Can tail risk explain size,book‐to‐market,momentum, and idiosyncratic volatility anomalies?

We examine the impact of tail risk on the return dynamics of size, book‐to‐market ratio, momentum and idiosyncratic volatility sorted portfolios. Our time‐series analyses document significant portfolio return exposures to aggregate tail risk. In particular, portfolios that contain small, value, high idiosyncratic volatility and low momentum stocks exhibit negative and statistically significant tail risk betas. Our cross‐sectional analyses at the individual stock level suggest that tail risk helps in explaining the four pricing anomalies, particularly size and idiosyncratic volatility anomalies.     

Jump risk,stock returns,and slope of implied volatility smile

In the presence of jump risk, expected stock return is a function of the average jump size, which can be proxied by the slope of option implied volatility smile. This implies a negative predictive relation between the slope of implied volatility smile and stock return. For more than four thousand stocks ranked by slope during 1996–2005, the difference between the risk-adjusted average returns of the lowest and highest quintile portfolios is 1.9% per month. Although both the systematic and idiosyncratic components of slope are priced, the idiosyncratic component dominates the systematic component in explaining the return predictability of slope. The findings are robust after controlling for stock characteristics such as size, book-to-market, leverage, volatility, skewness, and volume. Furthermore, the results cannot be explained by alternative measures of steepness of implied volatility smile in previous studies.     

Is idiosyncratic volatility priced in commodity futures markets?

This article investigates the relationship between expected returns and past idiosyncratic volatility in commodity futures markets. Measuring the idiosyncratic volatility of 27 commodity futures contracts with traditional pricing models that fail to account for backwardation and contango leads to the puzzling finding that idiosyncratic volatility is significantly negatively priced cross-sectionally. However, idiosyncratic volatility is not priced when the phases of backwardation and contango are suitably factored in the pricing model. A time-series portfolio analysis similarly suggests that failing to recognize the fundamental risk associated with the inexorable phases of backwardation and contango leads to overstated profitability of the idiosyncratic volatility mimicking portfolios.     

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.     

The cross section of cashflow volatility and expected stock returns

I show that historical cashflow volatility is negatively related to future returns cross-sectionally. The negative association is large; economically meaningful; long-lasting up to five years; robust to known return-informative effects of size, value, price and earnings momentums and illiquidity; and extends to both systematic and idiosyncratic cashflow volatilities. Using the standard deviations of cashflow to sales and of cashflow to book equity as proxies for cashflow volatility, the least volatile decile portfolio outperforms the most volatile decile portfolio by 13% a year relative to the Fama–French four factors. The cashflow volatility effect is closely related to the idiosyncratic return volatility effect documented in Ang et al. [Ang, A., Hodrick, R.J., Xing, Y. and Zhang, X. “The cross-section of volatility and expected returns.” Journal of Finance, 51 (2006), 259–299.]. However, in portfolios simultaneously sorted on both cashflow and return volatilities, and in cross sectional regressions of returns at the firm level, these two effects neither drive out nor dominate each other. While the pricing of idiosyncratic cashflow volatility represents an anomaly against the traditional asset pricing theories, the pricing of historical cashflow uncertainty sheds light on potential fundamental risks embodied in the Fama–French HML and SMB factors.     

The cross-section of industry equity returns and global tactical asset allocation across regions and industries

This study investigates which index characteristics predict returns in the cross-section of local industry indexes in six regions. The results show that geographical origin and market capitalization of indexes critically determine the predictive ability of characteristics. We find that industry indexes of any market capitalization with high earnings-to-price ratio yield higher expected returns in the US, Europe, and Asia-Pacific. Recent winner (loser) portfolios in Europe have a tendency to outperform (underperform) recent loser (winner) portfolios in the near future for all groups of market capitalization. Small portfolios with high idiosyncratic volatility in Asia-Pacific earn an idiosyncratic volatility premium. Dividend yield is positively related to future returns of small European portfolios. These results are robust to the inclusion of transaction costs and control variables and have implications for portfolio managers following a global tactical asset allocation policy.     

Return dispersion and expected returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.     

