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

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

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


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 languageEnglish (US)
Pages (from-to)251-287
Number of pages37
JournalJournal of Econometrics
Issue number1-2
StatePublished - 1995

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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