在债券多经销商到客户平台上的因果干预.
Paloma Marín Martínez1,2, Sergio Ardanza-Trevijano2,3, Javier Sabio1
1Corporate & Investment Banking, BBVA, Madrid, Madrid, Spain.
PloS one
|January 27, 2026
概括
本研究引入了一个新的框架,用于分析多经销商到客户平台上的电子交易谈判. 生成模型提供了竞争力的准确性,同时确保了经销商的基本商业规则.
科学领域:
- 量化金融 量化金融
- 计算经济学的计算经济学
- 金融中的机器学习
背景情况:
- 金融市场越来越数字化,交易从语音转向电子道.
- 多个经销商到客户 (MD2C) 平台为金融工具提供同时报价请求 (RfQ).
- 在MD2C平台上竞争对手定价的不透明性需要对经销商利能力进行严格的分析.
研究的目的:
- 引入一个新的框架来分析MD2C平台上的RfQ过程.
- 探索对经销商利能力至关重要的推断问题,包括最佳定价和收入估计.
- 为了比较RfQ分析的生成式和歧视式建模方法.
主要方法:
- 利用概率的图形模型和因果推理为一般的分析框架.
- 基于Fermanian,Guéant, & Pu (2017) 的生成模型开发.
- 在歧视性模型中使用机器学习技术,包括LightGBM.
主要成果:
- 生成模型的预测准确度与领先的歧视性算法相提并论 (ROC-AUC:0.742与0.743).
- 拟议的框架成功地结合了关键的业务约束,如扩散单调性.
- 证明了该框架对定价,收入估计和客户识别的实用性.
结论:
- 使用概率图形模型和因果推理的新框架有效地分析MD2C RfQ过程.
- 生成型模型为歧视性机器学习提供了可行的替代方案,平衡了预测能力和商业规则的遵守.
- 这些发现为在竞争激烈的电子交易环境中导航的经销商提供了宝贵的见解.
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