A new volatility model: GQARCH-It\^{o} model

TL;DR

This paper introduces the GQARCH-Itô model, a new econometric approach that captures volatility asymmetry in high-frequency financial data, with proven statistical properties and superior forecasting performance.

Contribution

The paper proposes the GQARCH-Itô model, combining high- and low-frequency data to better describe volatility asymmetry, along with new estimators and empirical validation.

Findings

The model accurately captures volatility asymmetry.

It outperforms existing GARCH-Itô models in forecasting.

Estimates have desirable asymptotic properties.

Abstract

Volatility asymmetry is a hot topic in high-frequency financial market. In this paper, we propose a new econometric model, which could describe volatility asymmetry based on high-frequency historical data and low-frequency historical data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed methodologies. And an empirical application is demonstrated that the new model has stronger volatility prediction power than GARCH-It\^{o} model in the literature.