Idiosyncratic Volatility and the Cross Section of Expected Returns

2008 ◽  
Vol 43 (1) ◽  
pp. 29-58 ◽  
Author(s):  
Turan G. Bali ◽  
Nusret Cakici
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Idiosyncratic Volatility ◽  
Expected Returns ◽  
Cross Sectional ◽  
Monthly Data ◽  
Portfolio Returns ◽  
Expected Stock Returns ◽  
The Cross ◽  
Data Frequency

AbstractThis 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.

scholarly journals Idiosyncratic Volatility and Cross-section of Stock Returns: Evidences from India

2016 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Prashant Sharma ◽  
Brajesh Kumar
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Empirical Relationship ◽  
Idiosyncratic Volatility ◽  
Expected Returns ◽  
Cross Sectional ◽  
The Cross ◽  
Highly Correlated ◽  
Indian Stock Market ◽  
The Relationship

<p>The present study examines the cross-sectional pricing ability of idiosyncratic volatility (IV) in Indian stock market and investigates the relationship amongst expected idiosyncratic volatility (EI), unexpected idiosyncratic volatility (UI), and cross-section of stocks returns. The study uses ARIMA (2, 0, 1) model to IV into EI and UI. The stocks returns are regressed on IV, EI and UI using Newey-West (1987) corrections, in order to investigate their empirical relationship.  The study finds that IV is positively related with stock returns. Further the IV significantly explains the cross-section of stock returns in Indian context. After imposing control over UI, as it is highly correlated with unexpected returns, the inter-temporal relationship between EI and expected returns turns out to be positive.</p>

Can the Cross-Sectional Variation in Expected Stock Returns Explain Momentum?

2009 ◽  
Vol 44 (4) ◽  
pp. 777-794 ◽  
Author(s):  
George Bulkley ◽  
Vivekanand Nawosah
Keyword(s):  
Stock Returns ◽  
Expected Returns ◽  
Sample Period ◽  
Expected Return ◽  
Cross Sectional ◽  
Holding Period ◽  
Expected Stock Returns ◽  
The Cross

AbstractIt has been hypothesized that momentum might be rationally explained as a consequence of the cross-sectional variation of unconditional expected returns. Stocks with relatively high unconditional expected returns will on average outperform in both the portfolio formation period and in the subsequent holding period. We evaluate this explanation by first removing unconditional expected returns for each stock from raw returns and then testing for momentum in the resulting series. We measure the unconditional expected return on each stock as its mean return in the whole sample period. We find momentum effects vanish in demeaned returns.

scholarly journals A Model-Free Measure of Aggregate Idiosyncratic Volatility and the Prediction of Market Returns

2014 ◽  
Vol 49 (5-6) ◽  
pp. 1133-1165 ◽  
Author(s):  
René Garcia ◽  
Daniel Mantilla-García ◽  
Lionel Martellini
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Strong Predictor ◽  
Idiosyncratic Volatility ◽  
Cross Sectional ◽  
Volatility Risk ◽  
Model Free ◽  
Asymptotically Efficient Estimator ◽  
The Cross ◽  
Asymptotically Efficient

AbstractIn this paper, we formally show that the cross-sectional variance of stock returns is a consistent and asymptotically efficient estimator for aggregate idiosyncratic volatility. This measure has two key advantages: It is model free and observable at any frequency. Previous approaches have used monthly model-based measures constructed from time series of daily returns. The newly proposed cross-sectional volatility measure is a strong predictor for future returns on the aggregate stock market at the daily frequency. Using the cross section of size and book-to-market portfolios, we show that the portfolios’ exposures to the aggregate idiosyncratic volatility risk predict the cross section of expected returns.

