Conference Agenda

This paper studies the relationship between investors’ subjective expectations and the term structure of interest rates. Departing from rational expectations, we allow investors to hold arbitrary---and potentially heterogeneous---beliefs about future interest rates. We derive the relationships that expected and realized interest rates must satisfy under different assumptions about expectation formation,

and we develop regression-based tests for two key hypotheses: (i) that bond risk premia are constant, and (ii) that investors’ expectations across maturities and forecast horizons are consistent with one another. Using survey data, we find no evidence of time-varying risk premia for short-term bonds. We also document that market participants’ expectations are inconsistent with the structural relationships that link short- and long-term interest rates.

We evaluate approaches to estimating demand elasticities in dynamic asset markets, both theoretically and empirically. We establish strict, necessary conditions that the dynamics of instrumented asset price variation must satisfy for valid identification. We illustrate these insights in a general equilibrium model of dynamic trade and derive the magnitude of biases that arise when these conditions are violated. Estimates are severely biased when the instrumented price variation is persistent or predictable. We then propose an approximate bias-correction factor and establish when such a correction is feasible. Empirically, we show that commonly used instruments yield elasticity estimates that are off by orders of magnitude, or even have the wrong sign. Our analysis further reveals significant shortcomings of standard multiplier calculations, and instead characterizes the dynamic asset market interventions required to sustain a targeted price support process, with direct implications for policies such as Quantitative Easing.

We develop an equilibrium model in which agents learn about both the mean and volatility of dividends and differ in ambiguity aversion. Because their conservative portfolios give them higher marginal risk-bearing capacity, ambiguity-averse investors step in as buyers when volatility rises. In contrast to economies populated only by Bayesian agents, volatility learning under ambiguity aversion has first-order effects: uncertainty premia rise with perceived volatility and portfolio flows predict returns. Using the concavity of the option-implied volatility surface as a proxy for ambiguity and index futures trading data, we provide empirical support for the model's predictions.