A Dynamic Behavioral Model of the Credit Boom

Abstract

In this paper we provide a dynamic model of banking competition where bounded rationality of some competitors explains how the credit cycle is amplified. We model the economic cycle following Rötheli (2012b) where boundedly rational banks, in their Bayesian learning, overestimate probabilities of success during booms and underestimate them during recessions. The main results obtained are three. First, the model suggests pessimism/underconfidence is not a powerful driver of credit cycles. Instead, it supports it is euphoria during large upswings what seeds the next crunch.Second, the dynamization of the model provides further insight on how boundedly rational competition amplifies the credit cycle. Finally, an additional prediction is that the effects of behavioral biases are expected to be more pervasive the lower the quality of the niche markets.

Retesting the institutional memory hypothesis: An experimental study

“…For example, Tobias Rötheli (2012) developed a model of boundedly rational credit cycles, arguing in favor of the role of bounded rationality in perceiving and processing information when making a decision. Based on the hypothesis of Rötheli (2012), David Peon, Manel Antelo, and Anxo Calvo (2015) developed a dynamic behavioral model of credit cycles, arising from the competition among boundedly rational banks and causing cyclical shifts in the quality of the bank's loan portfolio. Paul De Grauwe and Corrado Macchiarelli (2015) developed a behavioral macroeconomic model, including the banking sector, where agents have limited cognitive abilities (animal spirits) and are prone to waves of optimism and pessimism and where bank agents accelerate and amplify these vicious cycles.…”

The effect of behavioral biases on financial decisions

“…Moreover, Jacobsen et al (2014) observe men tend to be significantly more optimistic than women in financial markets. Recent research uses excessive optimism as a key factor behind credit booms (Peón, Antelo and Calvo, 2015), and observes a similar bias in governments' official forecasts (Frankel and Schreger, 2013).…”