Idiosyncratic volatility, aggregate volatility risk, and the cross-section of returns

Thesis (Ph. D.)--University of Rochester. William E. Simon Graduate School of Business Administration, 2008.The first chapter presents a simple real options model that explains why in cross-section high idiosyncratic volatility implies low future returns and why the value effect is stronger for high volatility firms. In the model, high idiosyncratic volatility makes growth options a hedge against aggregate volatility risk. Growth options become less sensitive to the underlying asset value as idiosyncratic volatility goes up. It cuts their betas and saves them from losses in volatile times that are usually recessions. Growth options value also positively depends on volatility. It makes them a natural hedge against volatility increases. In empirical tests, the aggregate volatility risk factor explains the idiosyncratic volatility discount and why it is stronger for growth firms. The aggregate volatility risk factor also partly explains the stronger value effect for high volatility firms. I also find that high volatility and growth firms have much lower betas in recessions than in booms. In the second chapter I show that the aggregate volatility risk factor (the BVIX factor) explains the well-known underperformance of small growth firms. The BVIX factor also reduces the underperformance of IPOs and SEOs by 45% and makes it statistically insignificant. The BVIX factor is unrelated to the investment factor proposed by Lyandres, Sun, and Zhang (2007) and has similar explanatory power. The BVIX factor is more helpful than the investment factor in explaining stronger new issues underperformance for small firms and growth firms. The investment factor is better at capturing the change in the underperformance in event time. The BVIX factor is also successful in explaining low returns to high cumulative issuance firms and the stronger cumulative issuance puzzle for growth firms. In the third chapter I show that the results in the first two chapters are robust to controlling for the interaction of leverage and idiosyncratic volatility and behavioral effects, to replacing market-to-book with investment, and to different ways of defining the BVIX factor and the idiosyncratic volatility discount. I also find that 15 to 20% of the anomalies in the first chapter are concentrated in the three days around earnings announcements, but this effect can be partly explained by the risk shift at the announcement