桂林理工大学学报2024，Vol.44Issue(1)：162-167,6.DOI:10.3969/j.issn.1674-9057.2024.01.023
基于CEEMD-LSTM-Adaboost模型的白糖期货跨期套利策略
Arbitrage strategies of sugar futures based on CEEMD-LSTM-Adaboost model
摘要
Abstract
The 5-minute high-frequency data of white sugar futures contracts SR2201 and SR2109 are taken as the research object.Under the condition that there is a long-term equilibrium relationship between them,GARCH model is constructed to describe the ARCH effect of the residual.When the complementary set empiri-cal mode decomposition(CEEMD)method is combined with the long-term and short-term memory network(LSTM)and adaptive lifting algorithm(Adaboost),arbitrage operation is carried out by predicting the rise and fall of price difference,setting different opening and closing thresholds,and making a comparative study of four neural network arbitrage strategies in the sample range.Results show that the neural network arbitrage strategy based on CEEMD-LSTM-Adaboost model is feasible and effective in white sugar futures market,and it has more advantages than BP,LSTM and LSTM-Adaboost neural networks in terms of prediction accuracy and arbitrage effect.关键词
跨期套利/CEEMD-LSTM-Adaboost模型/白糖期货Key words
intertemporal arbitrage/CEEMD-LSTM-Adaboost combination model/sugar futures分类
管理科学引用本文复制引用
甘柳燕,唐国强,蒋文希,覃良文..基于CEEMD-LSTM-Adaboost模型的白糖期货跨期套利策略[J].桂林理工大学学报,2024,44(1):162-167,6.基金项目
国家自然科学基金项目(11961013 （）
61763008) （）
