State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data

TL;DR

This paper introduces a novel state heterogeneous volatility model based on high-frequency data, capturing market dynamics influenced by economic states, and provides estimation and testing procedures with empirical validation on S&P 500 data.

Contribution

The paper proposes the SG-Ito model that incorporates state dependence in volatility, along with estimation and hypothesis testing methods for high-frequency financial data.

Findings

Evidence of leverage effects in S&P 500 volatility

Detection of investor attention and market illiquidity impacts

Identification of post-holiday effects

Abstract

Recently, to account for low-frequency market dynamics, several volatility models, employing high-frequency financial data, have been developed. However, in financial markets, we often observe that financial volatility processes depend on economic states, so they have a state heterogeneous structure. In this paper, to study state heterogeneous market dynamics based on high-frequency data, we introduce a novel volatility model based on a continuous Ito diffusion process whose intraday instantaneous volatility process evolves depending on the exogenous state variable, as well as its integrated volatility. We call it the state heterogeneous GARCH-Ito (SG-Ito) model. We suggest a quasi-likelihood estimation procedure with the realized volatility proxy and establish its asymptotic behaviors. Moreover, to test the low-frequency state heterogeneity, we develop a Wald test-type hypothesis testing procedure. The results of empirical studies suggest the existence of leverage, investor attention, market illiquidity, stock market comovement, and post-holiday effect in S&P 500 index volatility.