产出不确定条件下“公司+农户”型订单农业供应链协商模型研究

在农产品产出不确定性及零售价格受农产品产出率影响的条件下,研究了一类由风险规避农户和风险中性公司组成“公司+农户”型订单农业农产品供应链协调问题。在该农产品供应链中,农户和公司通过Nash协商谈判来分别决策最优的生产量和订单价格。研究结果表明,在农产品产出不确定及零售市场价格受农产品产出率影响的条件下,风险规避型农户和公司的Nash协商合作博弈存在均衡解。Nash协商谈判所达成的最优农产品产出量和订单价格均高于分散决策情形下的最优农产品产出量和订单价格。最优农产品产出量是关于农户风险规避度的单调增函数,而最优的订单价格是关于农户风险规避度的单调减函数。最后,通过与分散决策情形相比,证明了Nash协商谈判机制能够促使风险规避型农户和风险中性型公司均达到帕累托改进。     

基于偏态分布的风险度量计算

金融时间序列具有尖峰厚尾性,同时在股市中又存在着杠杆效应.对股票指数收盘价格的对数收益率序列建立ARMA-APARCH模型,在对数收益率序列分别满足Skewed-t分布和Skewed-GED的假设下,给出了在险价值及期望损失的计算方法.对t分布与Skewed-t分布、GED与Skewed-GED分别进行对比性实证分析,结果表明,在两个偏态分布假设下计算得到的期望损失估计结果更为保守,更能够捕捉到股市的尾部风险.     

Manufacturer’s return policy in a two-stage supply chain with two risk-averse retailers and random demand

This paper studies the manufacturer’s return policy and the retailers’ decisions in a supply chain consisting of one manufacturer and two risk-averse retailers under a single-period setting with price-sensitive random demand. We characterize each retailer’s risk-embedded objective via conditional value-at-risk, and construct manufacturer-Stackelberg games with and without horizontal price competition between the retailers. We explore, through numerical studies, the effects of the retailers’ aversion to risk and other parameters on the manufacturer’s return policy and profit and the retailers’ decisions. We further investigate the effect of distribution asymmetry by comparing the results with normal and lognormal demand.     

Value-at-Risk with quantile regression neural network: New evidence from internet finance firms

Traditional risk measurements have proven inadequate in capturing tail risk and nonlinear correlation. This study proposes a novel approach to measure financial risk in the Internet finance industry: a new Value-at-Risk (VaR) measurement based on quantile regression neural network (QRNN). Sparrow Search Algorithm (SSA) is utilized to optimize the QRNN model, which improves the model's performance in predicting internet finance risk. By comparing the TGARCH-VaR and QR-VaR approaches, our study demonstrates the effectiveness of the QRNN-VaR approach and its potential to improve the accuracy of risk prediction in the Internet finance industry. This study further examines and compares the risks between the traditional and internet finance industries. It also considers the unique impact of COVID-19 on industry risk based on statistical testing for differences and machine learning models. Our results indicate that the level of risk in the Internet finance industry is higher than in the traditional finance industry. Moreover, COVID-19 has contributed to increased risk within the Internet finance industry. These findings have significant implications for investors and policymakers seeking to better understand and manage risks within the Internet finance industry, particularly in the ongoing COVID-19 pandemic.     

A firm's optimizing behaviour under a value-at-risk constraint

In this article, we employ the concept of value-at-risk to model a kind of risk-averse behaviour of a firm which seeks to maximize profit?à?la Greenwald–Stiglitz [5]. It is shown that there exists a unique well-defined solution function, which relates output to the firm's net worth, but that this function is not monotone. The latter is due to the fact that whenever the VaR-constraint is not binding, the firm behaves in a risk-neutral fashion. It is also shown that in this context, the Modigliani–Miller theorem applies only in the special case where there is no risk of bankruptcy.     