Cross section of option returns and idiosyncratic stock volatility

This paper presents a robust new finding that delta-hedged equity option return decreases monotonically with an increase in the idiosyncratic volatility of the underlying stock. This result cannot be explained by standard risk factors. It is distinct from existing anomalies in the stock market or volatility-related option mispricing. It is consistent with market imperfections and constrained financial intermediaries. Dealers charge a higher premium for options on high idiosyncratic volatility stocks due to their higher arbitrage costs. Controlling for limits to arbitrage proxies reduces the strength of the negative relation between delta-hedged option return and idiosyncratic volatility by about 40%.     

Does more information in stock price lead to greater or smaller idiosyncratic return volatility?

We investigate the relation between price informativeness and idiosyncratic return volatility in a multi-asset, multi-period noisy rational expectations equilibrium. We show that the relation between price informativeness and idiosyncratic return volatility is either U-shaped or negative. Using several price informativeness measures, we empirically document a U-shaped relation between price informativeness and idiosyncratic return volatility. Our study therefore reconciles the opposing views in the following two strands of literature: (1) the growing body of research showing that firms with more informative stock prices have greater idiosyncratic return volatility, and (2) the studies arguing that more information in price reduces idiosyncratic return volatility.     

EXPECTED VOLATILITY,UNEXPECTED VOLATILITY,AND THE CROSS‐SECTION OF STOCK RETURNS

The existing literature finds conflicting results on the cross‐sectional relation between expected returns and idiosyncratic volatility. We contend that at the firm level, the sample correlation between unexpected returns and expected idiosyncratic volatility can cloud the true relation between the expected return and expected idiosyncratic volatility. We show strong evidence that unexpected idiosyncratic volatility is positively related to unexpected returns. Using unexpected idiosyncratic volatility to control for unexpected returns, we find expected idiosyncratic volatility to be significantly and positively related to expected returns. This result holds after controlling for various firm characteristics, and it is robust across different sample periods.     

Idiosyncratic return volatility and the role of firm fundamentals: A cross-country analysis

We investigate the relation between fundamental idiosyncratic volatility and stock returns idiosyncratic volatility using data from 56 countries. We find a strong positive relation between fundamental idiosyncratic volatility and idiosyncratic volatility of returns. This association, however, seems to be entirely concentrated in the developed economies, and we find no effect in the emerging markets. Specifically, fundamental idiosyncratic volatility does not lead to more idiosyncratic return volatility in countries with poor legal institutions and weak shareholder protection laws.     

Dynamic effects of idiosyncratic volatility and liquidity on corporate bond spreads

We study the dynamic impact of idiosyncratic volatility and bond liquidity on corporate bond spreads over time and empirically disentangle both effects. Using an extensive data set, we find that both idiosyncratic volatility and liquidity are critical mainly for the distress portfolios, i.e., low-rated and short-term bonds; for others only volatility matters. The effects of volatility and liquidity shocks on bond spreads were both exacerbated during the recent financial crisis. Liquidity shocks are quickly absorbed into bonds prices; however, volatility shocks are more persistent and have a long-term effect. Our results overall suggest significant differences between how volatility and liquidity dynamically impact bond spreads.     

市场摩擦对特质风险溢价的影响效应——基于A股主板市场的实证分析

与国外发达市场相比,我国A股主板市场的市场摩擦因素对市场微观结构和资产定价的影响更大。在防范和化解系统性风险的过程中,进一步分析市场摩擦如何作用于特质风险定价效应的问题具有重要的理论和现实意义。本文通过采用多维市场摩擦指标来代理信息不对称、交易成本、买卖限制、卖空限制、风险对冲和外部冲击,检验中国股市特质风险和预期收益率的关系,并判断出市场摩擦因素间的差异性影响机制。回归发现,市场摩擦和特质风险因子(特质波动率和特质偏度)都具有定价效应。各维度市场摩擦因素降低了股票流动性,进而增强了特质波动率的负向定价效应,部分解释了“特质波动率之谜”,但市场摩擦对特质偏度因子溢价的影响较为微弱。同时,基于特质波动率和特质偏度因子的投资策略能够产生超越CAPM、三因子和五因子模型的绝对收益,并印证了市场摩擦对特质风险因子绝对收益的影响作用。     

Firm age and realized idiosyncratic return volatility in China: The role of short-sales constraints

This paper investigates how realized idiosyncratic return volatility changes with firm age in the Chinese stock market. By employing a sample of 26,676 firm-year observations of 2798 A-share listed Chinese firms from 2001 to 2019, we find that realized idiosyncratic return volatility is negatively associated with firm age. Further, we find that loosening short-sales constraints strengthens this negative association, and that heterogeneity of investor beliefs is the most likely mechanism driving the negative relation, rather than the alternative explanations of cash flow volatility and growth options. Our results are fairly consistent under two different measures of firm age, and are robust to a choice of two multiple-factor models (the Fama-French three-factor and five-factor models) as well as two data frequencies (daily and monthly) used to estimate realized idiosyncratic return volatility.     

