Structural estimation of behavioral heterogeneity

Zhentao Shi; Huanhuan Zheng

doi:10.1002/jae.2640

Structural estimation of behavioral heterogeneity

Zhentao Shi
, Huanhuan Zheng

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

2 Citations (Scopus)

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. Owing 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 point 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. © 2018 John Wiley & Sons, Ltd.

Original language	English
Pages (from-to)	690-707
Journal	Journal of Applied Econometrics
Volume	33
Issue number	5
DOIs	https://doi.org/10.1002/jae.2640
Publication status	Published - 1 Aug 2018
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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title = "Structural estimation of behavioral heterogeneity",

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. Owing 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 point 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. {\textcopyright} 2018 John Wiley \& Sons, Ltd.",

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AU - Shi, Zhentao

AU - Zheng, Huanhuan

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - 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. Owing 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 point 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. © 2018 John Wiley & Sons, Ltd.

AB - 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. Owing 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 point 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. © 2018 John Wiley & Sons, Ltd.

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U2 - 10.1002/jae.2640

DO - 10.1002/jae.2640

M3 - RGC 21 - Publication in refereed journal

SN - 0883-7252

VL - 33

SP - 690

EP - 707

JO - Journal of Applied Econometrics

JF - Journal of Applied Econometrics

IS - 5

ER -

Structural estimation of behavioral heterogeneity

Abstract

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