Bernard Herskovic

Anderson School
University of California at Los Angeles

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
Institutional Affiliation: University of California at Los Angeles

NBER Working Papers and Publications

April 2014The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications
with Bryan T. Kelly, Hanno Lustig, Stijn Van Nieuwerburgh: w20076
We show that firms’ idiosyncratic volatility obeys a strong factor structure and that shocks to the common factor in idiosyncratic volatility (CIV) are priced. Stocks in the lowest CIV-beta quintile earn average returns 5.4% per year higher than those in the highest quintile. The CIV factor helps to explain a number of asset pricing anomalies. We provide new evidence linking the CIV factor to income risk faced by households. These three facts are consistent with an incomplete markets heterogeneous-agent model. In the model, CIV is a priced state variable because an increase in idiosyncratic firm volatility raises the average household’s marginal utility. The calibrated model matches the high degree of comovement in idiosyncratic volatilities, the CIV-beta return spread, and several other a...

Published: Herskovic, Bernard & Kelly, Bryan & Lustig, Hanno & Van Nieuwerburgh, Stijn, 2016. "The common factor in idiosyncratic volatility: Quantitative asset pricing implications," Journal of Financial Economics, Elsevier, vol. 119(2), pages 249-283. citation courtesy of

September 2013Firm Volatility in Granular Networks
with Bryan Kelly, Hanno Lustig, Stijn Van Nieuwerburgh: w19466
Firm volatilities co-move strongly over time, and their common factor is the dispersion of the economy-wide firm size distribution. In the cross section, smaller firms and firms with a more concentrated customer base display higher volatility. Network effects are essential to explaining the joint evolution of the empirical firm size and firm volatility distributions. We propose and estimate a simple network model of firm volatility in which shocks to customers influence their suppliers. Larger suppliers have more customers and customer-supplier links depend on customers size. The model produces distributions of firm volatility, size, and customer concentration consistent with the data.

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