Aggregate Idiosyncratic Volatility

Is There a Volatility Puzzle in the Hong Kong Stock Market?

The Information Content of Idiosyncratic Volatility

Idiosyncratic volatility generates a lot of interest in literature, as there are conflicting evidences regarding the relationship between stocks’ idiosyncratic volatility and future returns. In this paper, we re-investigate this relationship. We argue that the impact of idiosyncratic volatility on stock return is contextual, i.e., depending on the stocks’ attributes. We focus on stocks’ quality measures. We hypothesize that for companies with high quality measures, the idiosyncratic volatility would have a positive impact on stock return, as it is more likely to be idiosyncratic volatilities on the good side, while on the other hand, for companies with bad qualities, higher idiosyncratic volatility might lead to negative return, as these companies are facing larger default probabilities due to their higher idiosyncratic volatilities. Our empirical results show that within our expectations, for stocks with higher quality measures there is a positive and significant relationship between stocks’ future return and their idiosyncratic volatility, while the story for stocks with lower quality measures is mixed. Our research will contribute to the idiosyncratic volatility literature by providing a thorough and conditional analysis about its impact on stock returns. It will also provide insights for the investors who wish to better understand the impact of idiosyncratic volatility on future stock return and, thus, are able to make better investment decisions.

This paper introduces a framework for analysis of cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data. We rst consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations. Next, we study an idiosyncratic volatility factor model, in which we decompose the co-movements in idiosyncratic volatilities into two parts: those related to factors such as the market volatility, and the residual co-movements. When using high frequency data, naive estimators of all of the above measures are biased due to the estimation errors in idiosyncratic volatility. We provide bias-corrected estimators and establish their asymptotic properties. We apply our estimators to high-frequency data on 27 individual stocks from nine dierent sectors, and document strong cross-sectional dependence in their idiosyncratic volatilities. We also nd that on average 74% of this dependence can be explained by the market volatility.

This paper examines the idiosyncratic volatility puzzle and whether investor sentiment influences the relation between idiosyncratic volatility and stock returns in the Chinese stock market. The findings indicate the existence of a negative idiosyncratic volatility effect. In addition, the results show that the relation between idiosyncratic volatility and returns significantly depends on investor sentiment. Thus, investor sentiment plays a very important role in reconciling the relation between idiosyncratic volatility and stock returns in the Chinese stock market. This implies that investor sentiment may be one of the major risk factors that should be considered in the Chinese stock market. In terms of predictive ability of investor sentiment, idiosyncratic volatility and market volatility, the findings indicate that idiosyncratic volatility positively predicts future excess market returns in the Chinese stock market.

For a typical firm, idiosyncratic volatility is as sensitive to the relative value of assets in place as to growth options. However, for firms dominated by assets in place (growth options), idiosyncratic volatility is more sensitive to the relative value of assets in place (growth options). Binding irreversibility constraint (uncertainty) makes the effect of assets in place (growth options) more pronounced. The institutional ownership in China’s (the U.S.) stock market is positively (negatively) related to idiosyncratic volatility. Our dynamic tests show two-way Granger-causality between institutional ownership and idiosyncratic volatility in the U.S. and one-way Granger-causality in China. It indicates that institutional investors behave differently in the U.S. and China. Stock characteristics are important factors which affect idiosyncratic volatility associated with institutional holding. Our findings are robust to controlling for the financial crisis period, proportions of institutional ownership, long-term and short-term institutional ownership, and different types of institutional investors. Using Sims' two-sided regression approach, we show that 1) there is bidirectional causality between institutional ownership and stock return volatility and 2) not accounting for the feedback effects from institutional ownership to return volatility yields the opposite institutional preferences on volatility. Subsequent analyses reveal that (surprisingly) prudence plays a role in institutions' preference on return volatility. But we fail to find support for the informational advantage argument in the literature. Lastly, we find weak evidence in the importance of growth opportunities in institutions' preferences on volatility.

This study investigates the relation between idiosyncratic volatility and future returns around the firm-specific news announcements in the Korean stock market from July 1995 to June 2018. The excess returns of decile portfolios that are formed by sorting the stocks based on news and non-news idiosyncratic volatility measures. The Fama and French three-factor model is also examined to see whether systematic risk affects news and non-news idiosyncratic volatility profits. The pricing of our news and non-news idiosyncratic volatility are confirmed in the cross-sectional regression using the Fama and MacBeth method. Market beta, size, book to market, momentum, liquidity, and maximum return are controlled to determine robustness. Our empirical evidence suggests that the pricing of the non-news idiosyncratic volatility is more strongly negative compared to the news idiosyncratic volatility, which is contrary to the limited arbitrage explanation for the negative price of the idiosyncratic volatility. We find that the non-news idiosyncratic volatility has a robust negative relation to returns in non-January months. Macro-finance factors drive the conditioned on the missing risk factor hypothesis, the pricing of idiosyncratic volatility. This study contributes to a better understanding of the role of the conditional idiosyncratic volatility in asset pricing. As the Korean stocks provide a fresh sample, our non-U.S. investigation delivers a useful out-of-sample test on the pervasiveness of the non-news volatility effect across the emerging markets.

Idiosyncratic volatility: An indicator of noise trading?

Ambiguity, Macro Factors, and Stock Return Volatility

Tests of Idiosyncratic Risk: Informed Trading Versus Noise and Arbitrage Risk

High Idiosyncratic Volatility and Low Returns: A Prospect Theory Based Explanation

Based on studies of idiosyncratic volatility developed in the recent literature, this study analyzes its relation with expected returns through the breakdown of idiosyncratic volatility in the Brazilian stock market and presents evidence of the importance of expected idiosyncratic volatility for asset pricing. We study the impact of the expected and unexpected components of idiosyncratic volatility on the returns of shares listed on the BOVESPA between 2004 and 2011. The results show a strong positive and significant relation between expected idiosyncratic volatility and returns. This evidence is highlighted when we use unexpected idiosyncratic volatility to control for unexpected returns. Additional robustness tests, controlling for size and momentum effects, also have positive and significant coefficients, corroborating previous findings.

Informed Option Trading and Stock Market Mispricing

The scale of global exchange-traded funds (ETFs) has shown an explosive growth trend; however, research on the impact of ETF activity on the stock market is still in its infancy. While ETFs increase the volatility of the U.S. stock market, there is scant literature on the impact of ETF activity on the volatility of the emerging A-share market. Owing to the large differences in the institutional and investor structure of the A-share market, will there be significant differences in the impact of ETFs on the volatility of the A-share market? This study found that ETF activity significantly reduced idiosyncratic volatility but increased systemic volatility. Since the decrease in idiosyncratic volatility was greater than the increase in systemic volatility, the overall volatility showed a downward trend. Further research on the impact mechanism showed that the idiosyncratic volatility of the A-share market was positively correlated with noise information. ETF activity reduced idiosyncratic volatility by reducing the noise information. Systematic volatility was positively correlated with faster information integration speed. ETF activity improved systematic volatility by improving information integration speed. This study elucidated the impact of ETF activity on stock price volatility under different market backgrounds.

Do Chinese internet stock message boards convey firm-specific information?