通过投资组合分散优化银行资源配置和降低风险

A. Mohammadi, Mehrzad Minnoei, Zadollah Fathi, Mohamamd Ali Keramati, Hossein Baktiari
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引用次数: 1

摘要

所有经济公司最关心的是如何在不同的经济部门中配置和配置资源,以实现利润最大化和风险最小化。分权是降低投资风险的重要因素之一。投资者通过仔细规划和收集充分的经济形势信息,分析各行业的形势,计划进行投资。作为一个经济企业,银行寻求短期和长期投资的贷款类型,如保证金、公民参与、奖励等,以保证其资本的回报。本文考虑银行作为经济企业的情况,提出了一种既能增加利润又能降低风险的模型。定义了两个目标函数,第一个目标是风险最小化,第二个目标函数是银行利润最大化,并采用鲁棒规划和Malvi Sim模型。本文从基于场景的模型求解出发,利用粒子群算法和遗传优化算法,研究了银行资产的风险组合和非风险组合以及最优组合。粒子群算法(PSA)基于SPP-CVAR方法估计的各级置信度和最优风险值均小于遗传算法,表明粒子群算法(PSA)的性能优于遗传算法(GA)。同时,从PSA解中得到的最优财富在各级置信度上都高于遗传算法(Genetic Algorithm, GA)的对应值,这也是证实PSO算法相对于遗传算法(Genetic Algorithm, GA)性能的另一个原因。PSO算法得到的第一个目标函数在所有置信水平下的值都低于遗传算法。该算法获得的最优财富高于遗传算法。在0.9水平下,SPP-CVAR方法的kupiec统计量的LR值小于卡方统计量(临界值),假设可以接受。
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Optimal allocation of bank resources and risk reduction through portfolio decentralization
The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, which guarantees the return of their capital. In this paper, considering the condition of a bank as an economic enterprise, a model is presented which not only increases profit but also reduces risk. Two objective functions have been defined that the first objective is to minimize the risk and the second objective function is to maximize the of the bank profit, which is used by robust programming and Malvi Sim model. In this paper, we have investigated the Risky and non-Risky Partfolio and the optimal portfolio of bank assets from scenario based solution of the model and by using PSO and Genetic Optimization Algorithm. At all levels of confidence and optimal values of risk based on the estimation of SPP-CVAR method by Particle Swarm Algorithm (PSA) is less than genetic algorithm, which indicates better performance of Particle Swarm Algorithm (PSA) than Genetic Algorithm (GA). Also, the optimum wealth obtained from PSA solution is higher at all levels of confidence than the corresponding value of Genetic Algorithm (GA), and this is another reason to confirm the performance of PSO algorithm compared to the Genetic Algorithm (GA). The values of the first goal function, obtained from the PSO algorithm, for all confidence levels are lower than those of the genetic algorithm. The optimum wealth obtained from PSA is higher than genetic algorithm. At 0.9 level, the value of LR of kupiec statistics for the SPP-CVAR method was less than the Chi-square statistics (Critical value) which was assumed to be acceptable.
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