Idiosyncratic Volatility Puzzle Research Articles

Climate risk and the idiosyncratic volatility puzzle

ABSTRACT Our study offers intriguing evidence on the much-debated idiosyncratic volatility (IdVol) puzzle from a climate risk perspective. Using a set of US-listed stocks from July 2010 to December 2019, our robust portfolio- and stock-level results reveal that the IdVol puzzle exists in both carbon-footprint-disclosing and non-disclosing stocks. Furthermore, investors do not perceive a significant difference in the IdVol puzzle between carbon-footprint-disclosing and non-disclosing stocks. However, the IdVol puzzle is concentrated in high-carbon-intensive stocks as far as those carbon-footprint-disclosing stocks are concerned, implying the combined effect of investor attention and ethical screening of stocks. Overall, our results substantiate the specific impact of climate-related risks on asset pricing decisions.

Is The Idiosyncratic Volatility Puzzle Driven By A Missing Factor?

Is The Idiosyncratic Volatility Puzzle Driven By A Missing Factor?

Expected idiosyncratic entropy

PurposeWe propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.Design/methodology/approachWe estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.FindingsWe find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.Originality/valueWe propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

Dissecting the idiosyncratic volatility puzzle: A fundamental analysis approach

This paper argues fundamental information help resolve information uncertainty that leads to high idiosyncratic volatility premium. The IVOL-return relation is negative for stocks with poor fundamental strength but positive for stocks with strong fundamental strength. The arrival of fundamental news weakens the negative IVOL effect. Our findings are robust for alternative model specifications. Moreover, the negative IVOL effect dominates the positive IVOL effect due to arbitrage asymmetry that buying is easier than short selling stocks. Consistent with arbitrage asymmetry, the negative IVOL effect is stronger for stocks with low institutional ownership and following high investor sentiment. Overall, we provide a simple fundamental-based explanation for idiosyncratic volatility puzzle.

On the conditional performance of the IVOL anomaly

This paper proposes that the well-known idiosyncratic volatility (IVOL) puzzle is partly due to an overconditioning bias in IVOL estimates when there exists nonlinearity between stock and factor returns. We analytically derive the overconditioning bias when beta and IVOL are contemporaneously estimated using daily returns. To mitigate the estimation bias, we employ a conditional factor model, as in Avramov and Chordia (2006), to estimate systematic risk exposure and IVOL. Our empirical results show that the conditional IVOL estimates do not imply a negative return premium when firm fundamentals are used as conditioning variables, and the associated conditional factor-adjusted alpha is much smaller than the original IVOL puzzle. Our findings are robust in the U.S. and Chinese stock markets.

A closer look at the relationship between firm-specific return variation and stock returns

PurposePrior studies use two measures of firm-specific return variation (FSRV): idiosyncratic volatility in absolute and relative terms, the latter of which is also termed stock price nonsynchronicity. Whereas most research focuses on investigating the idiosyncratic volatility puzzle, the authors carry out comparison of these two measures and further investigate which of the two constituents of nonsynchronicity explain the association between FSRV and stock returns, emphasising the importance of assessing which component drives stock returns.Design/methodology/approachThe authors use the US individual stock returns from 1925 to 2016 and define the two measures of FRSV based on the Fama and French (1993) model. Specifically, the authors decompose the relative measure into two components: (i) absolute idiosyncratic volatility and (ii) systematic volatility. The authors conduct various tests based on high-minus-low, zero-investment quintile portfolio sorts and perform the Fama–MacBeth analysis by singling out each component.FindingsThe authors find a positive return on the portfolio sorted on relative idiosyncratic volatility or on systematic volatility, but find a negative return sorted on absolute idiosyncratic volatility. The results are robust after controlling for size, BM and other risk characteristics using a double-sorting approach. The Fama–MacBeth regression results show that a positive association between the relative measure and stock returns is driven primarily by the low-systematic-volatility anomaly across firms. The findings are robust to controlling for return residual momentum, skewness, jumps and information discreteness.Originality/valueExtant research posits the idiosyncratic volatility puzzle and the low-volatility anomaly. The authors emphasize the importance of integrating these two streams of research. This study enhances the understanding of the driving force underlying the relationship between FSRV and cross-sectional stock returns.

Research On the Impact of Margin Trading on The Stock Idiosyncratic Volatility

In classical asset pricing model, expected stock return is only determined by systematic risk, and idiosyncratic risk can be completely dispersed without asset pricing effect. However, the real world and the assumptions of classical asset pricing model are different, as idiosyncratic risk cannot be completely dispersed and has pricing effect. Recent studies found that the stock idiosyncratic volatility can actually predict stock return negatively, indicating the existence of the " Idiosyncratic volatility puzzle" in the Chinese stock market. There are constraints on margin trading in the Chinese stock market, which can affect investors' investment behavior and expectation of stock return. This article adopts difference-in-differences model and multivariable linear regression mode to test the impact of margin trading on idiosyncratic volatility, and further investigate the impact of margin trading on the " Idiosyncratic volatility puzzle ". The research results found that: In the process from the establishment of the margin trading system to the gradual expansion of underlying stocks, the impact of margin trading on idiosyncratic volatility has shifted from a positive impact to a negative impact. That is to say, margin trading has transitioned from initially increasing idiosyncratic volatility to later reducing idiosyncratic volatility, gradually playing a role in price discovery and reducing idiosyncratic risk in the stock market. The gradual improvement of the margin trading system is driving the development and maturity of the Chinese stock market.

