基于MES频谱数据异常贡献度估计与后验分析

Estimation of anomaly contribution in spectrum data based on MES and backtesting

摘要:

近些年，基于大数据分析模型的风险度量和控制方法研究变得越来越重要，而风险度量模型的后验分析研究能够保障和检验所用分析技术在实际数据分析中的有效性。边际期望损失(MES)作为度量个体对系统性风险的边际贡献的重要工具，其后验分析也是一个值得关注的问题。本文将C. Acerbi等提出的关于ES的后验分析方法进行二维变量下的延伸，提出2个新的对于MES的统计量。模拟实验的结果表明，在原假设分布和备择假设分布相差相对较小的情况下，2个统计量的统计功效均大于D. Banulescu等采用的统计量。实证分析的结果也表明，对于同样的预测结果，文中新提出的统计量在原假设的接受程度上相对更为谨慎。该方法对于大数据模型算法的后验分析具有一定的理论借鉴意义。

Abstract:

In recent years, the research on risk measurement and control methods based on big data analysis model has become more and more important, and the backtesting analysis on risk measurement tools can guarantee the effectiveness of the techniques used in actual data analysis. Marginal Expected Shortfall(MES) is an important tool to measure the marginal contribution of individual institutions to systemic risk, and the backtest methodologies for MES is also worthy to focus on. In this paper, the backtest method of ES in C. Acerb et al. is extended to the two-dimensional case and two backtest methodologies are proposed for MES. The results of simulation show that these two new statistics are more powerful than the statistics used in D.Banulescu et al. under situations that the difference between the null hypothesis and the alternative hypothesis is relatively small. The results of empirical analysis also support that these two new statistics proposed in this paper accept the null hypothesis more cautiously under the same prediction model. This method can give some reference for model algorithm backtesing under big data.