Economic uncertainty and stock market asymmetric volatility: analysis based on the asymmetric GARCH-MIDAS model

Zaifeng Wang, Tiancai Xing, Xiao Wang
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Abstract

PurposeWe aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.Design/methodology/approachWe construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.FindingsNegative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.Research limitations/implicationsMany GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.Practical implicationsOur conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.Social implicationsFirst, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.Originality/valueThis study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.
经济不确定性与股市非对称波动性:基于非对称 GARCH-MIDAS 模型的分析
目的我们旨在阐明经济不确定性对中国股市波动的影响。我们扩展了对经济不确定性与股市风险之间非对称关联性的理解,并提供了经济不确定性对上行风险和下行风险溢出效应的不同特征。设计/方法/方法我们利用因子增强变量自回归随机波动率(FAVAR-SV)模型,构建了高维数据的中国经济不确定性指数。然后,我们通过非对称广义自回归条件异方差-混合数据抽样(GARCH-MIDAS)模型研究了已实现波动率和经济不确定性对股市长期波动率成分的非对称影响。研究结果与正面消息相比,负面消息(包括负收益相关波动率和较高的经济不确定性)对长期波动率成分的影响更大。在 2008 年金融危机期间,经济不确定性和已实现波动率对长期波动率成分有显著影响,但在 2015 年 A 股股灾和 2020 年 COVID-19 大流行期间,经济不确定性和已实现波动率并不构成长期波动率成分。双因子非对称 GARCH-MIDAS 模型在长期波动率成分的解释力、拟合能力和样本外预测能力方面均优于其他两个模型。研究局限/意义许多 GARCH 序列模型也可以将 GARCH 序列模型与 MIDAS 方法相结合,包括但不限于指数 GARCH(EGARCH)和阈值 GARCH(TGARCH)。这些不同的模型可能会对经济不确定性做出不同的反应。我们的结论对包括政策制定者、市场监管者和投资者在内的利益相关者促进市场稳定具有重要意义。社会意义首先,面对国内外的不确定性和挑战,政策制定者必须加强与市场的沟通,提高政策透明度,有效引导市场预期。第二,股市管理部门应完善资本市场基础监管制度,优化投资者结构。原创性/价值本研究提供了一个新颖的视角,将高维 FAVAR-SV 模型构建的中国经济不确定性指数纳入非对称 GARCH-MIDAS 模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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