We develop an equilibrium model of the term structure of forward prices for storable commodities. As a consequence of a nonnegativity constraint on inventory, the spot commodity has an embedded timing option that is absent in forward contracts. This option's value changes over time due to both endogenous inventory and exogenous transitory shocks to supply and demand. Our model makes predictions about volatilities of forward prices at different horizons and shows how conditional violations of the "Samuelson effect" occur. We extend the model to incorporate a permanent second factor and calibrate the model to crude oil futures data.COMMODITY MARKETS IN RECENT YEARS have experienced dramatic growth in trading volume, the variety of contracts, and the range of underlying commodities. Market participants are also increasingly sophisticated about recognizing and exercising operational contingencies embedded in delivery contracts. 1 For all of these reasons, there is a widespread interest in models for pricing and hedging commodity-linked contingent claims. In this paper we present an equilibrium model of commodity spot and forward prices. By explicitly incorporating the microeconomics of supply, demand, and storage, our model captures some fundamental differences between commodities and financial assets.Empirically, commodities are strikingly different from stocks, bonds and other conventional financial assets. Among these differences are: * Graduate School of Industrial Administration, Carnegie Mellon University. We thank René Stulz and an anonymous referee for their helpful advice and

We address a text regression problem: given a piece of text, predict a real-world continuous quantity associated with the text's meaning. In this work, the text is an SEC-mandated financial report published annually by a publiclytraded company, and the quantity to be predicted is volatility of stock returns, an empirical measure of financial risk. We apply wellknown regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting the target variable, and a single model that uses both can significantly outperform past volatility. Interestingly, our approach is more accurate for reports after the passage of the Sarbanes-Oxley Act of 2002, giving some evidence for the success of that legislation in making financial reports more informative.

We provide an axiomatic model of preferences over atemporal risks that generalizes Gul's disappointment aversion model by allowing risk aversion to be "first order" at locations in the state space that do not correspond to certainty. Since the lotteries being valued by an agent in an assetpricing context are not typically local to certainty, our generalization, when embedded in a dynamic recursive utility model, has important quantitative implications for financial markets. We show that the state-price process, or asset-pricing kernel, in a Lucas-tree economy in which the representative agent has generalized disappointment aversion preferences is consistent with the pricing kernel that resolves the equity-premium puzzle. We also demonstrate that a small amount of conditional heteroskedasticity in the endowment-growth process is necessary to generate these favorable results. In addition, we show that risk aversion in our model can be both state-dependent and countercyclical, which empirical research has demonstrated is necessary for explaining observed assetpricing behavior.

We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contempora- neous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The re- sults highlight the potential of text streams as a substi- tute and supplement for traditional polling. consumer confidence and political opinion, and can also pre- dict future movements in the polls. We find that temporal smoothing is a critically important issue to support a suc- cessful model.

Extreme market outcomes are often followed by a lack of liquidity and a lack of trade. This market collapse seems particularly acute for markets where traders rely heavily on a specific empirical model such as in derivative markets like the market for mortgage backed securities or credit derivatives. Moreover, the observed behavior of traders and institutions that places a large emphasis on "worst-case scenarios" through the use of "stress testing" and "value-at-risk" seems different than Savage expected utility would suggest. In this paper, we capture model-uncertainty using an Epstein and Wang (1994) uncertainty-averse utility function with an ambiguous underlying asset-returns distribution. To explore the connection of uncertainty with liquidity, we specify a simple market where a monopolist financial intermediary makes a market for a propriety derivative security. The market-maker chooses bid and ask prices for the derivative, then, conditional on trade in this market, chooses an optimal portfolio and consumption. We explore how uncertainty can increase the bid-ask spread and, hence, reduces liquidity. Our infinite-horizon example produces short, dramatic decreases in liquidity even though the underlying environment is stationary. We show how these liquidity crises are closely linked to the uncertainty aversion effect on the optimal portfolio. Effectively, the uncertainty aversion can, at times, limit the ability of the market-maker to hedge a position and thus reduces the desirability of trade, and hence, liquidity.JEL Classification: G10, G13, G20