Dealing with the Inventory Risk. A solution to the market making problem (1105.3115v5)

Abstract: Market makers continuously set bid and ask quotes for the stocks they have under consideration. Hence they face a complex optimization problem in which their return, based on the bid-ask spread they quote and the frequency at which they indeed provide liquidity, is challenged by the price risk they bear due to their inventory. In this paper, we consider a stochastic control problem similar to the one introduced by Ho and Stoll and formalized mathematically by Avellaneda and Stoikov. The market is modeled using a reference price $S_t$ following a Brownian motion with standard deviation $\sigma$, arrival rates of buy or sell liquidity-consuming orders depend on the distance to the reference price $S_t$ and a market maker maximizes the expected utility of its P&L over a finite time horizon. We show that the Hamilton-Jacobi-BeLLMan equations associated to the stochastic optimal control problem can be transformed into a system of linear ordinary differential equations and we solve the market making problem under inventory constraints. We also shed light on the asymptotic behavior of the optimal quotes and propose closed-form approximations based on a spectral characterization of the optimal quotes.

• The paper transforms complex HJB equations into a system of linear ODEs, simplifying the derivation of optimal trading quotes under inventory constraints.
• It employs a stochastic control framework to model market dynamics, including reference price behavior and liquidity-dependent order arrivals.
• Empirical backtests confirm the model's efficacy, offering actionable insights for enhancing market making strategies in electronic financial markets.

Analytical Approach to Market Making under Inventory Risk

The paper "Dealing with the Inventory Risk" addresses the market making problem, particularly focusing on the optimization challenges faced by market makers. Through a refined analytical approach, this paper builds on the stochastic control problem framework introduced by Ho and Stoll, and further formalized by Avellaneda and Stoikov. It revisits this problem by integrating a stochastic model of the market and explores the optimal strategy for maximizing the expected utility of a market maker's profit and loss (P&L) over a finite horizon within the constraints of inventory risk.

Core Contributions

The primary contribution of this paper is the transformation of the Hamilton-Jacobi-BeLLMan (HJB) equations, which are central to the optimization problem, into a system of linear ordinary differential equations (ODEs). This transformation not only simplifies the computational process but also opens up a pathway to asymptotically characterize optimal quotes. The authors employ a newly established change of variables to facilitate this transformation, enabling the provision of closed-form approximations for the optimal transaction quotes.

Model and Methodology

The research builds its model by considering a reference price $S_t$ that follows a Brownian motion with a standard deviation $\sigma$. Liquidity-consuming orders follow arrival rates dependent on the distance to this reference price. The framework focuses on maximizing the expected utility of P&L for a market maker, who operates under inventory constraints defined by finite bounds on inventory positions. The paper solves the problem analytically, yielding results reflecting the asymptotic behavior of optimal quotes using spectral characteristics.

Results and Analysis

The paper provides insightful results on the behavior of optimal quotes and their relation to market parameters such as volatility ($\sigma$), liquidity ($A$), and risk aversion ($\gamma$). The asymptotic analysis of the optimal quotes, obtained via spectral methods, allows the authors to suggest practical applications, including approximations that may enhance trading strategies. Empirical backtests further demonstrate the model's practicality and efficacy against naive strategies.

Implications and Future Directions

Practically, this research has significant implications for improving the efficiency of market making in electronic financial markets. It offers a structured methodology for dealing with inventory risks and provides a robust tool for quantitative financial practitioners to enhance market making strategies. Theoretically, it extends the framework of stochastic control problems, offering new insights into market microstructure modeling.

Future research could build on this work by generalizing the model to accommodate more complex market conditions and intensity functions, thereby broadening its applicability. Additionally, integrating "passive market impact" into the model could refine the understanding of liquidity provision effects on price formation processes, presenting an exciting avenue for subsequent exploration.