Idiosyncratic volatility and firm-specific news: evidence from the Chinese stock market

This study investigates the effect of firm-specific news on the pricing of idiosyncratic volatility (IVOL) in China. Using a sample of non-financial A-share listed firms from January 2006 to June 2018, we find that the predictive ability of IVOL is much weaker around firm announcements compared to that without news, suggesting that the limited arbitrage cannot disentangle the IVOL puzzle completely in the emerging market. Additionally, we investigate the effect of news sentiment on the predictive ability of IVOL and find that it is much stronger following bad news compared to good news. Finally, when we include the macroeconomic variables known to predict returns to adjust the systematic risk, we obtain novel findings that the negative premium of IVOL becomes insignificant, suggesting that the negative premium is time-varying with macroeconomy.

Idiosyncratic volatility in commodity futures markets: measurement and puzzle

PurposeThis paper aims to propose an alternative method to measure idiosyncratic volatility and test whether the idiosyncratic volatility puzzle holds in commodity futures markets.Design/methodology/approachThis paper proposes a partially new measure of idiosyncratic volatility in commodity futures markets based on the Schwartz and Smith (2000) short-term/long-term model. This model enables us to capture systematic risks of commodity futures markets in a parsimonious way.FindingsUsing a sample of futures contracts for 20 commodities from 1973 to 2022, this paper demonstrates that idiosyncratic volatility is more significant than systematic volatility in commodity futures markets, and that the idiosyncratic volatility puzzle does not hold in these markets. This paper also performs robustness tests to investigate whether the puzzle holds during subsample periods when commodity markets are more volatile and find consistent results. This study highlights the differences between commodity futures markets and equity markets and emphasizes the importance of investigating idiosyncratic volatility in commodity futures markets.Originality/valueThe contributions of this paper are threefold. First, this paper contributes to the literature by focusing on the idiosyncratic volatility of commodity futures returns. Second, this paper constructs a partially new measure of idiosyncratic volatility in commodity futures markets. Finally, this paper also contributes to the literature on the idiosyncratic volatility puzzle and demonstrates that the puzzle may not exist in commodity futures markets.

The Causal Impact of Short-sale Constraints on the Idiosyncratic Volatility Puzzle

The Causal Impact of Short-sale Constraints on the Idiosyncratic Volatility Puzzle

Leasing and the Idiosyncratic Volatility Puzzle

Leasing and the Idiosyncratic Volatility Puzzle

Expected profitability, the 52-week high and the idiosyncratic volatility puzzle

We investigate the joint ability of fundamental-based and market-based news to explain the anomalous underperformance of stocks with high idiosyncratic volatility (high IVOL). An out-of-sample prediction of future profitability is adopted as a proxy for fundamental–based news while market-based news is represented by the 52-week high price ratio. A sample of UK stocks over the period January 1996 to December 2017 is analysed. The empirical results indicate that both the fundamental-based projected profitability and the 52-week high price ratio are important in explaining the IVOL anomaly. In contrast, individually, neither variable fully accounts for the anomaly. This relation is more pronounced following a period of high sentiment and during an upmarket. Further results suggest that underreaction lies at the heart of this explanation.

Pricing Implications of Noise

Abstract We study the interaction between noisy demand and skewed asset payoffs. In our model, price as a function of quantities is convex in a neighborhood around zero if and only if skewness is positive. The combination of convexity and noise produces the idiosyncratic skewness effect, a documented negative relationship between an asset’s idiosyncratic skewness and its expected return. We further offer an explanation for the idiosyncratic volatility puzzle. Finally, our theory predicts that higher idiosyncratic skewness strengthens the idiosyncratic volatility effect (and vice versa). We find support for this prediction in the cross-section of stock returns.

Investor attention, analysts coverage and idiosyncratic volatility puzzle: based on behavioral perspective

PurposeConsidering the role of analysts in disseminating information, the paper explains the idiosyncratic volatility puzzle of China's stock market. As the largest developing country, China's research can provide meaningful reference for the research of financial markets in other new countries.Design/methodology/approachFrom the perspective of behavior, establishing a direct link between individual investor attention and stock price overvaluation.FindingsThe authors find that there is a significant idiosyncratic volatility puzzle in China's stock market. Due to the role of mispricing, individual investor attention significantly enhances the idiosyncratic volatility effect, that is, as individual investor attention increases, the greater the idiosyncratic volatility, the lower the expected return. Attention can explain the idiosyncratic volatility puzzle in China's stock market. In addition, due to the role of information production and dissemination, securities analysts can reduce the degree of market information asymmetry and enhance the transparency of market information.Originality/valueChina is the second largest economy in the world, and few scholars analyze it from the perspective of investors' attention. The authors believe this paper has the potential in contributing to the academia.

