🤖 AI Summary
This paper addresses the core challenge in multi-to-multi bond trading platforms where dealers cannot observe counterparties’ quotes and struggle to ensure profitability. We propose the first general analytical framework integrating causal inference with probabilistic graphical models. Methodologically, we distinguish generative versus discriminative modeling approaches, explicitly capture the dynamic impact of RFQ (Request-for-Quote) negotiation mechanisms, identify key pricing drivers via causal interventions, and design prediction evaluation metrics tailored for optimal pricing. Our contribution lies in the first incorporation of structured causal modeling into electronic RFQ decision-making—overcoming the limitations of traditional black-box predictive models. Empirical results demonstrate significant improvements in price prediction accuracy and revenue estimation reliability, thereby enhancing dealers’ pricing capability and profitability under information asymmetry.
📝 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. We evaluate these methodologies using predictive metrics designed to assess their effectiveness in the context of optimal pricing, highlighting the relative benefits of using models that take into account the internal mechanisms of the negotiation process.