Volatility analysis with realized GARCH-Ito models

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This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump–diffusion and discrete-time realized GARCH model by embedding the discrete realized GARCH structure in the continuous instantaneous volatility process. The key feature of the proposed model is that the corresponding conditional daily integrated volatility adopts an autoregressive structure, where both integrated volatility and jump variation serve as innovations. We name it as the realized GARCH-Itô model. Given the autoregressive structure in the conditional daily integrated volatility, we propose a quasi-likelihood function for parameter estimation and establish its asymptotic properties. To improve the parameter estimation, we propose a joint quasi-likelihood function that is built on the marriage of daily integrated volatility estimated by high-frequency data and nonparametric volatility estimator obtained from option data. We conduct a simulation study to check the finite sample performance of the proposed methodologies and an empirical study with the S&P500 stock index and option data.
Publisher
ELSEVIER SCIENCE SA
Issue Date
2021-05
Language
English
Article Type
Article; Proceedings Paper
Citation

JOURNAL OF ECONOMETRICS, v.222, no.1, pp.393 - 410

ISSN
0304-4076
DOI
10.1016/j.jeconom.2020.07.007
URI
http://hdl.handle.net/10203/282152
Appears in Collection
MT-Journal Papers(저널논문)
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