A Firm-Level Analysis of the Cross-Sectional Relation between Expected Returns and Expected Idiosyncratic Volatility in the Korean Stock Market

2019 ◽  
Vol 32 (4) ◽  
pp. 513-561
Author(s):  
Sangkyu Lee ◽  
◽  
Young Sik Kim
Keyword(s):  
Stock Market ◽  
Idiosyncratic Volatility ◽  
Expected Returns ◽  
Firm Level ◽  
Cross Sectional ◽  
Korean Stock Market ◽  
The Cross ◽  
Level Analysis

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.

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>

Cross-Sectional Expected Returns and Predictability in the Korean Stock Market

2019 ◽  
Vol 56 (15) ◽  
pp. 3763-3784
Author(s):  
Toyoung Kim ◽  
Tong Suk Kim ◽  
Yuen Jung Park
Keyword(s):  
Stock Market ◽  
Expected Returns ◽  
Cross Sectional ◽  
Korean Stock Market

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.

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.

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