Abstract

http://ssrn.com/abstract=886717
 
 

Citations (75)



 


 



Idiosyncratic Volatility and the Cross-Section of Expected Returns


Turan G. Bali


Georgetown University - Robert Emmett McDonough School of Business

Nusret Cakici


Fordham University

December 2005


Abstract:     
This paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that (i) data frequency used to estimate idiosyncratic volatility, (ii) weighting scheme used to compute average portfolio returns, (iii) breakpoints utilized to sort stocks into quintile portfolios, and (iv) using a screen for size, price and liquidity play a critical role in determining the existence and significance of a relation between idiosyncratic risk and the cross-section of expected returns. Portfolio-level 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 there is no robust, significant relation between idiosyncratic volatility and expected returns.

Number of Pages in PDF File: 29

Keywords: idiosyncratic risk, total risk, expected stock returns, size, liquidity

JEL Classification: G10, G11, C13

working papers series


Download This Paper

Date posted: March 3, 2006  

Suggested Citation

Bali, Turan G. and Cakici, Nusret, Idiosyncratic Volatility and the Cross-Section of Expected Returns (December 2005). Available at SSRN: http://ssrn.com/abstract=886717 or http://dx.doi.org/10.2139/ssrn.886717

Contact Information

Turan G. Bali
Georgetown University - Robert Emmett McDonough School of Business ( email )
3700 O Street, NW
Washington, DC 20057
United States
(202) 687-5388 (Phone)
(202) 687-4031 (Fax)
HOME PAGE: http://faculty.msb.edu/tgb27/index.html

Nusret Cakici (Contact Author)
Fordham University ( email )
Fordham University
Graduate School of Business
New York, NY 10023
United States
2126366776 (Phone)
Feedback to SSRN


Paper statistics
Abstract Views: 2,440
Downloads: 905
Download Rank: 3,358
Citations:  75

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo8 in 0.328 seconds