@article { , title = {Modeling Price Volatility based on a Genetic Programming Approach}, 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.}, doi = {10.1111/1467-8551.12359}, eissn = {1467-8551}, issn = {1045-3172}, issue = {2}, journal = {British Journal of Management}, pages = {328-340}, publicationstatus = {Published}, publisher = {Wiley}, url = {https://nottingham-repository.worktribe.com/output/1608759}, volume = {30}, keyword = {Management of Technology and Innovation, Strategy and Management, General Business, Management and Accounting}, year = {2019}, author = {Ding, Shusheng and Zhang, Yongmin and Duygun, Meryem} }