Exponential Realized Garch-itô Volatility Models
Year: 2022
Type: article
Abstract: This paper introduces a novel Ito diffusion process to model high-frequency financial data, which can accommodate low-frequency volatility dynamics by embedding the discrete-time non-linear exponential GARCH structure with log-integrated volatility in a continuous instantaneous volatility process. The key feature of the proposed model is that, unlike existing GARCH-Ito models, the instantaneous volatility process has a non-linear structure, which ensures that the log-integrated volatilities have the realized GARCH structure. We call this the exponential realized GARCH-Ito (ERGI) model. Given the auto-regressive structure of the log-integrated volatility, we propose a quasi-likelihood estimation procedure for parameter estimation and establish its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and an empirical study with 50 assets among the S&P 500 compositions. The numerical studies show the advantages of the new proposed model.
Source: Econometric Theory
Author: Donggyu Kim
Institution: Korea Advanced Institute of Science and Technology
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
Award: G7947780071
OpenAlex ID: W4308645815