Algorithmic Trading in a Microstructural Limit Order Book Model

Abergel, Frédéric; Huré, Côme; Pham, Huyên

doi:10.1201/9781003265399-36

Cited by 1 publication

(2 citation statements)

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“…They modeled a market maker as an agent placing limit orders of constant size at the best bid and at the best ask, and solved the problem faced by a risk-averse market maker. More recently, in Abergel et al (2020), the authors proposed a different model for the limit order book (first introduced in Abergel et al ( 2016)), in which the limit orders, market orders, and cancel orders arrive according to Markov jump processes with intensities depending only on the state of the limit order book. They considered the case of a market maker trading in this limit order book, and proposed a quantization-based algorithm to numerically solve the resulting high-dimensional problem.…”

Section: Introductionmentioning

confidence: 99%

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Algorithmic market making in dealer markets with hedging and market impact

Barzykin

Bergault

Guéant

2022

Mathematical Finance

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In dealer markets, dealers provide prices at which they agree to buy and sell the assets and securities they have in their scope. With ever increasing trading volume, this quoting task has to be done algorithmically in most markets such as foreign exchange (FX) markets or corporate bond markets. Over the last 10 years, many mathematical models have been designed that can be the basis of quoting algorithms in dealer markets. Nevertheless, in most (if not all) models, the dealer is a pure internalizer, setting quotes and waiting for clients. However, on many dealer markets, dealers also have access to an interdealer market or even public trading venues where they can hedge part of their inventory. In this paper, we propose a model taking this possibility into account therefore allowing dealers to externalize part of their risk. The model displays an important feature well known to practitioners that within a certain inventory range, the dealer internalizes the flow by appropriately adjusting the quotes and starts externalizing outside of that range. The larger the franchise, the wider is the inventory range suitable for pure internalization. The model is illustrated numerically with realistic parameters for USDCNH spot market.

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Section: Introductionmentioning

confidence: 99%

“…More recently, in Abergel et al. (2020), the authors proposed a different model for the limit order book (first introduced in Abergel et al. (2016)), in which the limit orders, market orders, and cancel orders arrive according to Markov jump processes with intensities depending only on the state of the limit order book.…”

Section: Introductionmentioning

confidence: 99%