Expected Idiosyncratic Volatility Measures and Expected Returns

We find that idiosyncratic volatility forecasts using information available to traders at the time of the forecast are not related to expected returns. The positive relation documented in a number of other papers only exists when forward‐looking information is incorporated into the volatility estimate. That positive relation is driven by the realized idiosyncratic volatility component that cannot be forecasted by investors. Our findings are robust to several different empirical tests, volatility forecasting models and time periods.     

Idiosyncratic Volatility Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co‐varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross‐sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk‐averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.     

The Dynamic Relation Between Returns and Idiosyncratic Volatility

We claim that regressing excess returns on one-lagged volatility provides only a limited picture of the dynamic effect of idiosyncratic risk, which tends to be persistent over time. By correcting for the serial correlation in idiosyncratic volatility, we find that idiosyncratic volatility has a significant positive effect. This finding seems robusrt for various firm size portfolios, sample periods, and measures of idiosyncratic risk. Our findings suggest stock markets mis-price idiosyncratic risk. There may be some measurement problems with idiosyncratic risk. There may be some measurement problems with idiosyncratic risk that could be related to nondiversifiable risk.     

特质风险与市场收益动态关系的实证研究

本文使用流通市值加权的股票平均波动率作为股票市场未分散特质风险的间接衡量指标,对A股市场特质风险与市场预期收益之间的动态关系进行研究。有别于国内已有研究,本文使用流通市值加权股票平均波动率的自回归残差项作为回归模型的解释变量,避免了解释变量高度持续性特征给回归结果造成的不利影响,发现A股市场未分散的特质风险对预期市场超额收益具有预测能力,两者呈正相关关系,这种预测能力在考虑市场分割、流动性、经济周期以及不同特质风险度量方法后依然存在。     

Idiosyncratic Risk and REIT Returns

The volatility of a stock returns can be decomposed into market and firm-specific volatility, with the former commonly known as systematic risk and the later as idiosyncratic risk. This study examines the relevance of idiosyncratic risk in explaining the monthly cross-sectional returns of REIT stocks. Contrary to the CAPM theory, a significant positive relationship is found between idiosyncratic volatility and the cross-sectional returns. This suggests that firm-specific risk matters in REIT pricing. The regression results further show that once idiosyncratic risk is controlled for in the asset-pricing model, the size and book-to-market equity ratio factors ceased to be significant. The explanatory power of the momentum effect remains robust in the presence of idiosyncratic risk.

Price Momentum and Idiosyncratic Volatility

We find that returns to momentum investing are higher among high idiosyncratic volatility ( IVol) stocks, especially high IVol losers. Higher IVol stocks also experience quicker and larger reversals. The findings are consistent with momentum profits being attributable to underreaction to firm‐specific information and with IVol limiting arbitrage of the momentum effect. We also find a positive time‐series relation between momentum returns and aggregate IVol. Given the long‐term rise in IVol, this result helps explain the persistence of momentum profits since Jegadeesh and Titman's (1993) study.     

Idiosyncratic Volatility and the Expected Stock Returns for Exploring the Relationship with Panel Threshold Regression

This paper utilizes panel threshold regression to study the impact of idiosyncratic risk of stock returns on the Taiwan Security Market over the period from 2000 to 2011, during which there has been a noticeable increase idiosyncratic volatility. An innovative panel threshold regression model is applied to test the panel threshold effect of idiosyncratic risk on expected stock returns. The results support Merton’s (J Financ 42:579–590, 1987) investor recognition hypothesis and confirm that a threshold effect does exist. This study shows that it is possible to identify the definitive level beyond which a further increase in idiosyncratic volatility does not improve proportional expected stock returns. Some important policy implications arise from these findings. The conditional distribution of expected stock returns is allowed to vary across low volatility states. The evidence suggests that in Taiwan, idiosyncratic risk is a predictor of future market returns based upon threshold value during the lower variance state. In contrast, when the threshold value is exceeded, the relation between idiosyncratic risk and expected stock returns is not statistically significant.     

Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle

Buying is easier than shorting for many equity investors. Combining this arbitrage asymmetry with the arbitrage risk represented by idiosyncratic volatility (IVOL) explains the negative relation between IVOL and average return. The IVOL‐return relation is negative among overpriced stocks but positive among underpriced stocks, with mispricing determined by combining 11 return anomalies. Consistent with arbitrage asymmetry, the negative relation among overpriced stocks is stronger, especially for stocks less easily shorted, so the overall IVOL‐return relation is negative. Further supporting our explanation, high investor sentiment weakens the positive relation among underpriced stocks and, especially, strengthens the negative relation among overpriced stocks.     

Corporate Bond Returns and Volatility

Recent literature emphasizes the relation of stock volatility to corporate bond yields. We demonstrate that during 1996–2005 corporate bond excess return volatility is directly related to contemporaneous corporate bond excess returns. In fact, the decompositions of aggregate bond volatility have a higher contemporaneous correlation with bond yields in comparison to idiosyncratic stock risk. Additionally, bond volatility and idiosyncratic risk are significant predictors of corporate three‐month and six‐month ahead bond excess returns. We also find that corporate bond volatility contains both slow moving and time‐varying components.     

