Modeling Price Volatility Based on a Genetic Programming Approach
提出一种基于遗传规划的新模型LIQ-GARCH，用于预测商品价格波动性，在CRB指数、WTI原油期货和BDI指数上比传统GARCH模型更准确，可帮助企业设计风险管理策略。
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, the authors 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.