Generalized Method of Integrated Moments for High-Frequency Data

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• 作者：Jia Li and Dacheng Xiu
• 刊名：Econometrica
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：84
• 期：4.x
• 页码：1613-1633
• 全文大小：255K
• ISSN：1468-0262

文摘

We propose a semiparametric two-step inference procedure for a finite-dimensional parameter based on moment conditions constructed from high-frequency data. The population moment conditions take the form of temporally integrated functionals of state-variable processes that include the latent stochastic volatility process of an asset. In the first step, we nonparametrically recover the volatility path from high-frequency asset returns. The nonparametric volatility estimator is then used to form sample moment functions in the second-step GMM estimation, which requires the correction of a high-order nonlinearity bias from the first step. We show that the proposed estimator is consistent and asymptotically mixed Gaussian and propose a consistent estimator for the conditional asymptotic variance. We also construct a Bierens-type consistent specification test. These infill asymptotic results are based on a novel empirical-process-type theory for general integrated functionals of noisy semimartingale processes.