Integrating High-Frequency Data in Volatility Prediction: A DCC-GARCH-MIDAS Approach

Shuang  Jin; Wei Chong  Choo; Matemilola Bolaji  Tunde; Wan Cheong  Kin; Pengrui  Jin

doi:10.6180/jase.202505_28(5).0013

Integrating High-Frequency Data in Volatility Prediction: A DCC-GARCH-MIDAS Approach

Shuang Jin¹This email address is being protected from spambots. You need JavaScript enabled to view it., Wei Chong Choo¹, Matemilola Bolaji Tunde¹, Wan Cheong Kin², and Pengrui Jin³

¹School of Business and Economics, Universiti Putra Malaysia.

²Department of Economics and Corporate Administration, Faculty of Accountancy, Finance and Business, Tunku Abdul Rahman University of Management and Technology (TARUMT).

³University of Birmingham, Edgbaston, Birmingham.

Received: January 29, 2023
Accepted: April 14, 2024
Publication Date: July 11, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202505_28(5).0013

Traditional econometric models are restricted in their capacity to examine same-frequency data, resulting in the loss of valuable information from high-frequency data. To address this problem, We propose the DCC-GARCHMIDAS model by combining dynamic conditional correlation modeling with information from high-frequency data. For our empirical study, we utilised historical data from 2000 to 2019 as in-sample data and trained a model for predicting volatility. We applied the trained model to forecast data from 2020 to 2022, calculating the discrepancy between predicted volatility and actual observations, and comparing differences between the predicted and actual values. The research findings not only enhance comprehension of the correlation between macroeconomics and financial market instability but also propose a distinct strategy for resolving the problem of incongruent data frequencies.

Keywords: DCC, MIDAS, GARCH, Long-run correlation, Macroeconomic variables.

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