The joint cross-sectional variation of equity returns and volatilities

This paper analyzes the determinants of the simultaneous cross-sectional variation of return and volatility risk premia. Independently of the model specification employed, the estimated risk premium associated with the default premium beta is always positive and statistically different from zero. Moreover, the risk premium of the market volatility risk premium beta is negative and statistically significant. However, both risk factors are priced economically and statistically differently in the volatility and return segments of the market. On average, common factors in both segments explain 90% of the variability of volatility risk premium portfolios, but only 65% of the variability of equity return portfolios.     

Idiosyncratic volatility: An indicator of noise trading?

We investigate the market efficiency implications of firm-specific return variation measured by absolute idiosyncratic volatility. We find that the absolute idiosyncratic volatility (the variance of the residual from an asset-pricing model) displays a positive and robust relationship to mispricing, which reflects an increasing role of noise traders. Previous literature has produced similar – or opposing – results. We deepen our understanding of the previous conflicting results by showing that (1) market volatility by itself is associated with mispricing, (2) absolute idiosyncratic volatility is associated with mispricing even when controlling for market volatility, (3) the strength of the association between absolute idiosyncratic volatility and mispricing depends on the level of market volatility, and (4) absolute and relative measures of idiosyncratic volatility have opposing associations with mispricing. Our findings contribute to the existing literature by reconciling the mixed results for the relationship between idiosyncratic volatility and mispricing displayed in the previous literature.     

Return and Volatility Spillovers Between Large and Small Stocks in the UK

Abstract:  This paper investigates return and volatility spillover effects between the FTSE 100, FTSE 250 and FTSE Small Cap equity indices using the multivariate GARCH framework. We find that return and volatility transmission mechanisms between large and small stocks in the UK are asymmetric. In particular, there are significant spillover effects in both returns and volatility from the portfolios of larger stocks to the portfolios of smaller stocks. For volatility, there is also evidence of limited feedback from the portfolios of smaller stocks to the portfolios of larger stocks, although sub-period analysis suggests that this is to some extent period-specific. Simulation evidence shows that non-synchronous trading potentially explains some, but not all, of the spillover effects in returns, and that it explains none of the spillover effects in volatility. These results are consistent with a market in which information is first incorporated into the prices of large stocks before being impounded into the prices of small stocks.     

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.     

The return impact of realized and expected idiosyncratic volatility

We show that the negative relation between realized idiosyncratic volatility, measured over the prior month, and returns is robust in non-January months. Controlling for realized idiosyncratic volatility, we show that the relation between returns and expected idiosyncratic volatility is positive and robust. Realized and expected idiosyncratic volatility are separate and important effects describing the cross-section of returns. We find the negative return on a zero-investment portfolio that is long high realized idiosyncratic volatility stocks and short low realized idiosyncratic volatility stocks is dependent on aggregate investor sentiment. In cross-sectional tests, we find the negative relation is weaker for stocks with a large analyst following and stronger for stocks with high dispersion of analyst forecasts. The positive relation between expected idiosyncratic volatility and returns is not due to mispricing.     

Volatility co-movements between emerging sovereign bonds: Is there segmentation between geographical areas?

This paper analyses the dynamic interrelationship between spreads on selected sovereign bonds issued by 10 emerging countries. It investigates the nature of the volatility transmission in secondary bond markets through conditional covariance estimates obtained by orthogonal methods. This approach, which combines PCA with GARCH volatility modelling, filters away idiosyncratic news and focuses on spreads dynamics driven by common factors. We find convincing evidence of co-movements between spread changes; more within than across geographical areas. Conditional covariations increase in periods of turbulence and subsequently subside. The time-varying minimum variance artificial portfolios, which are used here for model validation, show that, in spite of systemic risk, international portfolio diversification is still a powerful strategy for risk reduction.