Market making by an FX dealer: tiers, pricing ladders and hedging rates for optimal risk control

This paper presents an optimal control framework for market making in the foreign exchange (FX) market, addressing the internalization vs. externalization dilemma faced by dealers. Dealers make money by providing liquidity to clients but face flow uncertainty and price risk. They can efficiently skew their prices and wait for clients to mitigate risk (internalization), or trade with other dealers in the open market to hedge their position and reduce their inventory (externalization). The better control associated with externalization comes with transaction costs and market impact. The authors propose a model of algorithmic market making with pricing and hedging in a unified stochastic optimal control framework, constituting an important encounter between two problems that have attracted academic and practitioners' interest in the last decade: optimal market making and optimal execution. The model allows one to set optimal pricing ladder and determine optimal hedging rate in external liquidity pools as functions of the inventory, risk aversion, and market-driven parameters. In this paper, the authors generalize their algorithmic market making model further to include a better modeling of the trading flow by introducing tiers that account for the complexity of the D2C segment of the FX market. Tiers are used by market makers to distinguish both different sources of trading flow and natural diversity among clients. The authors describe their modeling approach for trading flow using intensity parameters estimation techniques, demonstrating that tiers can be conveniently defined using clustering techniques on intensity parameters. The authors present their algorithmic market making model while looking into differences in optimal strategies for different tiers. By analyzing typical risk neutralization time and internalization ratio derived from the model, they prove the existence of a pure flow internalization area or an inventory threshold below which it is optimal for dealers not to externalize. This threshold is derived from a subtle balance between uncertainty, execution cost, and market impact. The results presented are part of research works carried out within HSBC FX Research Initiative; however, views expressed are those of the authors and do not necessarily reflect the views or practices at HSBC. The authors acknowledge Richard Anthony and Paris Pennesi for helpful discussions and support throughout the project.

Optimal Control Framework for Market Making in the Foreign Exchange (FX) Market

The foreign exchange (FX) market is a global decentralized market where currencies are traded. It is one of the largest and most liquid markets in the world, with an average daily turnover of more than $5 trillion. Dealers make money by providing liquidity to clients but face flow uncertainty and price risk. They can either internalize their trades or externalize them to other dealers in the open market. The better control associated with externalization comes with transaction costs and market impact, making it difficult for dealers to decide which option is best for them.

In this paper, researchers present an optimal control framework for algorithmic market making that addresses this dilemma faced by dealers. This model allows one to set optimal pricing ladder and determine optimal hedging rate in external liquidity pools as functions of inventory, risk aversion, and market-driven parameters. Furthermore, they generalize their algorithmic market making model further to include a better modeling of the trading flow by introducing tiers that account for the complexity of the D2C segment of the FX market.

Internalization vs Externalization

Dealers make money by providing liquidity to clients but face flow uncertainty and price risk when doing so. To mitigate these risks, they have two options: internalizing or externalizing their trades. Internalization involves setting prices on a dealer’s own platform that will attract customers who want to buy or sell currency pairs; while externalization involves trading with other dealers in order to hedge positions or reduce inventory levels without having direct contact with customers themselves.

Internalization has its advantages such as better control over pricing decisions; however it also has its drawbacks such as increased exposure to uncertain flows from customers which can lead to losses if not managed properly. On the other hand, externalization provides more flexibility when managing risk since it allows traders access to different sources of liquidity; however it also comes at a cost due to transaction fees and potential losses due to adverse movements in prices during execution time frames (market impact).

Algorithmic Market Making Model

The authors propose a model of algorithmic market making with pricing and hedging in a unified stochastic optimal control framework which constitutes an important encounter between two problems that have attracted academic interest over recent years: optimal market making and optimal execution strategies . This model allows one set up an optimal pricing ladder based on inventory levels as well as determine optimal hedging rates within external liquidity pools depending on factors such as risk aversion level and prevailing conditions within financial markets .

Furthermore ,the authors extend their algorithmic market making model further by introducing tiers into their approach . Tiers are used by dealers distinguish different sources of trading flow from each other , allowing them gain insight into how much exposure they may be taking on certain types orders . The authors describe their modeling approach using intensity parameters estimation techniques , demonstrating that tiers can be conveniently defined through clustering techniques applied onto intensity parameters .

Analysis & Results

By analyzing typical risk neutralisation time frames derived from their models ,the authors prove existence pure flow internalisation area -or inventory threshold- below which becomes more efficient not engage any form externalisation activities . This threshold is determined through subtle balance between uncertainty ,execution cost & potential losses incurred due adverse movements during execution times frames (market impact).

The results presented are part HSBC FX Research Initiative ; however views expressed those authors do necessarily reflect views practices HSBC . Authors acknowledge Richard Anthony Paris Pennesi helpful discussions support throughout project .