2019
DOI: 10.1080/00036846.2019.1613507
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Economic policy uncertainty and the Chinese stock market volatility: new evidence

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Cited by 56 publications
(35 citation statements)
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“…Furthermore, Wang et al [19] employed a GARCH-MIDAS model with skew Student's t-distribution to examine the impact of domestic and foreign EPU on the volatility of China's financial stocks and found that the EPU index has a negative impact on the volatility of China's financial stocks. Recently, Li et al [20] used a predictive regression approach to investigate the impact of EPU on Chinese stock market volatility and provided evidence that the EPU index has a significantly negative impact on future volatility of Chinese stock market. e predictive regression model adopted by Li et al [20] requires the frequency of EPU to match that of predicted (realized) volatility.…”
Section: Introductionmentioning
confidence: 99%
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“…Furthermore, Wang et al [19] employed a GARCH-MIDAS model with skew Student's t-distribution to examine the impact of domestic and foreign EPU on the volatility of China's financial stocks and found that the EPU index has a negative impact on the volatility of China's financial stocks. Recently, Li et al [20] used a predictive regression approach to investigate the impact of EPU on Chinese stock market volatility and provided evidence that the EPU index has a significantly negative impact on future volatility of Chinese stock market. e predictive regression model adopted by Li et al [20] requires the frequency of EPU to match that of predicted (realized) volatility.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, Li et al [20] used a predictive regression approach to investigate the impact of EPU on Chinese stock market volatility and provided evidence that the EPU index has a significantly negative impact on future volatility of Chinese stock market. e predictive regression model adopted by Li et al [20] requires the frequency of EPU to match that of predicted (realized) volatility. As a result, Li et al [20] employed the monthly data to perform the analysis, which may ignore important structural features of stock market data and constrain the choice of forecast horizons (i.e., one month).…”
Section: Introductionmentioning
confidence: 99%
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“…In addition, note that the bivariate approach also allows us to measure investors' uncertainty, derived from conditional volatility of sentiment, simultaneously with the volatility of stock markets. In this regard, our study is also different from the few studies that exist associated with in-sample and out-of-sample predictability of stock market volatility based on primarily the new papers-based measure of economic uncertainty, derived from outside the econometric model associated with volatility (e.g., see Liu and Zhang (2015), Su et al (2017Su et al ( , 2019, Fang et al (2018), and Li et al (2019)). Modeling joint dynamics is important, given the findings of recent studies (e.g., see Mumtaz and Theodoridis (2020) and Ludvigson, Ma, and Ng (2015)), which indicates that uncertainty is in fact endogenous rather than exogenous.…”
Section: Introductionmentioning
confidence: 79%
“…Finally, they declare that EPU is more negatively influential to small companies comparing to enterprises. [5] Similarly, Deng Meiwei also says that the stronger the company is, the less negative influence it receives. [6] In addition, Caldara et al claim that EPU has negative influences on investment and trading activities byThree measures of TPU are constructed using newspaper coverage, firms' earnings calls, and tariff rates.…”
Section: Mainstream Views and Analysismentioning
confidence: 99%