Causal Interventions in Bond Multi-Dealer-to-Client Platforms

ArXiv ID: 2506.18147 “View on arXiv”

Authors: Paloma Marín, Sergio Ardanza-Trevijano, Javier Sabio

Abstract

The digitalization of financial markets has shifted trading from voice to electronic channels, with Multi-Dealer-to-Client (MD2C) platforms now enabling clients to request quotes (RfQs) for financial instruments like bonds from multiple dealers simultaneously. In this competitive landscape, dealers cannot see each other’s prices, making a rigorous analysis of the negotiation process crucial to ensure their profitability. This article introduces a novel general framework for analyzing the RfQ process using probabilistic graphical models and causal inference. Within this framework, we explore different inferential questions that are relevant for dealers participating in MD2C platforms, such as the computation of optimal prices, estimating potential revenues and the identification of clients that might be interested in trading the dealer’s axes. We then move into analyzing two different approaches for model specification: a generative model built on the work of (Fermanian, Guéant, & Pu, 2017); and discriminative models utilizing machine learning techniques. Our results show that generative models can match the predictive accuracy of leading discriminative algorithms such as LightGBM (ROC-AUC: 0.742 vs. 0.743) while simultaneously enforcing critical business requirements, notably spread monotonicity.

Keywords: Probabilistic Graphical Models, Causal Inference, Quote Request (RfQ), Generative Models, Market Microstructure, Fixed Income

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced probabilistic graphical models and causal inference (structural equations, do-calculus analogs) to model RfQ dynamics, justifying high math complexity. Empirical rigor is strong, evidenced by backtest-ready analysis on a proprietary BBVA dataset comparing generative vs. discriminative models (ROC-AUC metrics), though it lacks full production backtesting code or live implementation details.

  flowchart TD
    A["Research Goal<br>Analyze RfQ Process & Dealer Profitability"] --> B["Data Input<br>MD2C Platform Trading Logs"]
    B --> C["Methodology<br>Probabilistic Graphical Model"]
    C --> D{"Computational Approaches"}
    D --> E["Generative Model<br>Spread Monotonicity Constraints"]
    D --> F["Discriminative Model<br>LightGBM ML Algorithm"]
    E & F --> G["Outcomes<br>Comparative Analysis"]
    G --> H{"Key Findings"}
    H --> I["Generative & Discriminative<br>Accuracy Comparable"]
    H --> J["Generative Model Enforces<br>Spread Monotonicity"]