计算机应用与软件2025，Vol.42Issue(5)：36-42,7.DOI:10.3969/j.issn.1000-386x.2025.05.006
基于改进Stacking集成学习的期权隐含波动率趋势预测
TREND PREDICTION OF OPTION IMPLIED VOLATILITY BASED ON IMPROVED STACKING ENSEMBLE LEARNING
摘要
Abstract
In order to improve the investment decision making level of option investors,the Stacking ensemble learning framework was used to predict the rise and fall trend of the implied volatility of SSE 50ETF options.Four tree-based ensemble models,random forest(RF),AdaBoost,Gradient Boosting Decision Tree(GBDT)and XGBoost,were selected as the base classifier to train the original data and carry out embedded feature selection.Again,the feature selection results were introduced into the base classifier,and the Stacking ensemble model based on feature selection was constructed by changing the different input features of the base classifier.The experimental results show that the improved Stacking ensemble model achieves significantly improvement on the precision accuracy and F1-score compared with the base classifier and the traditional Stacking model,and achieves a more ideal prediction effect.关键词
期权/隐含波动率/特征选择/StackingKey words
Option/Implied volatility/Feature selection/Stacking分类
信息技术与安全科学引用本文复制引用
张芹,刘家鹏,田冬梅,越瀚..基于改进Stacking集成学习的期权隐含波动率趋势预测[J].计算机应用与软件,2025,42(5):36-42,7.基金项目
国家社会科学基金项目(18BGL224). （18BGL224）
