国家风险是经济活动主体在国际业务中所面临的来自其他国家的风险, 深入研究其内在特征, 对于理解和把握国家风险的动态演化规律有着重要意义。鉴于国家风险复杂易变的特点, 本文提出了一种基于"分解重构"思想的多尺度特征提取与识别的研究框架, 利用Ensemble EMD方法将原始国家风险值分解到短期、中期和长期三个时间尺度上, 引入方差贡献率、相关系数和Shapley值刻画各尺度与原始国家风险序列间的波动特征、模态特征以及全局重要度。以12个OPEC石油输出国为样本, 实证结果发现:利用各尺度的模态特征和波动特征可以很好地实现样本国国家风险的分类管理, 且分类具有较好的一致性;由Shapley值获得不同尺度的全局重要度, 对于全部样本国呈现出一致且稳定的内在固有特征, 即短期、中期和长期三尺度对国家风险的"贡献度"约为1:1:3。这不仅能够为国家风险管理提供了更为丰富的动态特征信息, 而且对于更为全面的国家风险特征识别、监测与预测提供了一种新的研究方法。
Country risk is one kind of special risk which comes from other countries when taking part in international activities, and it is of great importance to understand the law of its dynamic features. In this paper, considering the complexity and mutability of country risk, a novel research framework which concentrates on multi-scale feature extraction and identification is proposed based on the thought of "decomposition and integration". Firstly, country risk data is decomposed to several intrinsic mode functions. Then these intrinsic mode functions are reconstructed to three components of different scales, which represent high frequency scale, low frequency scale and trend respectively. Furthermore, variance contribution rate, correlation coefficient and the Shapley value are introduced to depict features of dynamic fluctuation, correlations between different scales and original data and the global importance of different scales respectively. At last, empirical experiments are given by selecting OPEC countries as cases. It can be found that modes correlation and variance contribution features can effectively classify the sample countries, and the classified results using the two different features have consistency.The global importance of high frequency scale, low frequency scale and the trend is 1:1:3, that is, global importance, which can be obtained by the Shapley value, is coherence and stable between all the countries. Above all, the framework can not only offer more dynamic information of the country risk, but also can be regarded as a new method for the comprehensive identification, monitory and prediction to the country risk.
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