Can the Chinese volatility index reflect investor sentiment?

The low 2016 volatility index levels present a paradox in light of previous research suggesting periods of uncertainty and negative news events should reflect higher VIX levels. This study uses daily data for the VIX, VIX futures and the VVIX, to examine the information content of variations in the natural logarithmic changes in the index levels relative to 12 other parallel time periods encompassing 2004-2016. Straight-forward variation and predictive tests are constructed to determine signs of unusual market volatility behavior. The results reveal strong evidence of unusual volatility behavior during the 2016 election period, pocked by frequent periods of abnormal returns. The 2016 VIX levels alone are shown to be insufficient to draw conclusions regarding investor sentiment.

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Investor sentiment, realized volatility and stock returns

Review of Behavioral Finance ◽

10.1108/rbf-12-2020-0301 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Wafa Abdelmalek

Keyword(s):

Stock Returns ◽

Investor Sentiment ◽

Realized Volatility ◽

Causality Test ◽

Content Type ◽

Volatility Index ◽

Negative Sentiment ◽

Work Supports ◽

The Relationship ◽

Future Realized Volatility

PurposeThis paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression (QR) method.Design/methodology/approachIn the first step, the study applies the Granger causality test to understand the causal relationship between realized volatility, returns and sentiment as levels and changes. In the second step, the study employs a QR method to investigate whether investor sentiment and returns can predict realized volatility. This regression method gives robust results irrespective of distributional assumptions and to outliers in the dependent variable.FindingsEmpirical results show that the VIX volatility index is a better fear gauge of market-wide investors' sentiments and has a predictive power for future realized volatility in terms of levels and changes for both higher and lower data frequencies. This study provides evidence that the relationship between realized volatility, investor sentiment and returns, respectively, is not symmetric for all quantiles of QR, as opposed to OLS regression. Furthermore, this work supports the behavioral theory beyond leverage hypothesis in explaining the asymmetric relation between returns and volatility at higher and lower data frequencies.Originality/valueThis paper adds to the limited understanding of investor sentiment’s impact on volatility by proposing a QR model which provides a more complete picture of the relationship at all parts of the volatility distribution for both higher and lower data frequencies and in terms of levels and changes. To the author knowledge, this is the first paper to study the volatility responses to positive and negative sentiment changes for developed market and to use both lower and higher data frequencies as well as data in terms of levels and changes.

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An Investor Sentiment Barometer - Greek Implied Volatility Index

SSRN Electronic Journal ◽

10.2139/ssrn.1307680 ◽

2008 ◽

Author(s):

Costas Siriopoulos ◽

Athanasios Fassas

Keyword(s):

Implied Volatility ◽

Investor Sentiment ◽

Volatility Index

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On the relationship between investor sentiment, VIX and trading volume

Risk Governance and Control Financial Markets & Institutions ◽

10.22495/rgcv5i4c1art1 ◽

2015 ◽

Vol 5 (4) ◽

pp. 114-122

Author(s):

Simon Man Shing So ◽

Violet U. T. Lei

Keyword(s):

Stock Market ◽

Trading Volume ◽

Investor Sentiment ◽

Percentage Increase ◽

Noise Traders ◽

Volume Increase ◽

Volatility Index ◽

Fundamental Value ◽

The Relationship

As noise traders affect stock market by trading, sentiment, as a signal of noise, may have relationships with trading volume. This paper explores the effect of sentiment on the stock market’s trading volume. Increase in Volatility Index (VIX) can explain the percentage increase in trading volume, but only in high VIX period. Besides, higher level of VIX is likely to be associated with greater variability of trading volume. The noise traders add liquidity to the market and provide more chances for investors to time their trade as the volatility of liquidity increases. These two kinds of impact lower rational investors’ required return. The noise traders not only drive the price deviating from fundamental value, but also influence the liquidity dimensions

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Does Fear has Stronger Impact than Confidence on Stock Returns? The Case of Asia-Pacific Developed Markets

Scientific Annals of Economics and Business ◽

10.47743/saeb-2020-0009 ◽

2020 ◽

Vol 67 (2) ◽

pp. 157-175

Author(s):

Ngoc Bao Vuong ◽

Yoshihisa Suzuki

Keyword(s):

