The Bid-Ask Bounce Effect and the Pricing of Cross-Sectional Idiosyncratic Volatility: An Australian Study

2015 ◽  
Author(s):  
Bin Liu ◽  
Michael J. Dempsey ◽  
Monica Tan
Keyword(s):  
Idiosyncratic Volatility ◽  
Australian Study ◽  
Cross Sectional

scholarly journals Is idiosyncratic risk ignored in asset pricing: Sri Lankan evidence?

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Moinak Maiti
Keyword(s):  
Factor Model ◽  
Stock Exchange ◽  
Investment Decision ◽  
Idiosyncratic Risk ◽  
Idiosyncratic Volatility ◽  
Sri Lankan ◽  
Cross Sectional ◽  
Volatility Risk ◽  
And Performance ◽  
Three Factor

AbstractThe present study focused on one of the important South Asian nations—Sri Lanka—to examine the role of idiosyncratic volatility in asset prices. A four-factor model with idiosyncratic volatility was designed for capturing the market, size, value and idiosyncratic risk yields better than Fama and French’s (J Financ Econ 33:3–56, 1993) three-factor model and performance of the model. Fama–MacBeth’s cross-sectional regression, residual graphs and GRS test all confirm the superiority of four-factor model over 2 three-factor models. For all MC- and IVOL-based portfolios, idiosyncratic volatility is negatively related to the expected returns and positively related for all PB-based portfolios. Finally, study findings confirm that there is a high importance for idiosyncratic volatility risk factor while considering investment decision in Colombo stock exchange. Hence, investor should compensate for holding such risk factors in the portfolio.

Using Stocks or Portfolios in Tests of Factor Models

2019 ◽  
Vol 55 (3) ◽  
pp. 709-750 ◽  
Author(s):  
Andrew Ang ◽  
Jun Liu ◽  
Krista Schwarz
Keyword(s):  
Asset Pricing ◽  
Risk Premia ◽  
Factor Models ◽  
Idiosyncratic Volatility ◽  
Standard Errors ◽  
Coefficient Estimates ◽  
Pricing Models ◽  
Cross Sectional ◽  
Factor Loadings ◽  
Asset Pricing Models

We examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.

What explains the change in a firm’s idiosyncratic volatility after a dividend initiation?

2015 ◽  
Vol 41 (11) ◽  
pp. 1138-1158 ◽  
Author(s):  
Chintal A. Desai ◽  
Khoa H Nguyen
Keyword(s):  
Payout Policy ◽  
Free Cash Flow ◽  
Idiosyncratic Volatility ◽  
Cross Sectional ◽  
Content Type ◽  
Information Effects ◽  
Stage Analysis ◽  
Sectional Analysis ◽  
Dividend Tax ◽  
Robustness Check

Purpose – The purpose of this paper is to identify three (maturity, agency, and information) effects that help explain the change in idiosyncratic volatility after a firm initiates a dividend. Design/methodology/approach – The paper uses a cross-sectional analysis where the standard errors are adjusted for heteroskedasticity. As for robustness check, the authors perform two-stage analysis to control for potential self-selection bias. The authors also control for 2003 Dividend Tax Cut effect, matching-firm volatility, and confounding events. Findings – Using a sample of 688 dividend-initiating firms for a period of 1977 to 2010, the authors find evidence consistent with the hypotheses based on the maturity, agency, and information effects. The volatility changes upon the dividend initiation can be reliably explained by the changes in profit volatility and free cash flow per total assets, and whether the firm consummated a stock split prior to the dividend initiation. The information effect is also found to be economically significant. Originality/value – By studying a firm’s decision to initiate a dividend and its impact on the change in its volatility, the research helps contribute to the payout policy and volatility literatures.

Cross-Sectional Return Predictability in Taiwan Stock Exchange: An Empirical Investigation

2014 ◽  
Vol 17 (02) ◽  
pp. 1450010 ◽  
Author(s):  
Nusret Cakici ◽  
Kudret Topyan ◽  
Chia-Jane Wang
Keyword(s):  
Cash Flow ◽  
Predictive Power ◽  
Empirical Investigation ◽  
Stock Exchange ◽  
Return Predictability ◽  
Idiosyncratic Volatility ◽  
Short Term ◽  
Cross Sectional ◽  
Multiple Regressions ◽  
Taiwan Stock Exchange

This paper provides an analysis of the effectiveness of certain return predictors in Taiwan Stock Exchange (TWSE) from January 1990 to December 2011 by employing both portfolio method and cross-sectional regressions. While we found no statistically significant predictive power of beta, total volatility, and idiosyncratic volatility the two cheapness variables, book-to-market (BKMT) and cash-flow-to-price (FPR) ratios showed strong consistent economically and statistically significant predictive powers. In addition, our multiple regressions found predictive power in total volatility, short-term reversal (STREV), and market capitalization in the set of small stocks, while our all stock set showed predictive power only in total volatility and STREV.

The Determinants of Idiosyncratic Volatility

2017 ◽  
Vol 25 (4) ◽  
pp. 509-545
Author(s):  
Jaeuk Khil ◽  
Song Hee Kim ◽  
Eun Jung Lee
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Cross Section ◽  
Growth Rates ◽  
Idiosyncratic Volatility ◽  
Return Volatility ◽  
Firm Level ◽  
Cross Sectional ◽  
Asset Growth ◽  
The Cross

We investigate the cross-sectional and time-series determinants of idiosyncratic volatility in the Korean market. In particular, we focus on the empirical relation between firms’ asset growth rate and idiosyncratic stock return volatility. We find that, in the cross-section, companies with high idiosyncratic volatility tend to be small and highly leveraged, have high variance of ROE and Market to Book ratio, high turnover rate, and pay no dividends. Furthermore, firms with extreme (either high positive or negative) asset growth rates have high idiosyncratic return volatility than firms with moderate growth rates, suggesting the V-shaped relation between asset growth rate and idiosyncratic return volatility. We find that the V-shaped relation is robust even after controlling for other factors. In time-series, we find that firm-level idiosyncratic volatility is positively related to the dispersion of the cross-sectional asset growth rates. As a result, this study is contributed to show that the asset growth is the most important predictor of firm-level idiosyncratic return volatility in both the cross-section and the time-series in the Korean stock market. In addition, we show how the effect of risk factors varies with industries.

scholarly journals Sexual assault and general body injuries: A detailed cross-sectional Australian study of 1163 women

2017 ◽  
Vol 279 ◽  
pp. 112-120 ◽  
Author(s):  
Renate R. Zilkens ◽  
Debbie A. Smith ◽  
Maire C. Kelly ◽  
S. Aqif Mukhtar ◽  
James B. Semmens ◽  
...  
Keyword(s):  
Sexual Assault ◽  
Australian Study ◽  
Cross Sectional ◽  
General Body

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.

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