Statistical Inference for Unified Garch-Ito Models with High-Frequency Financial Data

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The existing estimation methods for the model parameters of the unified GARCH-Ito model (Kim and Wang, ) require long period observations to obtain the consistency. However, in practice, it is hard to believe that the structure of a stock price is stable during such a long period. In this article, we introduce an estimation method for the model parameters based on the high-frequency financial data with a finite observation period. In particular, we establish a quasi-likelihood function for daily integrated volatilities, and realized volatility estimators are adopted to estimate the integrated volatilities. The model parameters are estimated by maximizing the quasi-likelihood function. We establish asymptotic theories for the proposed estimator. A simulation study is conducted to check the finite sample performance of the proposed estimator. We apply the proposed estimation approach to the Bank of America stock price data.
Publisher
WILEY
Issue Date
2016-07
Language
English
Article Type
Article
Keywords

MAXIMUM LIKELIHOOD ESTIMATION; REALIZED VOLATILITY; NOISE

Citation

JOURNAL OF TIME SERIES ANALYSIS, v.37, no.4, pp.513 - 532

ISSN
0143-9782
DOI
10.1111/jtsa.12171
URI
http://hdl.handle.net/10203/225855
Appears in Collection
MT-Journal Papers(저널논문)
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