Hierarchical forecasting based on AR-GARCH model in a coherent structure

So Young Sohn, Michael Lim

Research output: Contribution to journalArticlepeer-review

Abstract

This paper compares the accuracy of the aggregate forecasting with the bottom-up forecasting based on AR-GARCH model for the return rate of simulated Dow Jones Industrial Average. Most of the existing stock price index studies did not consider the hierarchical structure and often missed the coherent relationships between individual components. In this experiment, we simulated 30 coherent components based on AR(2)-GARCH(1, 1) model. Then we evaluated the performance of both forecasting methods ignoring the coherent structure. The results of our experiment indicated that the accuracy of forecasting method varied depending on the correlation degree of 30 coherent components, however the data noise did not significantly influenced the performance of hierarchical forecasting method.

Original languageEnglish (US)
Pages (from-to)1033-1040
Number of pages8
JournalEuropean Journal of Operational Research
Volume176
Issue number2
DOIs
StatePublished - Jan 16 2007

Keywords

  • AR-GARCH model
  • Coherent structure
  • Dow Jones Industrial Average
  • Hierarchical forecasting

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Hierarchical forecasting based on AR-GARCH model in a coherent structure'. Together they form a unique fingerprint.

Cite this