High-Frequency-Based Volatility Model with Network Structure

TL;DR

This paper proposes a novel multivariate volatility model incorporating network structures from high-frequency data, reducing complexity and improving short-term forecast accuracy.

Contribution

It introduces a new volatility model that integrates network structures, with efficient parameter estimation and superior empirical performance over existing models.

Findings

Model reduces parameter count and computational complexity.

Outperforms network GARCH in empirical tests.

Significant gains at short forecast horizons.

Abstract

This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons.