This project has generated two papers focusing on two distinct aspects of the projects.

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Paper 1

The first paper, “Partial Consensus in Large Games and Markets” (by Gabriel Degranges and Sayantan Ghosal), proposes a solution concept for large games and markets. The use of rational expectations as a solution concept in mainstream macroeconomics essentially rules out stabilizing (as against coordinating) expectations as a problem. Partial consensus refers to outcomes of large games and markets (models with a continuum of actions and agents) which are "approximately" self-fulfilling, i.e. consistent with all that is commonly known by each agent via an iterative elimination process. The paper sets out to answer the following research questions: When is equilibrium the only outcome consistent with partial consensus? When does partial consensus allow for non-equilibrium outcomes that are "approximately" self-fulfilling? What additional economic insights can be obtained? In the paper, a formal definition of partial consensus and an associated, continuous measure of the degree of stability, via belief coordination, for equilibrium outcomes is developed.

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In an economic application, the foundations of intertemporal trade via belief coordination in a two-period economy is re-examined. Individuals submit demand functions so that belief coordination in current spot market prices is never a problem: instead the individuals need to coordinate beliefs about future prices when they trade in current spot markets. The conditions under which there are multiple rationalizable beliefs over second period prices is derived. These conditions require that second period spot market prices are sensitive to a redistribution of commodities in a spot market in either of the two time periods with the implication that the initial lack of consensus over second period prices is amplified. Under these conditions, partial consensus outcomes are compatible with the existence an asset price bubble.

 

Paper 2

The second paper, “Anxiety, Expectations Stabilization and Intertemporal Markets: Theory, Evidence and Policy” (by Francesco Carbonero, Jeremy Davies, Ekkehard Ernst, Sayantan Ghosal, Leaza McSorley), focuses on the analyzing the implications of incorporating anxiety in models of intertemporal trade. Anxiety aversion is modelled as a negative emotion regarding the anticipation of a future event: it constitutes a psychological payoff experienced by a decision-maker today in response to perceived future risk (Caplin and Leahy, 2001). Anxiety aversion implies that a higher perceived future risk will trigger a higher negative psychological payoff today. When anxiety rises, economic actors put more weight on (expected) negative outcomes, overreacting to negative news and discounting positive ones.

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In the paper, a simple model is developed that captures the link between anxiety, strategic uncertainty driven by multiple narratives and investment in risky assets and allows a role for policy measures to stabilise expectations. A new empirical measure of anxiety via a machine learning algorithm that applies sentiment analysis on news articles published online by The Daily Mail, Reuters and Press Association is constructed. The plausibility of the new measure is examined and it is used to carry out empirical tests of how anxiety shocks impact on stock market outcomes and verify a key prediction of the theoretical model. We discuss the policy implications our analysis focusing on the role of "lighthouse" policies in stabilizing expectations.