Volatility analysis with realized GARCH-Itô models

Xinyu Song, Donggyu Kim, Huiling Yuan, Xiangyu Cui, Zhiping Lu, Yong Zhou and Yazhen Wang

Journal of Econometrics, 2021, vol. 222, issue 1, 393-410

Abstract: 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.

Keywords: High-frequency financial data; Option data; Quasi-maximum likelihood estimation; Stochastic differential equation; Volatility estimation and prediction (search for similar items in EconPapers)
JEL-codes: C10 C22 C58 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:222:y:2021:i:1:p:393-410

DOI: 10.1016/j.jeconom.2020.07.007

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