Understanding both the dynamics of volatility and the shape of the distribution of returns conditional on the volatility state is important for many financial applications. A simple single-factor stochastic volatility model appears to be sufficient to capture most of the dynamics. It is the shape of the conditional distribution that is the problem. This paper examines the idea of modeling this distribution as a discrete mixture of normals. The flexibility of this class of distributions provides a transparent look into the tails of the returns distribution. Model diagnostics suggest that the model, SV-mix, does a good job of capturing the salient features of the data. In a direct comparison against several affine-jump models, SV-mix is strongly preferred by Akaike and Schwarz information criteria.

We present four stylized facts about the Dot Com Era: (1) there was a widespread belief in a Get Big Fast business strategy, (2) the increase and decrease in public and private equity investment was most prominent in the Internet and information technology sectors, (3) the survival rate of dot com firms is on par with or higher than other emerging industries, and (4) firm survival is independent of private equity funding. To connect these findings we offer a herding model that accommodates a divergence between the information and incentives of venture capitalists and their investors. A Get Big Fast belief cascade could have led to overly focused investment in too few Internet startups and, as a result, too little entry.