Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data

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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.
Publisher
ELSEVIER SCIENCE SA
Issue Date
2016-10
Language
English
Article Type
Article
Keywords

VOLATILITY

Citation

JOURNAL OF ECONOMETRICS, v.194, no.2, pp.220 - 230

ISSN
0304-4076
DOI
10.1016/j.jeconom.2016.05.003
URI
http://hdl.handle.net/10203/225854
Appears in Collection
MT-Journal Papers(저널논문)
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