基于神经网络模型与VAR的中国银行汇率风险管理优化研究
发布时间：2018-07-23 15:15
【摘要】：本文在对神经网络模型与VAR方法的应用及实现进行详细介绍的基础上,讨论了应用神经网络模型与VAR对中国银行汇率风险管理进行优化的可行性,形成一套具有一定实际意义的风险管理优化方案。 本文首先对文中所采用的两个风险管理模型进行了介绍。在对神经网络模型进行介绍时,选取了2009年12月31日至2013年3月1日共760个人民币对美元中间价作为样本,利用MATLAB软件建立神经网络模型,通过建立模型、训练模型、测试模型等三个步骤实现神经网络模型对外汇中间价的预测,获得的测试结果与实际数据基本吻合,证明了神经网络模型在汇率预测方面具有较高的准确度。在对VAR方法进行介绍时,选取2009年12月31日至2013年1月4日共724个交易日人民币对美元外汇中间价作为实证样本,采用报酬率计算公式为预测公式,获得资产组合价值分布图,最后以99%的置信区间获得VAR数值,经过以上四个步骤实现VAR的测算。 在完成对神经网络模型与VAR实现的介绍基础上,从风险管理组织架构、外汇交易市场风险管理、外汇交易操作风险管理等三个角度,对中国银行现行的外汇风险管理系统进行详细分析,并利用神经网络模型与VAR从以上三个角度对中国银行风险管理进行适用性分析,得出神经网络模型与VAR对中国银行风险管理的优化具有适用性的结论。在以上评价的基础上,将神经网络模型与VAR与中国银行风险管理现状相结合,从中国银行风险管理组织架构、市场风险管理、操作风险管理三个方面进行优化,与原有风险管理管理系统进行对比,优化后的风险管理系统有助于提高中国银行的外汇风险管理水平,具有良好的应用潜能。
[Abstract]:Based on the detailed introduction of the application and implementation of neural network model and VAR method, this paper discusses the feasibility of optimizing the exchange rate risk management of Bank of China by using neural network model and VAR. To form a set of practical significance of risk management optimization scheme. Firstly, two risk management models are introduced in this paper. In the course of introducing the neural network model, 760 RMB / US dollar median prices from December 31, 2009 to March 1, 2013 are selected as samples. The neural network model is established by using MATLAB software, and the training model is established through the establishment of the neural network model. The neural network model is used to predict the intermediate value of foreign exchange rate. The test results are in good agreement with the actual data, which proves that the neural network model has a high accuracy in the prediction of exchange rate. When introducing the VAR method, 724 trading days from December 31, 2009 to January 4, 2013, are selected as the empirical samples, and the return formula is used as the forecast formula to obtain the portfolio value distribution map. Finally, the VAR value is obtained by 99% confidence interval, and the VAR is calculated through the above four steps. On the basis of the introduction of neural network model and VAR implementation, from three angles of risk management organization structure, foreign exchange trading market risk management, foreign exchange trading operation risk management, The current foreign exchange risk management system of Bank of China is analyzed in detail, and the applicability of risk management of Bank of China is analyzed from the above three angles by using neural network model and VAR. It is concluded that the neural network model and VAR are applicable to the optimization of Chinese bank risk management. On the basis of the above evaluation, combining the neural network model with VAR and the current situation of risk management of Bank of China, the paper optimizes the risk management of Bank of China from three aspects: organizational structure, market risk management and operational risk management. Compared with the original risk management system, the optimized risk management system is helpful to improve the level of foreign exchange risk management of Bank of China, and has good application potential.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP183;F832.33
[Abstract]:Based on the detailed introduction of the application and implementation of neural network model and VAR method, this paper discusses the feasibility of optimizing the exchange rate risk management of Bank of China by using neural network model and VAR. To form a set of practical significance of risk management optimization scheme. Firstly, two risk management models are introduced in this paper. In the course of introducing the neural network model, 760 RMB / US dollar median prices from December 31, 2009 to March 1, 2013 are selected as samples. The neural network model is established by using MATLAB software, and the training model is established through the establishment of the neural network model. The neural network model is used to predict the intermediate value of foreign exchange rate. The test results are in good agreement with the actual data, which proves that the neural network model has a high accuracy in the prediction of exchange rate. When introducing the VAR method, 724 trading days from December 31, 2009 to January 4, 2013, are selected as the empirical samples, and the return formula is used as the forecast formula to obtain the portfolio value distribution map. Finally, the VAR value is obtained by 99% confidence interval, and the VAR is calculated through the above four steps. On the basis of the introduction of neural network model and VAR implementation, from three angles of risk management organization structure, foreign exchange trading market risk management, foreign exchange trading operation risk management, The current foreign exchange risk management system of Bank of China is analyzed in detail, and the applicability of risk management of Bank of China is analyzed from the above three angles by using neural network model and VAR. It is concluded that the neural network model and VAR are applicable to the optimization of Chinese bank risk management. On the basis of the above evaluation, combining the neural network model with VAR and the current situation of risk management of Bank of China, the paper optimizes the risk management of Bank of China from three aspects: organizational structure, market risk management and operational risk management. Compared with the original risk management system, the optimized risk management system is helpful to improve the level of foreign exchange risk management of Bank of China, and has good application potential.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP183;F832.33
【参考文献】
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