Price informativeness: a potential explanation for the idiosyncratic volatility puzzle

ABSTRACT Price informativeness is the amount of private information incorporated into stock prices and measured by from regressions of stock returns on systematic risk factors. While is closely related with idiosyncratic volatility, it is not interchangeable because magnitude of systematic volatility simultaneously changes. By controlling for the systematic volatility using the double-sorting portfolio approach, we suggest a potential involvement of price informativeness with the idiosyncratic volatility puzzle. Both cross-sectional evidence of monotonic and inverse relationship between idiosyncratic volatility and and time-series evidence of disappearing alphas of the low-minus-high idiosyncratic volatility portfolios during recessions support an explanation of the idiosyncratic volatility puzzle in association with price informativeness.

Idiosyncratic volatility puzzle exists at the country level

This paper reexamines the idiosyncratic volatility puzzle using 47 stock market indices from 1995 to 2016. We find that country-specific idiosyncratic volatility is significantly and negatively related to subsequent returns in the international markets, confirming the existence of the idiosyncratic volatility puzzle at the country level. Our results show that the positive relationship between idiosyncratic volatility and country-specific index returns documented by Bali and Cakici (2010) and Hueng and Yau (2013) is not robust to sample selections and model misspecifications. We show that the country-level idiosyncratic volatility puzzle consistently exists after controlling for different model specifications and market segmentation in various subsamples.

Idiosyncratic volatility, turnover and the cross-section of stock returns: evidence from the Korean stock market

PurposeThe authors aim to understand the driving forces behind the idiosyncratic volatility puzzle in the Korean stock market. The authors study the Korean stock market because previous works report a strong idiosyncratic volatility puzzle in Korea, and the market for the exchange-traded funds (ETFs) including low volatility ETFs has experienced drastic growth in Korea.Design/methodology/approachUsing common stocks listed either on KOSPI or KOSDAQ over the period 1997–2016, the authors estimate idiosyncratic volatility using the Fama–French three-factor model. In addition, based on prior literature, the authors use turnover as a proxy for overvaluation. The authors then study the role of turnover in understanding the idiosyncratic volatility puzzle in Korea.FindingsThe authors find that turnover is highly associated with idiosyncratic volatility. Turnover is extremely large among firms with high idiosyncratic volatility and the puzzle disappears after we control for turnover, meaning that turnover subsumes the explanatory power of idiosyncratic volatility for equity returns. The authors also find underperformance of stocks with high turnover and high idiosyncratic volatility exclusively during earnings announcement periods. Overall, our finding implies that the puzzle arises since high idiosyncratic volatility stocks due to high turnover are overvalued and experience correction afterwards.Originality/valueLiterature has suggested explanations based on lottery preferences of investors and market frictions behind the idiosyncratic volatility puzzle. What makes our study distinct from previous work is that we find the role of turnover in understanding the idiosyncratic volatility puzzle using turnover measure as a proxy for overvaluation in the Korean stock market.

The role of investor attention in idiosyncratic volatility puzzle and new results

The role of investor attention in idiosyncratic volatility puzzle and new results

Does idiosyncratic volatility proxy for a missing risk factor? Evidence using portfolios as test assets

AbstractOne of the main explanations for the idiosyncratic volatility (IVOL) puzzle (i.e., the negative relation between lagged IVOL and returns) is a missing risk factor. We show, analytically, that if IVOL proxies for a missing risk factor, then the negative relation between IVOL and returns should persist at the portfolio level. Empirically, we find that the IVOL puzzle disappears when we use well‐diversified portfolios as test assets. The IVOL puzzle also weakens after controlling for additional risk factors. Overall, our results suggest that both diversifiable (i.e., true idiosyncratic risk) and nondiversifiable risk play a role in explaining the IVOL puzzle.

Variance risk and the idiosyncratic volatility puzzle

We decompose the CBOE's VIX index into the variance risk premium (to proxy for risk aversion) and conditional variance of stock returns (to proxy for economic uncertainty). We show that the variance risk premium has relatively strong predictive power for the return spread between high and low idiosyncratic volatility (IVOL) quintile portfolios. Thus, investors demand a premium for variance risk, which reflects the uncertainty of the asset's return variance itself. The findings also indicate that higher levels of risk aversion and tense economic conditions help minimize the IVOL-return puzzle. Our results are robust using value-weighted and equal-weighted returns.