Samuel Drapeau ◽  
Michael Kupper ◽  
Antonis Papapantoleon

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