Skip to main content

Research Repository

Advanced Search

Modeling Price Volatility based on a Genetic Programming Approach

Ding, Shusheng; Zhang, Yongmin; Duygun, Meryem

Authors

Shusheng Ding

Yongmin Zhang

MERYEM DUYGUN Meryem.Duygun@nottingham.ac.uk
Aviva Chair in Risk and Insurance



Abstract

Business profitability is highly dependent on risk management strategies to hedge future cash flow uncertainty. Commodity price shocks and fluctuations are key risks for companies with global supply chains. The purpose of this paper is to show how Artificial Intelligence (AI) techniques can be used to model the volatility of commodity prices. More specifically we introduce a new model – LIQ-GARCH - that uses Genetic Programming to forecast volatility. The newly generated model is then used to forecast the volatility of the following three indexes: the Commodity Research Bureau (CRB) index, the West Texas Intermediate (WTI) oil futures prices and the Baltic Dry Index (BDI). The empirical model performance tests show that the newly generated model in this paper is considerably more accurate than the traditional GARCH model. As a result, this model can help businesses to design optimal risk management strategies and to hedge themselves against price uncertainty.

Citation

Ding, S., Zhang, Y., & Duygun, M. (2019). Modeling Price Volatility based on a Genetic Programming Approach. British Journal of Management, 30(2), 328-340. https://doi.org/10.1111/1467-8551.12359

Journal Article Type Article
Acceptance Date Feb 18, 2019
Online Publication Date May 8, 2019
Publication Date Apr 30, 2019
Deposit Date Mar 5, 2019
Publicly Available Date May 1, 2021
Journal British Journal of Management
Print ISSN 1045-3172
Electronic ISSN 1467-8551
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 30
Issue 2
Pages 328-340
DOI https://doi.org/10.1111/1467-8551.12359
Keywords Management of Technology and Innovation; Strategy and Management; General Business, Management and Accounting
Public URL https://nottingham-repository.worktribe.com/output/1608759
Publisher URL https://onlinelibrary.wiley.com/doi/full/10.1111/1467-8551.12359
Additional Information This is the peer reviewed version of the following article: Ding, S. , Zhang, Y. and Duygun, M. (2019), Modeling Price Volatility Based on a Genetic Programming Approach. Brit J Manage, 30: 328-340, which has been published in final form at 10.1111/1467-8551.12359. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

Files




You might also like



Downloadable Citations