Algorithmic Market Making in Spot Precious Metals

Alexander Barzykin, Philippe Bergault, Olivier Guéant
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Abstract

The primary challenge of market making in spot precious metals is navigating the liquidity that is mainly provided by futures contracts. The Exchange for Physical (EFP) spread, which is the price difference between futures and spot, plays a pivotal role and exhibits multiple modes of relaxation corresponding to the diverse trading horizons of market participants. In this paper, we introduce a novel framework utilizing a nested Ornstein-Uhlenbeck process to model the EFP spread. We demonstrate the suitability of the framework for maximizing the expected P\&L of a market maker while minimizing inventory risk across both spot and futures. Using a computationally efficient technique to approximate the solution of the Hamilton-Jacobi-Bellman equation associated with the corresponding stochastic optimal control problem, our methodology facilitates strategy optimization on demand in near real-time, paving the way for advanced algorithmic market making that capitalizes on the co-integration properties intrinsic to the precious metals sector.
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贵金属现货的算法做市
贵金属现货做市的主要挑战是如何驾驭主要由期货合约提供的流动性。期现价差(Exchange forPhysical,EFP)是期货与现货之间的价差,它起着举足轻重的作用,并表现出与市场参与者不同的交易视野相对应的多种放松模式。在本文中,我们提出了一个利用嵌套奥恩斯坦-乌伦贝克过程来模拟 EFP 价差的新框架。我们证明了该框架的适用性,即在最大限度地降低现货和期货库存风险的同时,最大限度地提高做市商的预期收益(P&L)。我们的方法使用一种计算效率高的技术来近似求解与相应随机最优控制问题相关的汉密尔顿-雅各比-贝尔曼方程,从而能够近乎实时地按需优化策略,为利用贵金属行业固有的协整特性进行高级算法做市铺平了道路。
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