scholarly journals Impact of Idiosyncratic Volatility on Average Stock Returns: Evidence from Sri Lanka

2020 ◽  
Vol 11 (2) ◽  
pp. 67
Author(s):  
H. A. P. K. Perera ◽  
T. C. Ediriwickrama
Keyword(s):  
Sri Lanka ◽  
Stock Returns ◽  
Idiosyncratic Volatility

Idiosyncratic volatility and average stock returns: evidence from Sri Lanka

2021 ◽  
Vol 11 (5) ◽  
pp. 740
Author(s):  
H.A.P.K. Perera ◽  
Tharindu Chamara Ediriwickrama
Keyword(s):  
Sri Lanka ◽  
Stock Returns ◽  
Idiosyncratic Volatility

scholarly journals Idiosyncratic volatility, option-based measures of informed trading, and investor attention

2021 ◽  
Author(s):  
Hannes Mohrschladt ◽  
Judith C. Schneider
Keyword(s):  
Stock Returns ◽  
Idiosyncratic Volatility ◽  
Investor Attention ◽  
Intuitive Idea ◽  
Joint Examination ◽  
Stock Market Investors ◽  
Private Investors ◽  
High Level ◽  
Do So ◽  
Option Market

AbstractWe establish a direct link between sophisticated investors in the option market, private stock market investors, and the idiosyncratic volatility (IVol) puzzle. To do so, we employ three option-based volatility spreads and attention data from Google Trends. In line with the IVol puzzle, the volatility spreads indicate that sophisticated investors indeed consider high-IVol stocks as being overvalued. Moreover, the option measures help to distinguish overpriced from fairly priced high-IVol stocks. Thus, these measures are able to predict the IVol puzzle’s magnitude in the cross-section of stock returns. Further, we link the origin of the IVol puzzle to the trading activity of irrational private investors as the return predictability only exists among stocks that receive a high level of private investor attention. Overall, our joint examination of option and stock markets sheds light on the behavior of different investor groups and their contribution to the IVol puzzle. Thereby, our analyses support the intuitive idea that noise trading leads to mispricing, which is identified by sophisticated investors and exploited in the option market.

Idiosyncratic volatility, the VIX and stock returns

2019 ◽  
Vol 47 ◽  
pp. 431-441 ◽  
Author(s):  
Mahmoud Qadan ◽  
Doron Kliger ◽  
Nir Chen
Keyword(s):  
Stock Returns ◽  
Idiosyncratic Volatility

The four-factor model and stock returns: evidence from Sri Lanka

2017 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Amal Peter Abeysekera ◽  
Pulukkuttige Don Nimal
Keyword(s):  
Sri Lanka ◽  
Stock Returns ◽  
Factor Model

On the Relation between EGARCH Idiosyncratic Volatility and Expected Stock Returns

2014 ◽  
Vol 49 (1) ◽  
pp. 271-296 ◽  
Author(s):  
Hui Guo ◽  
Haimanot Kassa ◽  
Michael F. Ferguson
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Return Time ◽  
Idiosyncratic Volatility ◽  
Model Parameters ◽  
Risk Return ◽  
Look Ahead ◽  
Monthly Return ◽  
Expected Stock Returns ◽  
Estimation Of Model Parameters

AbstractA spurious positive relation between exponential generalized autoregressive conditional heteroskedasticity (EGARCH) estimates of expected monthtidiosyncratic volatility and monthtstock returns arises when the monthtreturn is included in estimation of model parameters. We illustrate via simulations that this look-ahead bias is problematic for empirically observed degrees of stock return skewness and typical monthly return time series lengths. Moreover, the empirical idiosyncratic risk-return relation becomes negligible when expected monthtidiosyncratic volatility is estimated using returns only up to montht− 1.

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