Multiplicative Component GARCH Model of Intraday Volatility

TL;DR

This paper introduces a multiplicative component model for intraday volatility that accounts for periodicity, stochasticity, and transaction durations, applicable to both regular and irregular data, with a nonparametric estimation method.

Contribution

It extends existing models by incorporating transaction durations and provides a nonparametric approach for estimating intraday volatility periodicity.

Findings

Model effectively captures intraday return interdependencies.

Applicable to both regular and irregular spaced returns.

Demonstrates improved volatility modeling accuracy.

Abstract

This paper proposes a multiplicative component intraday volatility model. The intraday conditional volatility is expressed as the product of intraday periodic component, intraday stochastic volatility component and daily conditional volatility component. I extend the multiplicative component intraday volatility model of Engle (2012) and Andersen and Bollerslev (1998) by incorporating the durations between consecutive transactions. The model can be applied to both regularly and irregularly spaced returns. I also provide a nonparametric estimation technique of the intraday volatility periodicity. The empirical results suggest the model can successfully capture the interdependency of intraday returns.