用集成学习提升期权定价精度，兼顾理论与实证。
Option Pricing Using Ensemble Learning
- 采用参数迁移策略增强金融模拟的鲁棒性与真实性。
- 相比传统模型，集成方法在噪声环境下仍保持高精度。
- 融合金融理论评分机制，适合量化研究者参考。
集成学习具有灵活性、高精度和结构精炼的特点。作为计算金融中的关键环节，基于机器学习的期权定价需要高预测准确率和低结构复杂度，这恰好契合集成学习的优势。本文研究集成学习在期权定价中的应用，并与经典机器学习模型进行对比，评估其在准确性、局部特征提取及抗噪能力方面的表现。提出一种新型实验策略，通过跨实验的参数迁移提升金融模拟的鲁棒性与真实性。在此基础上，构建包含评分机制与加权评估策略的评价体系，明确强调金融理论的基础作用，实现理论与计算方法的有序融合。此外，研究还分析了滑动窗口技术与噪声之间的交互关系，揭示出潜在关联，为机器学习与数据科学领域的持续研究提供线索。
原文摘要 · Abstract (English)
Ensemble learning is characterized by flexibility, high precision, and refined structure. As a critical component within computational finance, option pricing with machine learning requires both high predictive accuracy and reduced structural complexity-features that align well with the inherent advantages of ensemble learning. This paper investigates the application of ensemble learning to option pricing, and conducts a comparative analysis with classical machine learning models to assess their performance in terms of accuracy, local feature extraction, and robustness to noise. A novel experimental strategy is introduced, leveraging parameter transfer across experiments to improve robustness and realism in financial simulations.Building upon this strategy, an evaluation mechanism is developed that incorporates a scoring strategy and a weighted evaluation strategy explicitly emphasizing the foundational role of financial theory. This mechanism embodies an orderly integration of theoretical finance and computational methods. In addition, the study examines the interaction between sliding window technique and noise, revealing nuanced patterns that suggest a potential connection relevant to ongoing research in machine learning and data science.
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