Abstract: We study a dynamic equilibrium model in which investors disagree on future volatility and trade volatility derivatives to hedge stock positions and speculate. On average, volatility disagreement makes the variance risk premium more negative. However, volatility trading enables a risk transfer among investors that turns the variance risk premium positive when the market underestimates future volatility. Under higher volatility, investors trade fewer volatility derivatives as these become too risky. These economic mechanisms shed light on empirical regularities during market turmoil. Volatility disagreement also lowers the stock market valuation, increases market volatility, and generates time-variation in the leverage effect.
Discussant: Christian Heyerdahl-Larsen, BI Norwegian Business School
Alessandro Crescini, Swiss Finance Institute - University of Geneva
Fabio Trojani, University of Geneva
Andrea Vedolin, Boston University
Abstract: This paper proposes a theoretical framework for recovering investors' subjective beliefs/expected returns using holdings data and option prices under the weak assumption of no-arbitrage. We empirically document that the statistical properties of subjective expected returns on the market differ wildly across investor type and depend crucially on their portfolio composition. While expected returns estimated from price data alone suggest that expected returns are highly volatile and countercyclical, including holdings data can imply returns that are less volatile and procyclical. More specifically, we show that the expected returns inferred from public investor beliefs increase in bad times when they become the net suppliers of crash insurance in option markets, mirroring price-based estimates. Financial intermediaries' expected returns decrease during bad times when they become the net buyers of crash protection when their constraints bind. Our findings are in line with the survey literature that documents large heterogeneity in measures of expected returns.
Discussant: Eben Lazarus, University of California-Berkeley
Abstract: The growth of natural, social, and economic phenomena including firms, cities, and pandemics is known to be heavy-tailed. Neither a simple explanation nor a well-fitting distributional form for these heavy-tailed growth phenomena is known. Here I show that an extension of the log-linear production function provides both a simple explanation and a emph{single} well-fitting and theoretically motivated distributional form for emph{all of them}. I discuss why these results arise as a consequence of the Central Limit Theorem and sketch dynamic models using this production function for the phenomena listed above, yielding remarkable fit between the predicted and observed data distributions. My results include: (i) predicting the distribution of firm cashflows; (ii) providing a well-behaved distribution for equity returns; (iii) sketching a model of increasing-returns-to-scale cities in which more than one city can rationally exist; (iv) proposing an extension to the classical Malthusian ``birth-death'' model; and (v) rationalizing a variety of observed growth distributions.
Discussant: Rustam Ibragimov, Imperial College London