Expected Returns and Idiosyncratic Risk: Industry-Level Evidence from Russia

We explore a relation between expected returns and idiosyncratic risk in Russia. Investors in the Russian stock market cannot fully diversify their portfolios due to transaction costs, information gathering and processing costs, and shortcomings in investor protection. This implies that investors demand a premium for idiosyncratic risk. We estimate the price of idiosyncratic risk using MIDAS regressions and a cross section of Russian industry portfolios. We find that idiosyncratic risk is economically significant and commands a negative (positive) premium, on average, of 10.0% (8.0) per year before (after) the global financial crisis in 2008. The results remain unaffected after controlling for global pricing factors and return reversal.     

Idiosyncratic Risk,Investor Base,and Returns

Using four different proxies for a firm's investor base we demonstrate that idiosyncratic risk premiums are larger for neglected stocks and smaller or economically insignificant for visible stocks. Since neglected stocks have greater idiosyncratic volatility (IV), the total IV risk premium (price × quantity) for neglected stocks will be greater than that of visible stocks. Additionally, we find a positive size effect and negative beta effect after controlling for IV. Overall, our results provide strong support for Merton's theory that market segmentation induced by incomplete information is an important component of the influence of IV in the cross‐section of returns.     

Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns

Recent empirical evidence suggests that the interdaily volatility clustering for most speculative returns are best characterized by a slowly mean-reverting fractionally integrated process. Meanwhile, much shorter lived volatility dynamics are typically observed with high frequency intradaily returns. The present article demonstrates, that by interpreting the volatility as a mixture of numerous heterogeneous short-run information arrivals, the observed volatility process may exhibit long-run dependence. As such, the long-memory characteristics constitute an intrinsic feature of the return generating process, rather than the manifestation of occasional structural shifts. These ideas are confirmed by our analysis of a one-year time series of five-minute Deutschemark-U.S. Dollar exchange rates.     

A Consumption-Based Explanation of Expected Stock Returns

When utility is nonseparable in nondurable and durable consumption and the elasticity of substitution between the two consumption goods is sufficiently high, marginal utility rises when durable consumption falls. The model explains both the cross‐sectional variation in expected stock returns and the time variation in the equity premium. Small stocks and value stocks deliver relatively low returns during recessions, when durable consumption falls, which explains their high average returns relative to big stocks and growth stocks. Stock returns are unexpectedly low at business cycle troughs, when durable consumption falls sharply, which explains the countercyclical variation in the equity premium.     

Idiosyncratic Volatility and Firm‐Specific News: Beyond Limited Arbitrage

We examine the relation between idiosyncratic volatility and returns around news announcements. Mispricing is most likely to occur during news announcements. If idiosyncratic volatility generates a limit to arbitrage, then the negative relation between returns and news volatility should be stronger than the relation to nonnews volatility. Instead, we find nonnews volatility has a robust negative relation to returns and lacks key features expected if volatility were a reflection of limits to arbitrage. Pricing of nonnews volatility is related to lottery‐like features of a stock's return. Our results suggest that volatility has a price effect beyond a limit to arbitrage.     

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.     

Idiosyncratic Consumption Risk and the Cross Section of Asset Returns

This paper investigates the importance of idiosyncratic consumption risk for the cross‐sectional variation in asset returns. We find that besides the rate of aggregate consumption growth, the cross‐sectional variance of consumption growth is also a priced factor. This suggests that consumers are not fully insured against idiosyncratic consumption risk, and that asset returns reflect their attempts to reduce their exposure to this risk. The resulting two‐factor consumption‐based asset pricing model significantly outperforms the CAPM, and its performance compares favorably with that of the Fama–French three‐factor model.     

Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach

This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests.     

Real Options,Volatility, and Stock Returns

We provide evidence that the positive relation between firm‐level stock returns and firm‐level return volatility is due to firms’ real options. Consistent with real option theory, we find that the positive volatility‐return relation is much stronger for firms with more real options and that the sensitivity of firm value to changes in volatility declines significantly after firms exercise their real options. We reconcile the evidence at the aggregate and firm levels by showing that the negative relation at the aggregate level may be due to aggregate market conditions that simultaneously affect both market returns and return volatility.     

Portfolio Returns,Market Volatility,and Seasonality

This paper examines three important issues related to the relationship between stock returns and volatility. First, are Duffee's (1995) findings of the relationship between individual stock returns and volatility valid at the portfolio level? Second, is there a seasonality of the market return volatility? Lastly, do size portfolio returns react symmetrically to the market volatility during business cycles? We find that the market volatility exhibits strong autocorrelation and small size portfolio returns exhibit seasonality. However, this phenomenon is not present in large size portfolios. For the entire sample period of 1962–1995, the highest average monthly volatility occurred in October, followed by November, and then January. Examining the two sub-sample periods, we find that the average market volatility increases by 15.4% in the second sample period of 1980–1995 compared to the first sample period of 1962–1979. During the contraction period, the average market volatility is 60.9% higher than that during the expansion period. Using a binary regression model, we find that size portfolio returns react asymmetrically with the market volatility during business cycles. This paper documents a strongly negative contemporaneous relationship between the size portfolio returns and the market volatility that is consistent with the previous findings at the aggregate level, but is inconsistent with the findings at the individual firm level. In contrast with the previous findings, however, we find an ambiguous relationship between the percentage change in the market volatility and the contemporaneous stock portfolio returns. This ambiguity is attributed to strongly negative contemporaneous and one-month ahead relationships between the market volatility and portfolio returns.