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风险值(Value-at-Risk,VaR)是现今衡量风险的标准。本文利用风险值(VaR)方法来为风险基础资本估计风险,使其能够准确地呈现保险人本身所面临风险的状况,并利于监督机关建立适当的监督预警措施,来保障全体保险人权益并维持金融秩序的稳定。考虑到多尺度变换对估计报酬率风险型态模型无需作假设的优点,且小波变换是一种重要的多尺度分析工具,本文引入小波变换来对非线性的保险数据序列中提取频率域的高频信息,利用多尺度分解的系数得到模型参数,从而实现更加准确的风险值估计。
Value-at-Risk (VaR) is the standard for measuring risk today. In this paper, VaR method is used to estimate risk for risk basic capital so that it can accurately present the insurer’s risk and help supervisory authority to establish appropriate supervision and early warning measures to protect the rights and interests of all insurers. Financial stability. Considering that the multi-scale transform does not need to make assumptions on the model of estimated return rate risk, and wavelet transform is an important tool for multi-scale analysis. In this paper, wavelet transform is introduced to extract high frequency domain from nonlinear insurance data sequence Frequency information, using multi-scale factor decomposition model parameters, in order to achieve a more accurate risk value estimation.