scholarly journals A Nucleolus-Based Quota Allocation Model for the Bitcoin-Refunded Blockchain Network

Author(s):  
Eduardo Bolonhez ◽  
Thuener Silva ◽  
Bruno Fanzeres dos Santos

Abstract The Bitcoin operates in a Blockchain network under which a group of participants are responsible for adding new blocks into the chain. These participants are called miners and the ones that successfully add a block into the network receive a reward for their work. As the technology evolved over the years, this "mining" process has become more challenging with miners facing long periods without positive cash flow, while still having costs associated. This resulting business architecture has driving participants away from the technology, jeopardizing its operations, and defying its progression. In order to cope with this issue, an alternative to provide miners' financial sustainability is to join a mining pool, which main purpose is to mitigate this cash flow sparsity by sharing the (more-recurrent) rewards obtained by the group. Therefore, in this work, we propose a reward sharing methodology for mining pools based on the Nucleolus of a stochastic cooperative game. A risk-averse value functional based on the Conditional Value-at-Risk (CVaR) is used to characterize the game's certainty equivalent. Two numerical experiments were conducted in this work: (i) one based on a small, illustrative network; and (ii) one derived from real data of the Bitcoin-refunded Blockchain network. The focus of the experiments is on the incremental value of the proposed methodology over using intuitive allocations (uniform and based on computational power) and in what extent the relative increase in the mining likelihood by playing as a group benefits the pool stability. Finally, we discuss and numerically analyze a nested procedure based on the proposed Nucleolus-based allocation seeking for higher "fairness" in sharing the pool rewards.

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue

<div>This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the related user-specific comfort constraints make it difficult to properly represent flexibility requirements and to monetize constraint violations.</div><div>To address these issues, we propose a new risk-aware probabilistic enforcement of flexibility constraints using the conditional-value-at-risk (CVaR). Next, an extended version of the model is introduced to mitigate the distributional ambiguity faced by the community manager when new sites with limited information are embedded in the portfolio. This is achieved by defining the worst-case CVaR based-constraint (WCVaR-BC) that differentiates the CVaR value among different sub-clusters of clients.</div><div>Both reformulations are linear, thus allowing to tackle large-scale stochastic problems. The proposed risk-based constraints are then trained and evaluated on real data collected from several industrial sites. Our findings indicate that using the WCVaR-BC leads to systematically higher out-of-sample reliability, while decreasing the exposure to extreme outcomes.</div>


2011 ◽  
Vol 204-210 ◽  
pp. 537-540
Author(s):  
Yu Ling Wang ◽  
Jun Hai Ma ◽  
Yu Hua Xu

Mean-variance model, value at risk and Conditional Value at Risk are three chief methods to measure financial risk recently. The demonstrative research shows that three optional questions are equivalence when the security rates have a multivariate normal distribution and the given confidence level is more than a special value. Applications to real data provide empirical support to this methodology. This result has provided new methods for us about further research of risk portfolios.


2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Leiyan Xu ◽  
Zhiqing Meng ◽  
Gengui Zhou ◽  
Yunzhi Mu ◽  
Minchao Zheng

Direct chain enterprises (DCEs) face a decision-making issue as to how to allocate and supply their products to their stores for sales with the minimum losses and maximum profits for the manufacturers. This paper presents a single-cycle optimal allocation model for DCEs under the given total production amount and conditional value at risk loss. The optimal strategy for production allocation and supply is derived. Subsequently, an approximate algorithm for solving the optimal total production amount is presented. The optimal allocation and supply strategy, the minimum total production amount, the minimum allocation strategy, and the discount pricing strategy are obtained for the single cycle. Finally, with the sales data of a food DCE, numerical results corroborate that adopting different production and supply strategies reduces the risk of expected losses and increases the expected return. It is of an important theoretical significance in guiding the production and operation of direct chain enterprises.


2008 ◽  
Vol 11 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Carlos Jabbour ◽  
Javier Peña ◽  
Juan Vera ◽  
Luis Zuluaga

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue

<div>This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the related user-specific comfort constraints make it difficult to properly represent flexibility requirements and to monetize constraint violations.</div><div>To address these issues, we propose a new risk-aware probabilistic enforcement of flexibility constraints using the conditional-value-at-risk (CVaR). Next, an extended version of the model is introduced to mitigate the distributional ambiguity faced by the community manager when new sites with limited information are embedded in the portfolio. This is achieved by defining the worst-case CVaR based-constraint (WCVaR-BC) that differentiates the CVaR value among different sub-clusters of clients.</div><div>Both reformulations are linear, thus allowing to tackle large-scale stochastic problems. The proposed risk-based constraints are then trained and evaluated on real data collected from several industrial sites. Our findings indicate that using the WCVaR-BC leads to systematically higher out-of-sample reliability, while decreasing the exposure to extreme outcomes.</div>


2017 ◽  
Vol 5 (2) ◽  
pp. 163-175
Author(s):  
Qingye Zhang ◽  
Yan Gao

Abstract Asset allocation is an important issue in finance, and both risk and return are its fundamental ingredients. Rather than the return, the measure of the risk is complicated and of controversy. In this paper, we propose an appropriate risk measure which is precisely a convex combination of mean semi-deviation and conditional value-at-risk. Based on this risk measure, investors can trade-off flexibly between the volatility and the loss to tackle the incurring risk by choosing different convex coefficients. As the presented risk measure contains nonsmooth term, the asset allocation model based on it is nonsmooth. To employ traditional gradient algorithms, we develop a uniform smooth approximation of the plus function and convert the model into a smooth one. Finally, an illustrative empirical study is given. The results indicate that investors can control risk efficiently by adjusting the convex coefficient and the confidence level simultaneously according to their perceptions. Moreover, the effectiveness of the smoothing function proposed in the paper is verified.


Author(s):  
Rong Jiang ◽  
Xueping Hu ◽  
Keming Yu

Abstract This article develops a single-index approach for modeling the expectile-based value at risk (EVaR). EVaR has an advantage over the conventional quantile-based VaR (QVaR) of being more sensitive to the magnitude of extreme losses. EVaR can also be used for calculating QVaR and expected shortfall (ES) by exploiting the one-to-one mapping from expectiles to quantiles and the relationship between VaR and ES. We develop an asymmetric least squares technique for estimating the unknown regression parameter and link function in a single-index model, and establish the asymptotic normality of the resultant estimators. Simulation studies and real data applications are conducted to illustrate the finite sample performance of the proposed methods.


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

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