  The over-the-counter (OTC) market is characterized by a unique feature that
allows market makers to adjust bid-ask spreads based on order size. However,
this flexibility introduces complexity, transforming the market-making problem
into a high-dimensional stochastic control problem that presents significant
challenges. To address this, this paper proposes an innovative solution
utilizing reinforcement learning techniques to tackle the OTC market-making
problem. By assuming a linear inverse relationship between market order arrival
intensity and bid-ask spreads, we demonstrate the optimal policy for bid-ask
spreads follows a Gaussian distribution. We apply two reinforcement learning
algorithms to conduct a numerical analysis, revealing the resulting return
distribution and bid-ask spreads under different time and inventory levels.

Trading and Market Microstructure