Credit Cycles

Paper Session

Friday, Jan. 6, 2017 2:30 PM – 4:30 PM

Hyatt Regency Chicago, Regency D
Hosted By: American Economic Association
  • Chair: Itay Goldstein, University of Pennsylvania

A Model of Credit Market Sentiment

Robin Greenwood
,
Harvard University
Samuel Hanson
,
Harvard University
Lawrence Jin
,
California Institute of Technology

Abstract

We present a model of credit market sentiment in which investors form beliefs about future creditworthiness by extrapolating past defaults. Our key contribution is to model the endogenous two-way feedback between credit market sentiment and credit market outcomes. This feedback arises because investors’ beliefs depend on past defaults, but beliefs also drive future defaults through investors’ willingness to refinance debt at low interest rates. Our model is able to capture many documented features of credit booms and busts, including the link between credit growth and future returns, and the “calm before the storm” periods in which fundamentals have deteriorated but the credit market has not yet turned.

Household Debt and Business Cycles Worldwide

Atif Mian
,
Princeton University
Amir Sufi
,
University of Chicago
Emil Verner
,
Princeton University

Abstract

A rise in the household debt to GDP ratio predicts lower output growth and a higher unemployment rate over the medium-run, contrary to standard open economy macroeconomic models in which an increase in debt is driven by news of better future income prospects. GDP forecasts by the IMF and OECD underestimate the importance of a rise in household debt to GDP, giving the change in the household debt to GDP ratio of a country the ability to predict growth forecasting errors. The predictive power of a rise in household debt to GDP ratios is signi cantly stronger in countries with a fixed exchange rate and countries with a floating exchange rate but against the zero lower bound
on nominal interest rates. A rise in household debt to GDP is associated contemporaneously with a rising consumption share of output, a worsening of the current account balance, and a rise in the share of consumption goods within imports. Taken together, these results are consistent with demand externality models where credit supply shocks result in households borrowing more than is socially optimal given the presence of nominal rigidities and monetary policy constraints. We also document a global household debt cycle with an increase in household debt to GDP at the global level predicting lower global output growth.

Diagnostic Expectations and Credit Cycles

Pedro Bordalo
,
Royal Holloway University of London
Nicola Gennaioli
,
Bocconi University
Andrei Shleifer
,
Harvard University

Abstract

We present a model of credit cycles arising from diagnostic expectations – a belief formation mechanism based on Kahneman and Tversky’s (1972) representativeness heuristic. In this formulation, when revising their beliefs agents overweight future outcomes that have become more likely in light of incoming data. Diagnostic expectations are forward looking, and as such are immune to the Lucas critique and nest rational expectations as a special case. Diagnostic expectations exhibit excess volatility, over-reaction to news, and systematic reversals. These dynamics can account for several features of credit cycles and macroeconomic volatility.

Mutual Fund Flows and Fluctuations in Credit and Business Cycles

Azi Ben-Rephael
,
Indiana University
Jaewon Choi
,
University of Illinois-Urbana-Champaign
Itay Goldstein
,
University of Pennsylvania

Abstract

We offer an early indicator for both credit and business cycles, using investor asset allocation in and out of high-yield corporate bond mutual funds. Our measure leads other predictors for business cycles known in the literature. It positively predicts net bond issuance, growth in financial intermediary balance sheets, shares of high-yield bond issuers, and the degrees of reaching for yield in the credit market. Importantly, our measure positively predicts GDP growth and negatively predicts unemployment earlier than other indicators in the literature that are based on credit spreads. This measure sheds new light on the theories of credit cycles and can also help policymakers in addressing future credit cycles.
Discussant(s)
Markus Brunnermeier
,
Princeton University
Philipp Schnabl
,
New York University
David Thesmar
,
Massachusetts Institute of Technology and CEPR
Adi Sunderam
,
Harvard University
JEL Classifications
  • E3 - Prices, Business Fluctuations, and Cycles
  • G0 - General