Paper: arXiv 2406.12983

Abstract

A corporate bond trader in a typical sell side institution such as a bank provides liquidity to the market participants by buying/selling securities and maintaining an inventory. Upon receiving a request for a buy/sell price quote (RFQ), the trader provides a quote by adding a spread over a \textit{“prevalent market price”}. For illiquid bonds, the market price is harder to observe, and traders often resort to available benchmark bond prices (such as MarketAxess, Bloomberg, etc.). In \cite{“Bergault2023ModelingLI”}, the concept of \textit{“Fair Transfer Price”} for an illiquid corporate bond was introduced which is derived from an infinite horizon stochastic optimal control problem (for maximizing the trader’s expected P&L, regularized by the quadratic variation). In this paper, we consider the same optimization objective, however, we approach the estimation of an optimal bid-ask spread quoting strategy in a data driven manner and show that it can be learned using Reinforcement Learning. Furthermore, we perform extensive outcome analysis to examine the reasonableness of the trained agent’s behavior.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper relies on advanced stochastic optimal control and Hamilton-Jacobi-Bellman equations (high math), while also detailing a data-driven RL approach with outcome analysis, though specifics on backtest metrics and implementation details are limited in the provided excerpt.

Research Flowchart

  flowchart TD
  A["Research Goal: Learn optimal bid-ask<br>spread quoting for illiquid bonds"] --> B["Methodology: Reinforcement Learning<br>vs Stochastic Optimal Control"]
  
  B --> C{"Data/Inputs"}
  C --> C1["Corporate Bond RFQs"]
  C --> C2["Trade Execution Records"]
  C --> C3["Market Reference Prices"]
  
  C --> D["Computational Process"]
  D --> D1["RL Agent Training<br>Deep Q-Network"]
  D --> D2["Compute Fair Transfer Price<br>Stochastic Control Solution"]
  D --> D3["Compare Quote Strategies"]
  
  D --> E["Key Findings/Outcomes"]
  E --> E1["RL learns spread <br>driven by inventory risk"]
  E --> E2["Optimal spread inversely <br>correlated with liquidity"]
  E --> E3["RL agents adapt to market <br>regimes without manual rules"]