Predicting Equity Returns in Developed Markets

2021 ◽  
pp. 68-90
Author(s):  
A. Doruk Günaydin
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Developed Countries ◽  
Equity Returns ◽  
Market Returns ◽  
Short Term ◽  
Firm Level ◽  
Cross Sectional ◽  
Expected Stock Returns ◽  
The Cross

This chapter examines the relation between various firm-specific variables and the cross-section of equity returns in 26 developed countries. Univariate portfolio analyses using equal-weighted returns show that low beta, book-to-market equity, and momentum analysis are also priced in the cross-section of developed market returns, whereas short-term reversal and downside beta manifest themselves in the opposite direction. Univariate portfolio analysis based on value-weighted returns reveal that the predictive power of book-to-market equity and short-term reversal is driven by small stocks. Multivariate firm-level cross-sectional regression analysis document that momentum, short-term reversal, illiquidity, idiosyncratic volatility, hybrid tail risk, lower partial moment are related to expected stock returns. Overall, the most robust cross-sectional predictor in developed market is found to be return momentum.

scholarly journals The cross-section of expected stock returns and components of idiosyncratic volatility

2021 ◽  
Author(s):  
◽  
Seyed Reza Tabatabaei Poudeh
Keyword(s):  
Risk Factors ◽  
Stock Returns ◽  
Cross Section ◽  
Idiosyncratic Volatility ◽  
Firm Level ◽  
Cross Sectional ◽  
Expected Stock Returns ◽  
Book To Market Ratio ◽  
Alpha Risk ◽  
The Relationship

We examine the relationship between stock returns and components of idiosyncratic volatility—two volatility and two covariance terms— derived from the decomposition of stock returns variance. The portfolio analysis result shows that volatility terms are negatively related to expected stock returns. On the contrary, covariance terms have positive relationships with expected stock returns at the portfolio level. These relationships are robust to controlling for risk factors such as size, book-to-market ratio, momentum, volume, and turnover. Furthermore, the results of Fama-MacBeth cross-sectional regression show that only alpha risk can explain variations in stock returns at the firm level. Another finding is that when volatility and covariance terms are excluded from idiosyncratic volatility, the relation between idiosyncratic volatility and stock returns becomes weak at the portfolio level and disappears at the firm level.

Good Volatility, Bad Volatility, and the Cross Section of Stock Returns

2019 ◽  
Vol 55 (3) ◽  
pp. 751-781 ◽  
Author(s):  
Tim Bollerslev ◽  
Sophia Zhengzi Li ◽  
Bingzhi Zhao
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
High Frequency ◽  
Firm Characteristics ◽  
Explanatory Variables ◽  
Portfolio Returns ◽  
Expected Stock Returns ◽  
Intraday Data ◽  
The Cross ◽  
The Individual

Based on intraday data for a large cross section of individual stocks and newly developed econometric procedures, we decompose the realized variation for each of the stocks into separate so-called realized up and down semi-variance measures, or “good” and “bad” volatilities, associated with positive and negative high-frequency price increments, respectively. Sorting the individual stocks into portfolios based on their normalized good minus bad volatilities results in economically large and highly statistically significant differences in the subsequent portfolio returns. These differences remain significant after controlling for other firm characteristics and explanatory variables previously associated with the cross section of expected stock returns.

scholarly journals The Cross-Section of Expected Stock Returns in Brazil

2016 ◽  
Vol 14 (2) ◽  
pp. 151
Author(s):  
Gyorgy Varga ◽  
Ricardo Dias de Oliveira Brito
Keyword(s):  
Risk Factors ◽  
Stock Market ◽  
Stock Returns ◽  
Cross Section ◽  
Explanatory Power ◽  
Positive Relation ◽  
Cross Sectional ◽  
Expected Stock Returns ◽  
The Cross

In a sample of the Brazilian stock market from 1999 to 2015, this paper shows that the book-to-market and momentum of individual firms capture some of the cross-sectional variation in average stock returns, while the market β and size do not play a role. The positive relation of cross-section of returns with book-to-market is more evident earlier, while the positive relation with momentum is stronger later in the sample. However, because none of these characteristics show explanatory power for all the subsamples studied, we are not fully convinced that they capture fundamental risk factors.

Sign in / Sign up

Export Citation Format

Share Document