Size matters for OTC market makers: General results and dimensionality reduction techniques

@article{Bergault2020SizeMF,
  title={Size matters for OTC market makers: General results and dimensionality reduction techniques},
  author={Philippe Bergault and Olivier Gu'eant},
  journal={Mathematical Finance},
  year={2020},
  volume={31},
  pages={279 - 322}
}
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… 

Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality

TLDR
A discrete-time method inspired by reinforcement learning techniques, namely, a model-based deep actor-critic algorithm for approximating the optimal bid and ask quotes over a large universe of bonds in a model à la Avellaneda–Stoikov.

Closed-form Approximations in Multi-asset Market Making

TLDR
This article proposes closed-form approximations for the value functions of many multi-asset extensions of the Avellaneda–Stoikov model that can be used as heuristic evaluation functions, as initial value functions in reinforcement learning algorithms, and/or directly to design quoting strategies through a greedy approach.

Algorithmic market making for options

TLDR
This article shows that the seemingly high-dimensional stochastic optimal control problem of an option market maker is in fact tractable, and is characterized by a low-dimensional functional equation that can be solved numerically using a Euler scheme along with interpolation techniques, even for large portfolios.

Adaptive trading strategies across liquidity pools

TLDR
A Bayesian update of the model parameters is presented to take into account possibly changing market conditions and extensions to include short/long trading signals, market impact or hidden liquidity are proposed.

Algorithmic market making in foreign exchange cash markets: a new model for active market makers

In OTC markets, one of the main tasks of dealers / market makers consists in providing prices at which they agree to buy and sell the assets and securities they have in their scope. With ever

A mean-field game of market-making against strategic traders

We design a market-making model à la Avellaneda and Stoikov in which the market-takers act strategically, in the sense that they design their trading strategy based on an exogenous trading signal.

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

Dealers make money by providing liquidity to clients but face flow uncertainty and thus price risk. They can efficiently skew their prices and wait for clients to mitigate risk (internalization), or

Optimal incentives in a limit order book: a SPDE control approach

With the fragmentation of electronic markets, exchanges are now competing in order to attract trading activity on their platform. Consequently, they developed several regulatory tools to control

Algorithmic market making in foreign exchange cash markets with hedging and market impact

In OTC markets, one of the main tasks of dealers / market makers consists in providing prices at which they agree to buy and sell the assets and securities they have in their scope. With ever

Algorithmic market making in dealer markets with hedging and market impact

In dealer markets, dealers / market makers 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

References

SHOWING 1-10 OF 19 REFERENCES

Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality

TLDR
A discrete-time method inspired by reinforcement learning techniques, namely, a model-based deep actor-critic algorithm for approximating the optimal bid and ask quotes over a large universe of bonds in a model à la Avellaneda–Stoikov.

Optimal market making

ABSTRACT Market makers provide liquidity to other market participants: they propose prices at which they stand ready to buy and sell a wide variety of assets. They face a complex optimization problem

Option market making under inventory risk

We propose a mean-variance framework to analyze the optimal quoting policy of an option market maker. The market maker’s profits come from the bid-ask spreads received over the course of a trading

The Financial Mathematics of Market Liquidity : From Optimal Execution to Market Making

This book is among the first to present the mathematical models most commonly used to solve optimal execution problems and market making problems in finance. The Financial Mathematics of Market

Dealing with the inventory risk: a solution to the market making problem

Market makers continuously set bid and ask quotes for the stocks they have under consideration. Hence they face a complex optimization problem in which their return, based on the bid-ask spread they

Algorithmic Trading with Model Uncertainty

TLDR
Analytical solutions for the robust optimal strategies are provided, the resulting dynamic programming equations have classical solutions, and a proof of verification is provided about the behavior of the ambiguity averse MM.

Algorithmic market making for options

TLDR
This article shows that the seemingly high-dimensional stochastic optimal control problem of an option market maker is in fact tractable, and is characterized by a low-dimensional functional equation that can be solved numerically using a Euler scheme along with interpolation techniques, even for large portfolios.

A Stochastic Control Approach to Option Market Making

This paper presents a model for the market making of options on a liquid stock. The stock price follows a generic stochastic volatility model under the real-world probability measure . Market

Algorithmic market making: the case of equity derivatives

In this article, we tackle the problem of a market maker in charge of a book of equity derivatives on a single liquid underlying asset. By using an approximation of the portfolio in terms of its

Buy Low, Sell High: A High Frequency Trading Perspective

TLDR
A High Frequency trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders is developed, which shows that HF traders who do not include predictors of short-term-alpha in their strategies are driven out of the market.