Jason Fink

What Drove the Increase in Idiosyncratic Volatility during the Internet Boom

Aggregate idiosyncratic volatility spiked nearly fivefold during the Internet boom of the late 1990s, dwarfing in magnitude a moderately increasing trend. While some researchers argue that this rise in idiosyncratic risk was the result of changes in the characteristics of public firms, others argue that it was driven by the changing sentiment of irrational traders. We present evidence that the marketwide decline in maturity of the typical public firm can explain most of the increase in firm-specific risk during the Internet boom. Controlling for firm maturity, we find no evidence that investor sentiment drives idiosyncratic risk throughout the Internet boom.

Firm Age and Fluctuations in Idiosyncratic Risk

This paper presents empirical evidence that fluctuations in idiosyncratic risk are largely driven by the age characteristics of the firms composing the market. Consistent with previous studies, we find that the age of the typical firm at its IPO date has fallen dramatically from nearly 40 years old in the early 1960s to less than 5 years old by the late 1990s. Since younger firms tend to be riskier, this systematic decline in the average age of the typical public firm, combined with the increasing number of firms going public over the last 30 years, has caused the increase in idiosyncratic risk over the last four decades. We show that after controlling for the proportion of young firms in the market, this time period exhibits no trend in the time series of idiosyncratic risk. Moreover, we find some evidence of a negative trend in idiosyncratic risk after controlling for other measures of firm maturity.

The January Effect - A New Piece for an Old Puzzle

For decades the finance profession has noticed the tendency of equity prices, most noticeably for small stocks, to rise in value in the month of January. The small stock January effect appears to have been significant over the last thirty five years. However, we demonstrate that even after controlling for both the size of the firm and systematic factors, young firms experience significant and abnormally positive January returns. This age effect has several possible sources, including firm level information uncertainty in January or increased investor demand related to young firms.

Estimation of a Stochastic Volatility Model Using Pricing and Hedging Information

Estimation of option pricing models in which the underlying asset exhibits stochastic volatility presents complicated econometric questions. One such question, thus far unstudied, is whether the inclusion of information derived from hedging relationships implied by an option pricing model may be used in conjunction with pricing information to provide more reliable parameter estimates than the use of pricing information alone. This paper estimates, using a simple least-squares procedure, the stochastic volatility model of Heston (1993), and includes hedging information in the objective function. This hedging information enters the objective function through a weighting parameter that is chosen optimally within the model. With the weight appropriately chosen, we find that incorporating the hedging information reduces both the out-of-sample hedging and pricing errors associated with the Heston model.