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Open Access Article
Advances in International Finance. 2021; 3: (1) ; 1-15 ; DOI: 10.12208/j.aif.20210001.
深圳技术大学商学院 广东深圳
易方达基金管理有限公司 广东广州
上海商学院财务金融学院 上海
*通讯作者： 高翔,单位：上海商学院财务金融学院 上海；
国家自然科学基金“操作风险动态量化方法研究：从微观机构到宏观系统”
发布时间: 2021-05-10 总浏览量: 2303
PDF 全文下载 引用本文 收录截图（CNKI-Scholar）
将广义自回归条件异方差模型 (generalized autoregressive conditional heteroskedasticity model, 简称GARCH) 与混频数据抽样模型 (mixed data sampling, 简称MIDAS) 相结合构建单因子、三因子GARCH-MIDAS模型来预测波动率并计算波动率信息比率，通过Logit模型和1996至2019年中国股票市场数据，验证了波动率信息比率指标与中国股票市场崩盘风险之间的关系。结果表明：中国股票市场日度波动率服从 GARCH(1,1) 过程且与宏观经济变量水平值及波动率显著相关，波动率信息因子与国内股票市场崩盘风险显著负相关且较为稳健，三因子GARCH-MIDAS模型拟合能力优于单因子模型，包含波动率信息因子的Logit模型能有效预测国内股市崩盘风险。
This paper utilizes the single-factor and three-factor GARCH-MIDAS model to predict Chinese market volatility and calculate the volatility information ratio, which purports to measure investor overconfidence and irrational behaviors. The ratio’s predictivity of market crash is investigated with a Logit specification with Chinese stock market index data from 1996-2019. The empirical results indicate that the daily volatility of Chinese stock market follows a GARCH(1,1) process driven by the level and fluctuation of macroeconomic indicators. There exists a significantly negative and robust relationship between our information ratio and market crash risk in China. Moreover, the three-factor GARCH-MIDAS model outperforms the single-factor one in forecasting the crash risk in China’s stock market when our proposed volatility information ratio is incorporated.
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