Topic: Generalized Method of Integrated Moments for High-Frequency Data
Speaker: Jia Li, Assistant Professor, Department of Economics, Duke University
Date: March 25th (Wed.)
Time: 12:30pm-1:30pm
Location: Building 1, Room 501, Faculty Lounge
Language: English
Abstract:
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.
About the speaker:
Jia Li joined Department of Economics at Duke University after he received his PhD in Economics from Princeton University in 2011. Dr. Li’s research focuses on nonparametric estimation and inference of financial risk factors, such as volatility and jumps, based on high frequency financial data. Such data exhibit a microscopic view of asset price behaviors, but also raise new challenges for econometricians. He is currently working on spot variance regressions, volatility occupation times, and forecast evaluation with latent variables. His research has been published on Journal of Econometrics, Econometrica and Annals of Statistics.
