Idiosyncratic Volatility and the Cross Section of Expected Returns

By Turan G. Bali, Nusret Cakici. Journal of Financial and Quantitative Analysis, 2008

847 citations recorded by the source corpus.

Abstract This paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that i) the data frequency used to estimate idiosyncratic volatility, ii) the weighting scheme used to compute average portfolio returns, iii) the breakpoints utilized to sort stocks into quintile portfolios, and iv) using a screen for size, price, and liquidity play critical roles in determining the existence and significance of a relation between idiosyncratic risk and the cross section of expected returns. Portfoliolevel analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse volatility-weighted), three breakpoints (CRSP, NYSE, equal market share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that no robustly significant relation exists between idiosyncratic volatility and expected returns.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/9711c9e94d57084744bdc6e0078ab6ba42d84208

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Citations in the corpus

Return Reversals, Idiosyncratic Risk, and Expected Returns (2009, positive): “Bali and Cakici (2008) compare the estimates from different data frequency and find that the realized monthly idiosyncratic volatility from daily data is subject to market microstructure biases. Using the conditional idiosyncratic volatility estimates from GARCH (1, 1) and EGARCH (1, 1) models as be”
Charles A. Dice Center for Research in Financial Economics The Implied Cost of Capital: A New Approach (2010, neutral): “…2011), retail trading proportion (Han and Kumar, 2013), one-month return reversal (Fu, 2009; Huang, Liu, Rhee, and Zhang, 2009), illiquidity (Bali and Cakici, 2008; Han and Lesmond, 2011), uncertainty (Johnson, 2004), average variance beta (Chen and Petkova, 2012), and earnings surprises…”
The empirical risk–return relation: A factor analysis approach ☆ (2006, neutral): “…a broad cross-section of industry equity returns, returns on 100 portfolios of equities sorted into ten size and ten book-tomarket categories (Fama and French, 1992), and a group of variables we call ‘‘riskfactors,’’ since they have been used in cross-sectional or time-series studies to…”
On the Scope and Drivers of the Asset Growth Effect (2011, neutral): “We obtained the estimates of the Gibbs sampler estimate from Joel Hasbrouck. We denote this measure GIBBS. We do not use measures of quoted or effective spreads because of the lack of necessary high-frequency data, which are only available for a relatively short time series. The indirect measures we”
Gambling Preference and the New Year Effect of Assets with Lottery Features (2012, result): “Using equal-weighted returns, however, this relative underperformance of lottery-type stocks diminishes,, consistent with Bali and Cakici (2008).”
Customer Satisfaction and Stock Returns Risk (2009, positive): “This is especially important because prior work in finance has shown that conclusions drawn from an analysis of abnormal returns and risk measures can change when factors such as sampling aspects and models used to measure returns and/or risk are changed (see Bali and Cakici 2008; Fama 1998).”
Stock Returns in Relation to Environmental, Social and Governance Performance: Mispricing or Compensation for Risk? (2011, methodology): “…is assessed using cross-sectional regression of excess stock returns on ESG scores and four factors known to explain stock returns: beta, size, value, and momentum, i.e., the Fama-French three-factor model (Fama and French, 1992) plus the momentum factor identified by Jegadeesh and Titman (1993).”
An Estimation of Economic Models with Recursive (2007, positive): “We compare the specification errors of the estimated EZW recursive utility model to those of the time-separable, constant relative risk aversion (CRRA) power utility model (3) and to two alternative asset pricing models that have been studied in the literature: the three-factor, portfolio-based asse”
Costly Arbitrage and Idiosyncratic Risk: Evidence from Short Sellers (2010, positive): “This result is consistent with Bali and Cakici (2008) who argue that there is no robust relation between idiosyncratic risk and subsequent returns.”
Liquidity Biases and the Pricing of Cross-Sectional Idiosyncratic Volatility around the World (2015, neutral): “This is exemplified in the findings of Bali and Cakici (2008) who show that controlling for price and size”
World market risk, country-specific risk and expected returns in international stock markets (2010, neutral): “Bali and Cakici (2008) shed light on the methodological differences in previous studies that mainly led the existing literature to present conflicting evidence.”
Fund management, intellectual capital, intangibles and private disclosure (2006, neutral): “This has increased the ability of companies to change the rules of competition and increased the chance of corporate failure. As intangibles such as knowledge and innovation have become an increasingly important part of corporate value then this has exacerbated the problem of how to disclose the val”
Idiosyncratic Risk and the Cross-Section of Expected Stock Returns (2009, positive): “Bali and Cakici (2008) suggest that AHXZ’s results are sensitive to: (i) data frequency used to estimate idiosyncratic volatility, (ii) weighting schemes used to compute average portfolio returns, (iii) breakpoints utilized to sort stocks into quintile portfolios, and (iv) using a screen for size, p”
The Volatility Effect in Emerging Markets (2013, neutral): “For example, Bali and Cakici (2008) argue that the negative empirical relation between risk and return is driven by small-caps, especially the strong negative returns of high (idiosyncratic) volatility stocks.”