scholarly journals Study on Single Cycle Production Allocation and Supply Strategy for DCEs Based on the CVaR Criterion

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Leiyan Xu ◽  
Zhiqing Meng ◽  
Gengui Zhou ◽  
Yunzhi Mu ◽  
Minchao Zheng
Keyword(s):  
Value At Risk ◽  
Optimal Allocation ◽  
Approximate Algorithm ◽  
Conditional Value At Risk ◽  
Total Production ◽  
Single Cycle ◽  
Expected Return ◽  
Allocation Model ◽  
Production Allocation ◽  
The Given

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.

EFFECTS OF MULTIPLE CRITERIA ON PORTFOLIO OPTIMIZATION

2014 ◽  
Vol 13 (01) ◽  
pp. 77-99 ◽  
Author(s):  
TUNCER ŞAKAR CEREN ◽  
MURAT KÖKSALAN
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Decision Maker ◽  
Value At Risk ◽  
Efficient Frontier ◽  
Conditional Value At Risk ◽  
Computational Studies ◽  
Expected Return ◽  
Risk Criteria ◽  
Return Variance

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.

Novel Bidding Decision Model for Generation Company Based on CVaR Model

2013 ◽  
Vol 732-733 ◽  
pp. 1438-1443
Author(s):  
Pin Jie Xie ◽  
Jian Chao Hou ◽  
Quan Sheng Shi
Keyword(s):  
Value At Risk ◽  
Decision Model ◽  
Decision Models ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Decision Variables ◽  
Bidding Price ◽  
Generation Company ◽  
Optimal Bidding ◽  
Benefit And Risk

It is an urgent problem to solve for generation companies that how to find the optimal bidding strategy to obtain the highest profits and to decrease the risk to the lowest level. This paper presented a new bidding decision model with risk management for generation companies based on the conditional value at risk (CVaR). In the process of building the optimal bidding decision models, three situations are considered separately, including only consider maximize the expected return or the CVaR value of benefit, and considering the benefit and risk (CVaR). By this method, the generation companies can be determined the two decision variables of bidding price and bidding output to maximize its revenue at the same time to declare the risk.

scholarly journals The Case of “Less is More”: Modelling Risk-Preference with Expected Downside Risk

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Mihály Ormos ◽  
Dusán Timotity
Keyword(s):  
Value At Risk ◽  
Utility Theory ◽  
Risk Measure ◽  
Negative Relationship ◽  
Downside Risk ◽  
Psychological Explanation ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Linear Predictor ◽  
Less Is More

AbstractThis paper discusses an alternative explanation for the empirical findings contradicting the positive relationship between risk (variance) and reward (expected return). We show that these contradicting results might be due to the false definition of risk-perception, which we correct by introducing Expected Downside Risk (EDR). The EDR parameter, similar to the Expected Shortfall or Conditional Value-at-Risk, measures the tail risk, however, fits and better explains the utility perception of investors. Our results indicate that when using the EDR as risk measure, both the positive and negative relationship between expected return and risk can be derived under standard conditions (e. g. expected utility theory and positive risk-aversion). Therefore, no alternative psychological explanation or additional boundary condition on utility theory is required to explain the phenomenon. Furthermore, we show empirically that it is a more precise linear predictor of expected return than volatility, both for individual assets and portfolios.

scholarly journals A Bi-Objective Portfolio Optimization with Conditional Value-at-Risk

2010 ◽  
Vol 4 (2) ◽  
pp. 47-69 ◽  
Author(s):  
Bartosz Sawik
Keyword(s):  
Value At Risk ◽  
Stock Exchange ◽  
Risk Measure ◽  
Performance Measure ◽  
Conditional Value At Risk ◽  
Programming Approach ◽  
Expected Return ◽  
Worst Case ◽  
Warsaw Stock Exchange ◽  
The Relationship

This paper presents a bi-objective portfolio model with the expected return as a performance measure and the expected worst-case return as a risk measure. The problems are formulated as a bi-objective linear program. Numerical examples based on 1000, 3500 and 4020 historical daily input data from the Warsaw Stock Exchange are presented and selected computational results are provided. The computational experiments prove that the proposed linear programming approach provides the decision maker with a simple tool for evaluating the relationship between the expected and the worst-case portfolio return.

