Idiosyncratic volatility and cross‐sectional stock returns in Southeast Asian stock markets

We examine the role of idiosyncratic risk in five ASEAN markets of Malaysia, Singapore, Thailand, Indonesia, and the Philippines. Our research was motivated by the findings of Ang et al. (2006, 2009) of a ‘puzzling’ negative relation between idiosyncratic volatility and 1‐month ahead stock returns in developed markets and the suggestion of the ubiquity of these results in other markets. In contrast, we find no evidence of an idiosyncratic volatility puzzle in these Asian stock markets; instead, we document a positive relationship between idiosyncratic volatility and returns in Malaysia, Singapore, Thailand, and Indonesia and no relationship in the Philippines. The idiosyncratic volatility trading strategy could result in significant trading profits in Malaysia, Singapore, Thailand, and to some extent in Indonesia. Our study underscores the fact that generalizing empirical results obtained in developed stock markets to new and emerging markets could potentially be misleading.     

Is idiosyncratic volatility priced in cryptocurrency markets?

This paper investigates how idiosyncratic volatility is priced in the cross-section of cryptocurrency returns. By conducting both portfolio-level analysis and Fama-MacBeth regression analysis, we demonstrate that idiosyncratic volatility is positively related to the expected returns of cryptocurrencies. This finding is not subsumed by effects of size, momentum, liquidity, volume, and price and is robust to different weighting schemes, holding periods, and sample sizes. Besides, we find no evidence of temporal relation between idiosyncratic volatility and returns in cryptocurrency markets.     

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。     

Determinants of idiosyncratic volatility: Evidence from the Indian stock market

This paper investigates whether firm-specific characteristics explain idiosyncratic volatility in the stocks of non-financial firms traded in the Indian stock market. It employs the linear time series five-factor model, augmented with a liquidity factor and the conditional EGARCH model, to extract yearly idiosyncratic volatility. We estimate a panel data regression to quantify the relationship between firm-specific characteristics and the volatility of individual securities. The results show that idiosyncratic volatility is significant in emerging markets such as India, and that cross-sectional return variations of firms are associated with firm-specific characteristics such as firm size, book-to-market ratio, momentum, liquidity, cash flow-to-price ratio, and returns on assets. We find that the idiosyncratic risk documented in this study is associated with smaller size of company, higher liquidity, low momentum, high book-to-market ratio, and low cash flow-to-price ratio. The findings suggest need to develop alternative tools to make investment decisions in emerging markets.     

Dissecting the idiosyncratic volatility anomaly

The idiosyncratic volatility (IVOL) anomaly, documented in Ang, et al. (2006), has garnered a great deal of attention in the literature. Yet questions remain regarding the robustness and pervasiveness of the IVOL anomaly, with a particular concern that the IVOL anomaly might simply be the manifestation of market microstructure effect. In this paper, we show that the IVOL anomaly is strong and pervasive after we exclude stocks most susceptible to market microstructure noise — such as microcap stocks, penny stocks, and stocks with strong short-term return reversal. These results are robust to equal-weighting or value-weighting stocks in the IVOL portfolios. Our findings suggest that rather than being the cause of the anomaly, market microstructure noise actually weakens the IVOL anomaly.     

Time-varying moments, idiosyncratic risk, and an application to hot-issue IPO aftermarket returns

This paper proposes time-varying idiosyncratic risk as a component driving conditional abnormal returns and outlines a corresponding Engle et al. [Econometrica 55 (1987) 391] ARCH-M market model. An application is given to initial public offering (IPO) aftermarket stock returns, where a positive relation between idiosyncratic risk and returns is consistent with young issues’ equity as a contingent claim on firm assets. The empirical results for an illustrative sample of German Neuer Markt stocks traded during the first two years after initial listing indicate pronounced skewness as well as a positive relation between conditional idiosyncratic risk and expected returns. Conditioning aftermarket performance on risk yields much lower levels of abnormal return significance than a standard approach.     

Have European Stocks become More Volatile? An Empirical Investigation of Idiosyncratic and Market Risk in the Euro Area

We examine the dynamics of idiosyncratic risk, market risk and return correlations in European equity markets using weekly observations from 3515 stocks listed in the 12 euro area stock markets over the period 1974–2004. Similarly to Campbell et al. (2001) , we find a rise in idiosyncratic volatility, implying that it now takes more stocks to diversify away idiosyncratic risk. Contrary to the US, however, market risk is trended upwards in Europe and correlations are not trended downwards. Both the volatility and correlation measures are pro‐cyclical, and they rise during times of low market returns. Market and average idiosyncratic volatility jointly predict market wide returns, and the latter impact upon both market and idiosyncratic volatility. This has asset pricing and risk management implications.     

The MAX effect: European evidence

The maximum daily return over the previous month (MAX) of Bali et al. (2011) is a strong and significant predictor of future stock returns in non-U.S. equity markets. Once it is controlled for MAX in the cross-section of average returns, the puzzling negative idiosyncratic volatility-return relation disappears. Consistent with the assumption that MAX is the true effect, for which idiosyncratic volatility is just a proxy, we find that MAX can be traced back to firm fundamentals in the manner of idiosyncratic volatility. The negative MAX-return relation is stronger among firms with high cash flow volatility and weaker among firms with high profitability.     

