Nonparametric estimation of structural models for high-frequency currency market data

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dc.contributor.author

Bansal, R

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Gallant, AR

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Hussey, R

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Tauchen, G

dc.date.accessioned

2010-03-09T15:29:22Z

dc.date.issued

1995-01-01

dc.description.abstract

Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method for estimation of structural economic models. The method forces the model to match (under a GMM criterion) the score function of a nonparametric estimate of the conditional density of observed data. The estimation uses weekly U.S.-German currency market data, 1975-90. © 1995.

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application/pdf

dc.identifier.issn

0304-4076

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https://hdl.handle.net/10161/1902

dc.language.iso

en_US

dc.publisher

Elsevier BV

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Journal of Econometrics

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10.1016/0304-4076(94)01618-A

dc.title

Nonparametric estimation of structural models for high-frequency currency market data

dc.type

Journal article

pubs.begin-page

251

pubs.end-page

287

pubs.issue

1-2

pubs.organisational-group

Duke

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Economics

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Faculty

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Fuqua School of Business

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Trinity College of Arts & Sciences

pubs.publication-status

Published

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66

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