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 Forecasting using cross-section average–augmented time series regressions

2020 ◽  
Author(s):  
Hande Karabiyik ◽  
Joakim Westerlund
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Regression Models ◽  
Common Factor ◽  
Principal Component ◽  
Small Sample ◽  
Interactive Effects ◽  
Cross Sectional ◽  
Second Development ◽  
The Cross

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much attention is concerned with the use of cross-section averages as common factor estimates in interactive effects panel regression models. The main justification for this second development is the simplicity and good performance of the cross-section averages when compared with estimated principal component factors. In view of this, it is quite surprising that no one has yet considered the use of cross-section averages for forecasting. Indeed, given the purpose to forecast the conditional mean, the use of the cross-sectional average to estimate the factors is only natural. The present paper can be seen as a reaction to this. The purpose is to investigate the asymptotic and small-sample properties of forecasts based on cross-section average–augmented regressions. In contrast to most existing studies, the investigation is carried out while allowing the number of factors to be unknown.

The Determinants of Corporate CDS Spreads: Do Equity Liquidity and Jump Matter?

2014 ◽  
Vol 22 (3) ◽  
pp. 565-595
Author(s):  
Yuen Jung Park ◽  
Jungmu Kim
Keyword(s):  
Time Series ◽  
Stock Return ◽  
Global Financial Crisis ◽  
Explanatory Power ◽  
Firm Level ◽  
Cross Sectional ◽  
Stock Return Volatility ◽  
The Cross ◽  
Market Variables ◽  
The Global Financial Crisis

This paper investigates whether equity liquidity and stock return jump are important determinants for the Korean corporate CDS spreads. The previous studies mainly have examined the determinants of CDS spread time series levels, whereas this study focuses on the determinants of changes or differences of CDS spread time series as well as the effecting factors of cross-sectional variations. Using monthly averaged CDS quotes for 29 firms from Jan. 2005 to Nov. 2012, we first demonstrate that the explanatory power for CDS spread changes is improved to about 39% by adding both credit risk-related market variables and firm-level jump variables, contrary to the low explanatory power (approximately 21%) reported by the previous study. However, since the principle component analysis for residuals from the regression shows that a common risk factor exists, it is possible that additional important factor remains. In addition, we demonstrate that stock return volatility is a robust variable to explain the cross-sectional differences in CDS spreads. We also find that the equity liquidity is a robust and significant factor for the cross-sectional differences in CDS spreads after the global financial crisis period. The result implies that, after the recent crisis, investors more actively considered equity illiquidity costs when they hedged their CDS exposures by stocks.

On a model for a time series of cross-sections

1986 ◽  
Vol 23 (A) ◽  
pp. 113-125 ◽  
Author(s):  
P. M. Robinson
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Sections ◽  
Standard Form ◽  
Cross Sectional ◽  
Aggregated Data ◽  
Section Data ◽  
Infinite Population ◽  
The Cross ◽  
Observation Times

Dynamic stationary models for mixed time series and cross-section data are studied. The models are of simple, standard form except that the unknown coefficients are not assumed constant over the cross-section; instead, each cross-sectional unit draws a parameter set from an infinite population. The models are framed in continuous time, which facilitates the handling of irregularly-spaced series, and observation times that vary over the cross-section, and covers also standard cases in which observations at the same regularly-spaced times are available for each unit. A variety of issues are considered, in particular stationarity and distributional questions, inference about the parameter distributions, and the behaviour of cross-sectionally aggregated data.

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.

On the Scope and Drivers of the Asset Growth Effect

2011 ◽  
Vol 46 (6) ◽  
pp. 1651-1682 ◽  
Author(s):  
Marc L. Lipson ◽  
Sandra Mortal ◽  
Michael J. Schill
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Negative Correlation ◽  
Small Firms ◽  
Explanatory Power ◽  
Analyst Forecasts ◽  
Growth Effect ◽  
Idiosyncratic Volatility ◽  
Asset Growth ◽  
Return Effect

AbstractRecent papers have debated whether the negative correlation between measures of firm asset growth and subsequent returns is of little importance since it applies only to small firms, is justified as compensation for risk, or is evidence of mispricing. We show that the asset growth effect is pervasive, and evidence to the contrary arises due to specification choices; that one measure of asset growth, the change in total assets, largely subsumes the explanatory power of other measures; that the ability of asset growth to explain either the cross section of returns or the time series of factor loadings is linked to firm idiosyncratic volatility (IVOL); that the return effect is concentrated around earnings announcements; and that analyst forecasts are systematically higher than realized earnings for faster growing firms. In general, there appears to be no asset growth effect in firms with low IVOL. Our findings are consistent with a mispricing-based explanation for the asset growth effect in which arbitrage costs allow the effect to persist.

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 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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