Modeling Price Volatility based on a Genetic Programming Approach

Ding, Shusheng; Zhang, Yongmin; Duygun, Meryem

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.
• Contract Date: Mar 5, 2019