Knowledge that Transforms

A stylized theoretical model with stochastic volatility suggests the existence of a trade-off between returns and volatility-of-volatility. Using the VVIX, a measure of the option-implied volatility of the volatility index, we confirm this prediction and detect that time-varying aggregate volatility-of-volatility commands an economically substantial and statistically significant negative risk premium. We find that a two-standard-deviation increase in aggregate volatility-of-volatility factor loadings is associated with a decrease in average annual returns of about 11%. These results are robust to controlling for aggregate volatility, jump risk, and several other characteristics and factor sensitivities, as well as various additional tests.

Do stocks bear a crash risk premium? We examine the empirical performance of the tail index measure from Kelly and Jiang (2014). We find that the tail index explains the cross-section of the discount rate component of returns, but not the cash-flow component. Moreover, in the time series the tail index is uncorrelated with theoretically motivated measures of aggregate uncertainty and systemic risk. In contrast, the tail index Granger causes and is Granger caused by the level of the term structure, and the slope of the term structure Granger causes tail risk. Received June 22, 2016; editorial decision December 23, 2017 by Editor Raman Uppal.

Using hedge funds’ holdings of IPO stocks, we find that stocks with abnormally high hedge fund holdings, based on stock and deal characteristics, yield abnormal returns. Moreover, hedge funds are able to sell IPO stocks in a timely fashion before long-run underperforming periods start, suggesting that hedge funds possess information advantages in IPO stocks. Finally, we address the question of where hedge funds may have obtained their information advantages. Hedge funds earn higher abnormal returns in “connected” stocks when their prime brokers also serve as IPO underwriters, indicating that such connections enable hedge funds to make more informed investment decisions in IPO stocks. Received December 31, 2014; editorial decision May 27, 2017 by Editor Wayne Ferson.

Journal Article Subscription Page Get access The Review of Asset Pricing Studies, Volume 7, Issue 2, December 2017, Page i3, https://doi.org/10.1093/rapstu/rax007 Published: 13 November 2017

Journal Article Announcements Get access The Review of Asset Pricing Studies, Volume 7, Issue 1, June 2017, Page 1, https://doi.org/10.1093/rapstu/rax013 Published: 12 May 2017

Journal Article Announcements Get access The Review of Asset Pricing Studies, Volume 7, Issue 2, December 2017, Page 171, https://doi.org/10.1093/rapstu/rax022 Published: 13 November 2017

We specify and estimate a no-arbitrage model for sovereign CDS contracts in which countries’ default intensities depend on economic and financial indicators. To facilitate identification and to distinguish the importance of local and global covariates, we estimate a model with three global and four local covariates using CDS spreads for five maturities and twenty-five countries. The model provides a good fit. The impact of the economic and financial variables on spreads is consistent with economic intuition, and substantially varies across countries and over time. Estimated risk premiums are highly variable and peak during the 2008 financial crisis for most countries.

We use a unique data set from the Trade Reporting and Compliance Engine (TRACE) to study liquidity effects in the U.S. structured product market. Our main contribution is the analysis of the relation between accuracy in measuring liquidity and the level of detail of the trading data employed. We find evidence that, in general, liquidity measures that use dealer-specific information can be efficiently proxied by means of measures that use less detailed information. However, when the level of trading activity in individual securities or overall market activity is low, measures based on more detailed trading data permit a more precise assessment of liquidity. These results provide us with a better understanding of the information contained in disseminated OTC trading data, in general.