scholarly journals A New Portfolio Rebalancing Model with Transaction Costs

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Meihua Wang ◽  
Cheng Li ◽  
Honggang Xue ◽  
Fengmin Xu
Keyword(s):  
At Risk ◽  
Transaction Costs ◽  
Cost Function ◽  
Transaction Cost ◽  
Value At Risk ◽  
Financial Data ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Portfolio Rebalancing ◽  
Proposed Model

A portfolio rebalancing model with self-finance strategy and consideration of V-shaped transaction cost is presented in this paper. Our main contribution is that a new constraint is introduced to confirm that the rebalance necessity of the existing portfolio needs to be adjusted. The constraint is constructed by considering both the transaction amount and transaction cost without any additional supply to the investment amount. The V-shaped transaction cost function is used to calculate the transaction cost of the portfolio, and conditional value at risk (CVaR) is used to measure the risk of the portfolios. Computational tests on practical financial data show that the proposed model is effective and the rebalanced portfolio increases the expected return of the portfolio and reduces the CVaR risk of the portfolio.

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

2021 ◽  
Author(s):  
Eduardo Bolonhez ◽  
Thuener Silva ◽  
Bruno Fanzeres dos Santos
Keyword(s):  
Cash Flow ◽  
Value At Risk ◽  
Real Data ◽  
Relative Increase ◽  
Conditional Value At Risk ◽  
Allocation Model ◽  
Incremental Value ◽  
Quota Allocation ◽  
Positive Cash Flow ◽  
Value Functional

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.

scholarly journals Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy

2020 ◽  
Vol 34 (04) ◽  
pp. 4436-4443
Author(s):  
Ramtin Keramati ◽  
Christoph Dann ◽  
Alex Tamkin ◽  
Emma Brunskill
Keyword(s):  
Value At Risk ◽  
Conditional Value At Risk ◽  
Learning Approaches ◽  
High Stakes ◽  
Expected Return ◽  
Continuous State ◽  
Optimal Policies ◽  
Markov Decision ◽  
The Face ◽  
Efficient Learning

While maximizing expected return is the goal in most reinforcement learning approaches, risk-sensitive objectives such as conditional value at risk (CVaR) are more suitable for many high-stakes applications. However, relatively little is known about how to explore to quickly learn policies with good CVaR. In this paper, we present the first algorithm for sample-efficient learning of CVaR-optimal policies in Markov decision processes based on the optimism in the face of uncertainty principle. This method relies on a novel optimistic version of the distributional Bellman operator that moves probability mass from the lower to the upper tail of the return distribution. We prove asymptotic convergence and optimism of this operator for the tabular policy evaluation case. We further demonstrate that our algorithm finds CVaR-optimal policies substantially faster than existing baselines in several simulated environments with discrete and continuous state spaces.

scholarly journals Optimal Allocation Model of Virtual Power Plant Capacity considering Electric Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Shiping Geng ◽  
Caixia Tan ◽  
Dongxiao Niu ◽  
Xiaopeng Guo
Keyword(s):  
Electric Vehicles ◽  
Optimal Allocation ◽  
Clean Energy ◽  
Capacity Allocation ◽  
Shanxi Province ◽  
Conditional Value At Risk ◽  
Virtual Power ◽  
Allocation Model ◽  
Virtual Power Plants ◽  
Optimal Allocation Model

To push forward the development of electric vehicles while improving the economy and environment of virtual power plants (VPPs), research on the optimization of VPP capacity considering electric vehicles is carried out. In this paper, based on this, this paper first analyzes the framework of the VPP with electric vehicles and models each unit of the VPP. Secondly, the typical scenarios of wind power, photovoltaic, electric vehicle charging and discharging, and load are formed by the Monte Carlo method to reduce the output deviation of each unit. Then, taking the maximization of the net income and clean energy consumption of the VPP as the objective function, the capacity optimal allocation model of the VPP considering multiobjective is constructed, and the conditional value-at-risk (CVaR) is introduced to represent the investment uncertainty faced by the VPP. Finally, a VPP in a certain area of Shanxi Province is used to analyze a calculation example and solve it with CPLEX. The results of the calculation example show that, on the one hand, reasonable selection of the optimal scale of EV connected to the VPP is able to improve the economy and environment of the VPP. On the other hand, the introduction of CVaR is available for the improvement of the scientific nature of VPP capacity allocation decisions.

An estimation-free, robust conditional value-at-risk allocation model

2008 ◽  
Vol 11 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Carlos Jabbour ◽  
Javier Peña ◽  
Juan Vera ◽  
Luis Zuluaga
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Risk Allocation ◽  
Allocation Model
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