Computational aspects of minimizing conditional value-at-risk

We consider optimization problems for minimizing conditional value-at-risk (CVaR) from a computational point of view, with an emphasis on financial applications. As a general solution approach, we suggest to reformulate these CVaR optimization problems as two-stage recourse problems of stochastic programming. Specializing the L-shaped method leads to a new algorithm for minimizing conditional value-at-risk. We implemented the algorithm as the solver CVaRMin. For illustrating the performance of this algorithm, we present some comparative computational results with two kinds of test problems. Firstly, we consider portfolio optimization problems with 5 random variables. Such problems involving conditional value at risk play an important role in financial risk management. Therefore, besides testing the performance of the proposed algorithm, we also present computational results of interest in finance. Secondly, with the explicit aim of testing algorithm performance, we also present comparative computational results with randomly generated test problems involving 50 random variables. In all our tests, the experimental solver, based on the new approach, outperformed by at least one order of magnitude all general-purpose solvers, with an accuracy of solution being in the same range as that with the LP solvers. János Mayer: Financial support by the national center of competence in research "Financial Valuation and Risk Management" is gratefully acknowledged. The national centers in research are managed by the Swiss National Science Foundation on behalf of the federal authorities.     

A smoothing method for solving portfolio optimization with CVaR and applications in allocation of generation asset

This paper focuses on the computation issue of portfolio optimization with scenario-based CVaR. According to the semismoothness of the studied models, a smoothing technology is considered, and a smoothing SQP algorithm then is presented. The global convergence of the algorithm is established. Numerical examples arising from the allocation of generation assets in power markets are done. The computation efficiency between the proposed method and the linear programming (LP) method is compared. Numerical results show that the performance of the new approach is very good. The remarkable characteristic of the new method is threefold. First, the dimension of smoothing models for portfolio optimization with scenario-based CVaR is low and is independent of the number of samples. Second, the smoothing models retain the convexity of original portfolio optimization problems. Third, the complicated smoothing model that maximizes the profit under the CVaR constraint can be reduced to an ordinary optimization model equivalently. All of these show the advantage of the new method to improve the computation efficiency for solving portfolio optimization problems with CVaR measure.     

VaR测度下的风险对冲策略研究

本文分别在正态分布和任意分布设定下讨论最小在险价值(VaR)的风险对冲问题。在正态分布设定下,本文深入讨论最小方差对冲比率和最小VaR对冲比率的性质,并得出最小VaR对冲策略下组合收益率的均值和方差大于最小方差策略下组合收益率的均值和方差。在任意分布设定下,本文构建一种新的VaR对冲模型,该模型引入非参数核估计方法对VaR进行估计,然后基于VaR核估计量建立风险对冲问题,实现风险估计与风险对冲同步进行。实证结果非常稳健地表明,不做任何分布假设下的核估计法得到的风险对冲效果优于最小方差对冲策略和正态分布设定下的最小VaR对冲策略。     

基于CVaR准则的单制造商两零售商供应链决策模型与协调

针对单制造商和两零售商组成的供应链,假定产品存在次品需返修、产品需求为随机的且受到产品质量和零售商促销努力的影响,运用均衡分析方法建立了零售商为风险中性时的供应链集中式决策模型和分散式决策模型,给出了协调供应链的回购加促销补贴合同。然后基于条件风险值准则建立了零售商为风险厌恶时的供应链集中式决策模型和分散式决策模型,给出了协调该供应链的回购加促销补贴合同。最后的算例表明了模型的合理性和协调合同的有效性。研究表明:零售商越厌恶风险,其订货量越少,制造商、零售商和供应链的收益都将下降;而产品正品率的提高有利于供应链各方;零售商为风险中性时的供应链决策是零售商为风险厌恶者的特例。     

Vector-valued multivariate conditional value-at-risk

In this study, we propose a new definition of multivariate conditional value-at-risk (MCVaR) as a set of vectors for arbitrary probability spaces. We explore the properties of the vector-valued MCVaR (VMCVaR) and show the advantages of VMCVaR over the existing definitions particularly for discrete random variables.