Stock Returns ◽

Investor Sentiment ◽

Return Predictability ◽

Asia Pacific ◽

Market Returns ◽

Investor Confidence ◽

Developed Markets ◽

Volatility Index ◽

The Impact ◽

The Relationship

Employing data from Australia, Hong Kong, and Japan over the period between January 2004 to December 2017, this study investigates the relationship between investor sentiment and stock returns. We analyze two reversed sentiment indicators, namely Consumer Confidence Index (CCI) and Volatility Index (VIX), in two conversing situations: low and high sentiment. The empirical evidence suggests that sentiment has a significant link with concurrent returns, but its influence seems to wipe out quickly as the little to no return predictability is detected. More importantly, we find that “investor fear gauge” (VIX) generates a more significant contemporaneous effect on market returns than investor confidence. The impact on future returns, on the contrary, is inconclusive since low CCI and VIX dominate the opposite ones most of the time.

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Measures Of Investor Sentiment: A Comparative Analysis Put-Call Ratio Vs. Volatility Index

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v6i8.2458 ◽

2011 ◽

Vol 6 (8) ◽

Cited By ~ 7

Author(s):

Arindam Bandopadhyaya ◽

Anne Leah Jones

Keyword(s):

Asset Prices ◽

Investor Sentiment ◽

Economic Factors ◽

Short Term ◽

Market Sentiment ◽

Volatility Index ◽

Daily Data ◽

Traditional Research ◽

Two Measures ◽

Better Than

Traditional research on asset pricing has focused on firm-specific and economy-wide factors that affect asset prices.  Recently, the finance literature has turned to non-economic factors, such as investor sentiment, as possible determinants of asset prices (see for example, Fisher and Statman 2000 and Baker and Wurgler 2006).  Studies such as Baek, Bandopadhyaya and Du (2005) suggest that shifts in investor sentiment may explain short-term movements in asset prices better than any other set of fundamental factors.  A wide array of investor sentiment measures are now available, which leads us quite naturally to the question of which measure best mirrors actual market movements.   In this paper, we begin to address this question by comparing two measures of investor sentiment which are computed daily by the Chicago Board Options Exchange (CBOE) and for which historical data are freely available on the CBOE website, thus making them ideal for use by both academics and practitioners studying market behavior: the Put-Call Ratio (PCR) and the Volatility Index (VIX).  Using daily data from January 2, 2004 until April 11, 2006, we find that the PCR is a better explanatory variable than is the VIX for variations in the S&P 500 index that are not explained by economic factors.  This supports the argument that, if one were to choose between these two measures of market sentiment, the PCR is a better choice than the VIX.

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DOES POLICY UNCERTAINTY AFFECT EQUITY, COMMODITY, INTEREST RATES, AND CURRENCY MARKETS? EVIDENCE FROM CBOE’S VOLATILITY INDEX

Journal of Business Economics and Management ◽

10.3846/jbem.2020.13164 ◽

2020 ◽

Vol 21 (5) ◽

pp. 1350-1374

Author(s):

Imlak Shaikh

Keyword(s):

Economic Policy ◽

Interest Rates ◽

Implied Volatility ◽

Empirical Work ◽

Investor Sentiment ◽

Policy Uncertainty ◽

Stock Market Volatility ◽

Currency Markets ◽

Volatility Index ◽

The Us

Economic policy drives investment, production, employment, and other macroeconomic indicators of the economy. The study examines the equity, commodity, interest rates, and currency markets, taking into consideration the US economic policy uncertainty (EPU) index. The present work determines the association among policy uncertainty and volatility index, expressed in terms of generalized autoregressive conditional heteroscedasticity and period of empirical work spanning from 2000 to 2018. The results suggest that equity markets’ volatility tends to be very high based on a high degree of policy uncertainty. The findings on the commodity market indicate that crude oil and gold prices remain more volatile during the presidential election and financial crisis. One of the essential results shows that the 2000s boom, early credit crunch, Lehman’s collapse and recession, and fiscal policy battles have significantly affected the equity, currency, and commodity markets. The interest rates and currency markets have responded considerably to Feds’ and EPU index. The empirical outcome provides evidence that implied volatility index is a forward looking expectation of future stock market volatility, and it uncovers that policy uncertainty affects investor sentiment. The present work holds some practical implications for the government to formulate policies to regulate the US market.

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