In most over‐the‐counter (OTC) markets, a small number of market makers provide liquidity to other market participants. More precisely, for a list of assets, they set prices at which they agree to buy and sell. Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money while mitigating the risk associated with holding inventory in a volatile market. Many market‐making models have been proposed in the academic literature, most of them dealing with single‐asset market making whereas market makers are usually in charge of a long list of assets. The rare models tackling multiasset market making suffer however from the curse of dimensionality when it comes to the numerical approximation of the optimal quotes. The goal of this paper is to propose a dimensionality reduction technique to address multiasset market making by using a factor model. Moreover, we generalize existing market‐making models by the addition of an important feature: the existence of different transaction sizes and the possibility for the market makers in OTC markets to answer different prices to requests with different sizes.

中文翻译：

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场外交易做市商的规模很重要：总体结果和降维技术

在大多数场外交易（OTC）市场中，少数做市商为其他市场参与者提供流动性。更准确地说，对于资产清单，他们确定同意买卖的价格。因此，做市商面临一个有趣的优化问题：他们需要选择要价和要价以赚钱，同时减轻与在动荡的市场中持有库存有关的风险。学术文献中提出了许多做市模型，其中大多数涉及单一资产做市，而做市商通常负责一长串资产。但是，在涉及最佳报价的数值逼近时，处理多资产做市的稀有模型会遭受维度的诅咒。本文的目的是提出一种降维技术，通过使用因子模型来解决多资产做市商问题。此外，我们通过增加一个重要功能来概括现有的做市模型：存在不同的交易规模，场外交易市场中的做市商有可能以不同的价格回答不同的价格。