The Many Faces of the Cross-sectional Relationship between Expected Returns and Expected Idiosyncratic Volatilities

2004 ◽  
Author(s):  
Chu Zhang
Keyword(s):  
Expected Returns ◽  
Cross Sectional ◽  
The Cross ◽  
The Many

The Cross Section of Expected Returns with MIDAS Betas

2011 ◽  
Vol 47 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Mariano González ◽  
Juan Nave ◽  
Gonzalo Rubio
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Market Risk ◽  
Ordinary Least Squares ◽  
Mixed Data ◽  
Expected Returns ◽  
Data Sampling ◽  
Cross Sectional ◽  
Market Risk Premium ◽  
The Cross

AbstractThis paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.

scholarly journals The Cross Section of Foreign Currency Risk Premia and Consumption Growth Risk: Comment

2011 ◽  
Vol 101 (7) ◽  
pp. 3456-3476 ◽  
Author(s):  
Craig Burnside
Keyword(s):  
Foreign Currency ◽  
Risk Premia ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Forward Premium Puzzle ◽  
Cross Sectional ◽  
Consumption Growth ◽  
Forward Premium ◽  
The Cross ◽  
Premium Puzzle

Lustig and Verdelhan (2007) argue that the excess returns to borrowing US dollars and lending in foreign currency “compensate US investors for taking on more US consumption growth risk,” yet the stochastic discount factor corresponding to their benchmark model is approximately uncorrelated with the returns they study. Hence, one cannot reject the null hypothesis that their model explains none of the cross sectional variation of the expected returns. Given this finding, and other evidence, I argue that the forward premium puzzle remains a puzzle. JEL: C58, E21, F31, G11, G12

scholarly journals On the Cross-sectional Relation between Expected Returns and Betas

1994 ◽  
Vol 49 (1) ◽  
pp. 101-121 ◽  
Author(s):  
RICHARD ROLL ◽  
STEPHEN A. ROSS
Keyword(s):  
Expected Returns ◽  
Cross Sectional ◽  
The Cross

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.

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>

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