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

Ravi Bansal; A. Ronald Gallant; Robert Hussey; George Tauchen

doi:10.1016/0304-4076(94)01618-A

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

Ravi Bansal, A. Ronald Gallant, Robert Hussey, George Tauchen

Economics

Research output: Contribution to journal › Article › peer-review

68 Scopus citations

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.

Original language	English (US)
Pages (from-to)	251-287
Number of pages	37
Journal	Journal of Econometrics
Volume	66
Issue number	1-2
DOIs	https://doi.org/10.1016/0304-4076(94)01618-A
State	Published - 1995

All Science Journal Classification (ASJC) codes

Economics and Econometrics

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10.1016/0304-4076(94)01618-A

Cite this

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title = "Nonparametric estimation of structural models for high-frequency currency market data",

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.",

author = "Ravi Bansal and Gallant, {A. Ronald} and Robert Hussey and George Tauchen",

note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grants No. SES-911 I867 and SES-9023083. We thank Geert Bekaert, Lars Hansen. David Hsieh, Ellen M&rattan, Tom Sargent, and many seminar and conference participants for helpful comments at various stages of this research.",

year = "1995",

doi = "10.1016/0304-4076(94)01618-A",

language = "English (US)",

volume = "66",

pages = "251--287",

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T1 - Nonparametric estimation of structural models for high-frequency currency market data

AU - Bansal, Ravi

AU - Gallant, A. Ronald

AU - Hussey, Robert

AU - Tauchen, George

N1 - Funding Information: This material is based upon work supported by the National Science Foundation under Grants No. SES-911 I867 and SES-9023083. We thank Geert Bekaert, Lars Hansen. David Hsieh, Ellen M&rattan, Tom Sargent, and many seminar and conference participants for helpful comments at various stages of this research.

PY - 1995

Y1 - 1995

N2 - 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.

AB - 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.

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

Abstract

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