Strengthening Banks' Portfolio Against Asset Shocks: A Genetic Computational Approach

S. Gurciullo
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引用次数: 1

Abstract

This thesis investigates models of market risk assessment based on genetic algorithms, with specific reference to asset portfolio choice under volatile market conditions. It does so by developing computational simulations of asset portfolios, which are then subjected to stressful price events. A genetic algorithm functions as an optimising process, allowing portfolios to evolve towards a structure that is – on average – less fragile against asset shocks. The importance of this research is dictated by the grave outcomes of, for instance, the 2008 financial crisis: 371 commercial banks failed between 1/1/2008 and 1/7/2011 in the United States alone. Such events highlighted the need for the renovation of the financial risk framework supposed to at least partially shield banks from unexpected adverse events.A synthetic, computational model is constructed, where asset portfolios are structured so as to invest in four main asset categories: sovereign bonds, financial institutions bonds, corporate bonds and real estate. Each of the categories has an underlying non-normal probability distribution of prices, empirically derived by the literature. These are used to simulate volatile and adverse scenarios affecting the value of the portfolios. A genetic algorithm is designed to select, crossover and mutate, at each generation, the portfolios that best perform under the simulated conditions. After a number of generations, it is expected that one or more portfolios structures will be highlighted as the ones that best perform under adverse scenarios.The model is run three times with different sets of optimization constraints, each specifying the minimum relative proportion of portfolios to be dedicated to each asset category. All versions of the model indicate that the best performing portfolios structures under volatile conditions are the ones that are mainly composed by the asset category featuring less fat tails. The results of the model are checked for their robustness, by running versions with different sets of simulated scenarios and additional numbers of synthetic asset categories. Limitations of the design of the study are identified. The model lacks a simulation of the liability side of financial institutions, and its results are not tested on a systemic level, thus not shedding light on what consequences the indicated portfolio strategy for a single bank would have on the network of banks. Such issues will be addressed in future research.
加强银行资产组合抵御资产冲击:一种遗传计算方法
本文研究了基于遗传算法的市场风险评估模型,具体涉及波动市场条件下的资产组合选择。它通过开发资产组合的计算模拟来实现这一点,然后这些资产组合将受到压力价格事件的影响。遗传算法发挥着优化过程的作用,使投资组合朝着一种平均而言在面对资产冲击时不那么脆弱的结构发展。这项研究的重要性是由2008年金融危机的严重后果决定的,例如,2008年1月1日至2011年1月7日期间,仅在美国就有371家商业银行倒闭。这些事件突显出,有必要对金融风险框架进行改革,至少在一定程度上保护银行免受意外不利事件的影响。构建了一个综合的计算模型,构建了资产组合,以便投资于四种主要资产类别:主权债券、金融机构债券、公司债券和房地产。每一个类别都有一个潜在的非正态概率分布的价格,由文献经验推导。它们用于模拟影响投资组合价值的波动和不利情况。设计了一种遗传算法,在每一代中选择、交叉和变异在模拟条件下表现最佳的投资组合。经过几代之后,预计一个或多个投资组合结构将被突出显示为在不利情况下表现最佳的结构。该模型使用不同的优化约束集运行三次,每次都指定用于每种资产类别的投资组合的最小相对比例。该模型的所有版本都表明,在波动条件下,表现最好的投资组合结构是那些主要由肥尾较少的资产类别组成的组合。通过运行具有不同模拟场景集和额外数量的合成资产类别的版本,来检查模型的结果是否具有鲁棒性。确定了研究设计的局限性。该模型缺乏对金融机构负债方面的模拟,其结果没有在系统层面上进行测试,因此无法阐明单个银行的所示投资组合策略对银行网络的影响。这些问题将在未来的研究中得到解决。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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