Journal
JOURNAL OF ECONOMETRICS
Volume 194, Issue 2, Pages 220-230Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2016.05.003
Keywords
GARCH; Ito process; Quasi-maximum likelihood estimator; Realized volatility; Stochastic differential equation
Categories
Funding
- National Science Foundation [DMS-10-5635, DMS-12-65203]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1005635] Funding Source: National Science Foundation
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This paper introduces a unified model, which can accommodate both continuous-time Ito processes used to model high-frequency stock prices and GARCH processes employed to model low-frequency stock prices, by embedding a discrete-time GARCH volatility in its continuous-time instantaneous volatility. This model is called a unified GARCH-Ito model. We adopt realized volatility estimators based on high frequency financial data and the quasi-likelihood function for the low-frequency GARCH structure to develop parameter estimation methods for the combined high-frequency and low-frequency data. We establish asymptotic theory for the proposed estimators and conduct a simulation study to check finite sample performances of the estimators. We apply the proposed estimation approach to Bank of America stock price data. (C) 2016 Elsevier B.V. All rights reserved.
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