Heterogeneous preferences and equilibrium trading volume

The representative-agent Lucas model stresses aggregate risk and hence does not allow us to study the impact of agents’ heterogeneity on the dynamics of equilibrium trading volume. In this paper, we investigate under what conditions non-informational heterogeneity, i.e., differences in preferences and endowments, leads to nontrivial trading volume in equilibrium. We present a non-informational no-trade theorem that provides necessary and sufficient conditions for zero equilibrium trading volume in a continuous-time Lucas market model with heterogeneous agents, multiple goods, and multiple securities. We explain in detail how no-trade equilibria are related to autarky equilibria, portfolio autarky equilibria, and peculiar financial market equilibria, which play an important role in the literature on international risk sharing.     

Incomplete Information, Heterogeneity, and Asset Pricing

We consider a pure exchange economy where the drift of aggregateconsumption is unobservable. Agents with heterogeneous beliefsand preferences act competitively on financial and goods markets.We discuss how equilibrium market prices of risk differ acrossagents, and in particular we discuss the properties of the marketprice of risk under the physical (objective) probability measure.We propose a number of specifications of risk aversions andbeliefs where the market price of risk is much higher, and theriskless rate of return lower, than in the equivalent full informationeconomy (homogeneous and heterogeneous preferences) and thuscan provide an(other) answer to the equity premium and risk-freerate puzzles. We also derive a representation of the equilibriumvolatility and numerically assess the role of heterogeneityin beliefs. We show that a high level of stock volatility canbe obtained with a low level of aggregate consumption volatilitywhen beliefs are heterogeneous. Finally, we discuss how incompleteinformation may explain the apparent predictability in stockreturns and show that in-sample predictability cannot be exploitedby the agents, as it is in fact a result of their learning processes.     

Incomplete information,idiosyncratic volatility and stock returns

When investors have incomplete information, expected returns, as measured by an econometrician, deviate from those predicted by standard asset pricing models by including a term that is the product of the stock’s idiosyncratic volatility and the investors’ aggregated forecast errors. If investors are biased this term generates a relation between idiosyncratic volatility and expected stocks returns. Relying on forecast revisions from IBES, we construct a new variable that proxies for this term and show that it explains a significant part of the empirical relation between idiosyncratic volatility and stock returns.