Can the Chinese volatility index reflect investor sentiment?

Long, Wen; Zhao, Manyi; Tang, Ye-ran

doi:10.1016/j.irfa.2020.101612

Cited by 31 publications

(10 citation statements)

References 78 publications

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“…Especially during the COVID-19 pandemic, fear-and pessimism-induced investor sentiments were one of the major reasons that plummeted clean energy stock returns (Yahya et al 2021). Recent studies on the Chinese volatility index (iVX) show its predictive power to reflect investor sentiments (Xu and Zhou 2018;Long et al 2021b). Li et al (2019) argued that the iVX index reflects investor fear sentiments and negatively affects the Chinese market (Shanghai 50ETF).…”

Section: Investor Sentiments and Energy Stocksmentioning

confidence: 99%

“…Li et al (2019) argued that the iVX index reflects investor fear sentiments and negatively affects the Chinese market (Shanghai 50ETF). Long et al (2021b) argued that the Chinese volatility index can be utilized as investor sentiment at the micro, meso, and macro-level. Since we are evaluating the major predictors of the Chinese solar energy market, we find iVX a more relevant index to gauge investor sentiments.…”

Section: Investor Sentiments and Energy Stocksmentioning

confidence: 99%

“…Lastly, we have incorporated the distinguishing factor, i.e., investor sentiments, that explains the movements in energy and financial markets (Ji et al 2019;Song et al 2019). Previous studies have utilized the investor sentiment index utilized by Baker and Wurgler (2007); however, recently, Long et al (2021b) and Li et al (2019) revealed that the Chinese volatility index can reflect investor sentiments up to a considerable degree.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach

Yahya

Razzaq

2022

Environ Sci Pollut Res

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The study investigates the asymmetric effect of temperature, exchange rate, metals (rare metals and electrical conductors), and investor sentiments on solar stock price performance in China. The novel econometric techniques, i.e., QARDL (quantile autoregressive distributive lag) approach and Granger causality-in-quantiles to analyze the results. In both short-and long-run estimations, the findings suggest that rare metals (cadmium, germanium, indium, and selenium) and electrical conductors (silver, aluminum, and copper) have significant and positive linkage with solar energy stocks at different quantiles based on bullish, bearish, and normal market conditions. On the other hand, negative effects are found for temperature, RMB exchange rate, and investor sentiments in both the short-and long-run. In the short run, the effect of exchange rate varies across different quantiles but it confines to only lower quantiles (bearish market condition) in the longer run. Solar stocks are more prone to investor sentiments under higher quantiles (bullish market conditions). Lastly, we find that temperature is not merely a behavioral anomaly for the solar energy market as it spreads across middle quantiles (normal market conditions) in the longer run. The findings of Granger causality in quantiles further confirm the results of QARDL.

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Section: Investor Sentiments and Energy Stocksmentioning

confidence: 99%

Section: Investor Sentiments and Energy Stocksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach

Yahya

Razzaq

2022

Environ Sci Pollut Res

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“…In addition, such a forecast improvement is measurable in the short-term forecast horizon, but weakens as the forecast horizon increases. The study in [23] examines whether China's volatility index can reflect investor sentiment. This study examines whether China's official volatility index can indicate investor interest.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

et al. 2021

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The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals.

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“…Broadstock and Zhang [4] have shown that sentiment investor extracted from social-media (Twitter), has pricing power towards American stock market volatility. Likewise, [5] found that the common sentiment and expectations of Chinese investors has an impact on the excessive volatility of the Chinese financial market caused by the coronavirus epidemic in 2020.…”

Section: Review Articlementioning

confidence: 99%

Excess Volatility in the Tunisian Stock Market: Explanation by Behavioral Finance

Chkioua

2021

SAJSSE

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In this paper, we tried to show the existence of excess volatility of stock prices in the Tunisian stock exchange during the period 2000 - 2017, by applying the variance bounds of Shiller. We used data on daily closing prices and the transaction volume of 22 companies listed on Tunisian Financial market during the period 2016/2017 to identify the relationship between over confidence bias and the Excess Volatility via the Granger causality test. Based on Chuang and Lee’s approach, we studied the effect of the excess confidence component on volatility by the E-GARCH Model (1.1). Our results show that high market volatility resulted from overconfident investors.

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Can the Chinese volatility index reflect investor sentiment?

Cited by 31 publications

References 78 publications

The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach

The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach

Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

Excess Volatility in the Tunisian Stock Market: Explanation by Behavioral Finance

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