基于W-G-VaR模型的股票市场风险测度

doi:10.6040/j.issn.1671-9352.0.2023.322

摘要/Abstract

摘要： 为了验证基于不同频域尺度捕捉金融时间序列的概率分布不确定性特征可以有效提高VaR模型的度量精度,首次将小波多分辨率分析与非线性期望理论相结合构建W-G-VaR模型,选择美国标准普尔500指数(Standard & Poors 500 composite stock price index, S& P 500 Index)与上证综合指数作为样本进行实证分析。结果表明,相比于G-VaR模型,从时域和频域双视角下构建的W-G-VaR模型在整个样本期间,尤其在重大风险发生期间具有更精确的风险度量结果,且捕捉不确定性时的窗口大小不会影响W-G-VaR模型的优越性。

关键词: 小波多分辨率分析, 非线性期望理论, W-G-VaR模型, 尾部风险

Abstract: In order to verify that capturing the uncertainty features of the probability distribution of financial time series based on different frequency domain scales can effectively improve the measurement accuracy of VaR model, the W-G-VaR model is constructed by combining wavelet multi-resolution analysis with nonlinear expectation theory for the first time. Standard & Poors 500 composite stock price index(S& P 500 Index)and Shanghai(securities)composite index are selected as samples for empirical analysis. The results show that, compared to the G-VaR model, the W-G-VaR model constructed from the dual perspectives of the time domain and the frequency domain has more accurate risk measurement results throughout the sample period, especially during the occurrence of major risks, and the size of the window when capturing uncertainties does not affect the superiority of the W-G-VaR model.

Key words: wavelet multi-resolution analysis, nonlinear expectation theory, W-G-VaR model, tail risk

中图分类号:

O211.9

张慧,魏佳琪,孟纹羽,朱庆峰. 基于W-G-VaR模型的股票市场风险测度[J]. 《山东大学学报(理学版)》, 2025, 60(8): 21-33.

ZHANG Hui, WEI Jiaqi, MENG Wenyu, ZHU Qingfeng. Stock market risk measurement based on W-G-VaR model[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2025, 60(8): 21-33.

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