The impact of aggregator composition on liquidity provision in over-the-counter markets

点击翻译按钮获取中文摘要

The purpose of this project is to study the interaction between a trader and a given number of liquidity providers in an over-the-counter market. An example of an over-the-counter market is the FX market, which is the largest financial market in the world with a daily volume of several trillion dollars. Unlike the stock market, which is organised into exchanges with transparent limit order books, the FX market hinges on bi-lateral trading with liquidity providers. This is typically done in an aggregator, a technology that consolidates liquidity from several sources in the form of a limit order book.This research aims to address a void in the literature regarding issues specific to liquidity provision in a`blind auction' over-the-counter setting. This topic has received limited attention up to this point, in stark contrast with optimal market making and optimal execution problems in stock markets. This is mostly due to the differences in the information structures between these two types of markets. In a `blind auction 'over-the counter setting, a liquidity provider is unaware of the prices offered to a trader by other liquidity providers. In a stock market, however, market participants are typically aware of the exact shape of the limit order book. This data is also easily accessible, unlike the confidential prices offered by liquidity providers in an over-the-counter market.Due to the asymmetric information structure in over-the counter markets, it is challenging to find tractable models that are consistent with the microstructure. This research contributes to the literature by providing theoretical results in the setting when information is shared across the liquidity providers. While this is a strong assumption, the results provide a useful benchmark for experiments that are consistent with the microstructure. Such experiments involve liquidity providers having to compromise exploration and exploitation in an uncertain environment (for example, running multi-armed bandit algorithms). They shed light on practical aspects of over-the-counter market making such as algorithmic collusion.All in all, this research should improve the quality of liquidity provision in over-the-counter markets by illustrating how certain trader behaviours can lead to situations that are unfavorable for both the traders and the liquidity providers. For example, a trader may attempt to interact with as many liquidity providers as possible, but in fact one can argue about an optimal number of liquidity providers in an aggregator. A trader can also create a prisoner's dilemma setting by mixing liquidity providers with different risk management styles. While these problems are well known among liquidity providers, there is value in having rigorous mathematics as well as extensive simulations to support the messages conveyed to traders.This project falls within the EPSRC Statistics and applied probability research area. It is done in collaboration with the Fixed Income and Currencies Quantitative Trading department at Deutsche Bank AG.