Structural Estimation of Behavioral Heterogeneity

TL;DR

This paper introduces a behavioral asset pricing model with heterogeneous agents who switch strategies based on market conditions and noisy information, and estimates it using a novel identification approach to match real market data.

Contribution

It develops a nonlinear structural model incorporating behavioral heterogeneity and information frictions, and employs a new identification method using a thin set for estimation.

Findings

Estimated model replicates key return moments

Results are robust across samples and methods

Model captures heterogeneity and strategic switching

Abstract

We develop a behavioral asset pricing model in which agents trade in a market with information friction. Profit-maximizing agents switch between trading strategies in response to dynamic market conditions. Due to noisy private information about the fundamental value, the agents form different evaluations about heterogeneous strategies. We exploit a thin set---a small sub-population---to pointly identify this nonlinear model, and estimate the structural parameters using extended method of moments. Based on the estimated parameters, the model produces return time series that emulate the moments of the real data. These results are robust across different sample periods and estimation methods.