Global investigation on the country-level idiosyncratic volatility and its determinants

Adapting the Fama–French three-factor model to a global context, this paper investigates idiosyncratic volatility as a measure of country-specific risk, and explores its determinants by using the equity and risk data of 47 developed and emerging countries during the period 1995–2016. We find the stock market turnover to have a positive and significant impact on the country-level idiosyncratic volatility, while information disclosure and investor uncertainty avoidance degree are negatively associated with country-level idiosyncratic risk. Moreover, improvements in economic, financial, and political risks, as measured by GDP growth, FX stability, foreign debt health, and non-corruption degree decrease the country-level idiosyncratic volatility significantly. Among all sets of market structure, investor preference, and economic, financial, and political risk variables considered, we find financial risk factors, FX stability and foreign debt health, to have the highest explanatory power over the cross-sectional differences in country-level idiosyncratic risk.     

Relation between time-series and cross-sectional effects of idiosyncratic variance on stock returns

Consistent with the post-1962 US evidence by Ang et al. [Ang, A., Hodrick, R., Xing Y., Zhang, X., 2006. The cross-section of volatility and expected returns. Journal of Finance 51, 259–299], we find that stocks with high idiosyncratic variance (IV) have low CAPM-adjusted expected returns in both pre-1962 US and modern G7 data. We also test in three ways the conjecture that IV is a proxy of systematic risk. First, the return difference between low and high IV stocks – that we dub as IVF – is a priced factor in the cross-section of stock returns. Second, loadings on lagged market variance and lagged average IV account for a significant portion of variation in average returns on portfolios sorted by IV. Third, the variance of IVF correlates closely with average IV, and the two variables have similar explanatory power for the time-series and cross-sectional stock returns.     

Trend in aggregate idiosyncratic volatility

We suggest that price interaction among stocks is an important determinant of idiosyncratic volatility. We demonstrate that as more (less) stocks are listed in the markets, price interaction among stocks increases (decreases), and hence stocks, on average, become more (less) volatile. Our results show that price interaction has a significant positive effect of idiosyncratic volatility. The results of various robustness checks indicate that the effect of price interaction is still significant to the presence of liquidity, newly listed firms, cash flow variables, business cycle variables, and market volatility. Once the price interaction effect is taken into account, no trend remains in idiosyncratic volatility. We conclude that there is no trend, but a reflection of the positive effect of price interaction on idiosyncratic volatility.     

Explaining the idiosyncratic volatility puzzle using Stochastic Discount Factors

I use Stochastic Discount Factors to examine the sources of the idiosyncratic volatility premium. I find that non-zero risk aversion and firms’ non-systematic coskewness determine the premium on idiosyncratic volatility risk. The firm’s non-systematic coskewness measures the comovement of the asset’s volatility with the market return. When I control for the non-systematic coskewness factor, I find no significant relation between idiosyncratic volatility and stock expected returns. My results are robust across different sample periods and firm characteristics.     

Incomplete information,idiosyncratic volatility and stock returns

When investors have incomplete information, expected returns, as measured by an econometrician, deviate from those predicted by standard asset pricing models by including a term that is the product of the stock’s idiosyncratic volatility and the investors’ aggregated forecast errors. If investors are biased this term generates a relation between idiosyncratic volatility and expected stocks returns. Relying on forecast revisions from IBES, we construct a new variable that proxies for this term and show that it explains a significant part of the empirical relation between idiosyncratic volatility and stock returns.     

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%.     

Are idiosyncratic volatility and MAX priced in the Canadian market?

The negative relationship between realized idiosyncratic volatility (RIvol) and future returns uncovered by Ang et al. (2006) for the U.S. market has been attributed to return reversals. For the Canadian market where return reversals are considerably less important, we find that RIvol is positively related to future returns, even after controlling for risk loadings, illiquidity and reversals. Unlike the findings of Bali et al. (2001) for the U.S. market, we find that the relationship between extreme positive returns (MAX) and future returns for the Canadian market is positive and that idiosyncratic volatility continues to be consistently positively related to future returns after controlling for MAX. We find evidence that suggests that reversals for stocks with extreme daily returns are confined to (typically small) stocks with low institutional holdings.     

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.     

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.

High idiosyncratic volatility and low returns: International and further U.S. evidence

Stocks with recent past high idiosyncratic volatility have low future average returns around the world. Across 23 developed markets, the difference in average returns between the extreme quintile portfolios sorted on idiosyncratic volatility is -1.31%$-1.31\%$ per month, after controlling for world market, size, and value factors. The effect is individually significant in each G7 country. In the United States, we rule out explanations based on trading frictions, information dissemination, and higher moments. There is strong covariation in the low returns to high-idiosyncratic-volatility stocks across countries, suggesting that broad, not easily diversifiable factors lie behind this phenomenon.     

Estimating the expected marginal rate of substitution: A systematic exploitation of idiosyncratic risk

We develop a methodology to estimate the shadow risk free rate or expected intertemporal marginal rate of substitution, “EMRS”. Our technique relies upon exploiting idiosyncratic risk, since theory dictates that idiosyncratic shocks earn the EMRS. We apply our methodology to recent monthly and daily data sets for the New York and Toronto Stock Exchanges. We estimate EMRS with precision and considerable time-series volatility, subject to an identification assumption. Both markets seem to be internally integrated; different assets traded on a given market share the same EMRS. We reject integration between the stock markets, and between stock and money markets.