Essays on Idiosyncratic Volatility and Asset Pricing

In this thesis, I study three aspects of idiosyncratic volatility. First, I examine the relation between idiosyncratic volatility and future stock returns. Next, I examine the share price effect and its interaction with the idiosyncratic volatility on stock returns. Finally, I examine the time series pattern of monthly aggregate monthly idiosyncratic volatility. In the first chapter, I examine the relation between idiosyncratic volatility and future stock returns. In their paper, Ang, Hodrick, Xing, and Zhang [AHXZ (2006)] show that idiosyncratic volatility is inversely related to future stock returns: low idiosyncratic volatility stocks earn higher returns than do high idiosyncratic volatility stocks. The main contribution of this paper is to provide evidence that it is the month to month changes in idiosyncratic volatility that produce AHXZ’s results. More specifically, a portfolio of stocks that move from Quintile 1 (low idiosyncratic volatility) to Quintile 5 (high idiosyncratic volatility) earns an average risk-adjusted return of 5.64% per month in the month of the change. Whereas, a portfolio of stocks that move from the highest to the lowest idiosyncratic volatility quintiles earns -0.94% per month in the month of the change. Eliminating all firm-month observations with idiosyncratic volatility quintile changes, I find the opposite results to AHXZ: it is persistently low idiosyncratic volatility stocks that earn lower returns than do persistently high idiosyncratic volatility stocks. I find that many of the extreme changes in idiosyncratic volatility are related to business events. In general, the pattern usually observed is that an announcement or an event increases uncertainty about a stock and hence, its idiosyncratic volatility increases. After the event, uncertainty is resolved and the stock returns to a lower idiosyncratic volatility quintile. In the second chapter, I examine how the level of the share price interacts with idiosyncratic volatility to affect future stock returns. Ignoring transaction costs, a trading strategy that is long high-priced and short low-priced stocks earns positive abnormal returns with respect to the Fama-French (1992) three factor model. However, the observed positive abnormal returns are less significant if momentum is taken into account via the Carhart (1997) four factor model. Also the relation between idiosyncratic volatility and future stock returns differs for price sorted portfolios: it is negative for low and mid-priced stocks but positive for high-priced ones. These results are robust for low and-mid-priced stocks even after momentum is included. However, the positive relation for high-priced stocks disappears due to relatively large loadings on momentum for high idiosyncratic volatility stocks. I also show that skewness and momentum are significant determinants of idiosyncratic volatility for low-priced stocks and high-priced stocks respectively. One implication is that the importance of idiosyncratic volatility for future stock returns may in part be due its role as a disguised risk factor: either for momentum for high-priced stocks and skewness for low and mid-priced stocks. In the third chapter, I investigate the time series pattern of aggregate monthly idiosyncratic volatility. It has been shown that new riskier listings in the US stock markets are a reason for the increase in idiosyncratic volatility during the period 1963-2004. First, I show that this is more pronounced for Nasdaq new listings. Second, I show that for Nasdaq, prior to 1994 low-priced new listings became riskier, whereas during the internet bubble period it is the higher-priced listings that became riskier. Third, I show that institutional holdings have increased over time and have had a different impact on each new listing group: a negative for pre-1994 listings and a positive impact for post-1994 listings. Hence, I conclude that the observed time-series pattern of idiosyncratic volatility is a result of the changing nature of Nasdaq’s investor clientele.

Advisor: White, Alan