能源金融风险预警模型研究——基于GABP算法  

Study on Pre-warning Model of Energy Financial Risk

作　　者：王新霞[1] 林晓霞[2] 黄显林[3] 

机构地区：[1]西安外国语大学,陕西西安710128 [2]武汉理工大学,湖北武汉430070 [3]中国人民银行南宁中心支行,广西南宁530028

出　　处：《区域金融研究》2016年第7期24-28,共5页Journal of Regional Financial Research

基　　金：陕西省教育厅科研计划项目资助(项目编号:15JK1605)

摘　　要：立足我国能源金融的发展特点,本文设计了涵盖能源金融风险系统特征的预警指标体系,基于遗传算法优化的BP神经网络法,建立了更完善、适应性更强的能源金融风险预警模型。该模型对预警指标权重的赋值比较合理,预警指标临界值的划分有效,预警结果准确度较高,计算所得2015年我国不同能源行业的金融风险水平符合实际情况。本文所构建的能源金融风险预警模型可为政府及相关管理部门提供风险识别与风险预警的信息。Based on the characteristics of the development of China＇ s energy finance, this paper designs the pre- warning index system which covers the system characteristics of energy financial risk and establishes a more perfect, more adaptive pre-warning model of energy financial risk with BP neural network method optimized by genetic algo- rithm. In the model, the weights of pre-warning index are more reasonable, the critical values of pre-warning index are effective, and the pre-warning results are accurate. The estimated financial risk level of different energy sectors in 2015 conforms to the actual situation. The pre-warning model of energy financial risk in this paper can provide the in- formation of risk identification and pre-warning for the government and the relevant administrative departments.

关 键 词：能源金融 风险预警 遗传算法 神经网络 

分 类 号：F831.5[经济管理—金融学]