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on Market Microstructure |
| By: | Dominic Bauer; Derick Diana; Tim Gebbie |
| Abstract: | We use random walks to simulate the fluid limit of two coupled diffusive limit order books to model correlation emergence. The model implements the arrival, cancellation and diffusion of orders coupled by a pairs trader profiting from the mean-reversion between the two order books in the fluid limit for a Lit order book with vanishing boundary conditions and order volume conservation. We are able to demonstrate the recovery of an Epps effect from this. We discuss how various stylised facts depend on the model parameters and the numerical scheme and discuss the various strengths and weaknesses of the approach. We demonstrate how the Epps effect depends on different choices of time and price discretisation. This shows how an Epps effect can emerge without recourse to market microstructure noise relative to a latent model but can rather be viewed as an emergent property arising from trader interactions in a world of asynchronous events. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.03181 |
| By: | Wee Ling Tan; Stephen Roberts; Stefan Zohren |
| Abstract: | We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.21791 |
| By: | Bidder, Rhys (King’s Business School); Coen, Jamie (Imperial College London); Lepore, Caterina (International Monetary Fund); Silvestri, Laura (Bank of England) |
| Abstract: | Using novel data on bond trading in the UK, we develop a new measure of selling pressure that can be applied to any trader. We identify exogenous selling pressure in a bond using traders’ sales of other, unrelated bonds. The price impact of a sale depends on who is selling: sales by dealers and hedge funds generate significantly larger impacts than equally sized sales by other investors. We rationalise our findings using a model of differentially informed investors. All else equal, our results suggest that more attention should be devoted to risks to financial stability from these impactful sellers. |
| Keywords: | Fire sales; liquidity; fixed income; financial stability |
| JEL: | G10 G12 G21 G23 |
| Date: | 2024–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1088 |
| By: | Yuheng Zheng; Zihan Ding |
| Abstract: | This paper establishes a new and comprehensive theoretical analysis for the application of reinforcement learning (RL) in high-frequency market making. We bridge the modern RL theory and the continuous-time statistical models in high-frequency financial economics. Different with most existing literature on methodological research about developing various RL methods for market making problem, our work is a pilot to provide the theoretical analysis. We target the effects of sampling frequency, and find an interesting tradeoff between error and complexity of RL algorithm when tweaking the values of the time increment $\Delta$ $-$ as $\Delta$ becomes smaller, the error will be smaller but the complexity will be larger. We also study the two-player case under the general-sum game framework and establish the convergence of Nash equilibrium to the continuous-time game equilibrium as $\Delta\rightarrow0$. The Nash Q-learning algorithm, which is an online multi-agent RL method, is applied to solve the equilibrium. Our theories are not only useful for practitioners to choose the sampling frequency, but also very general and applicable to other high-frequency financial decision making problems, e.g., optimal executions, as long as the time-discretization of a continuous-time markov decision process is adopted. Monte Carlo simulation evidence support all of our theories. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.21025 |
| By: | Colliard, Jean-Edouard (HEC Paris); Foucault, Thierry (HEC Paris); Lovo, Stefano (HEC Paris) |
| Abstract: | We let ``Algorithmic Market Makers'' (AMs), using Q-learning algorithms, determine prices for a risky asset in a standard market making game with adverse selection and compare these prices to the Nash equilibrium of the game. We observe that AMs effectively adapt to adverse selection, adjusting prices post-trade as anticipated. However, AMs charge a markup over the competitive price and this markup increases when adverse selection costs decrease, in contrast to the predictions of the Nash equilibrium. We attribute this unexpected pattern to the diminished learning capacity of AMs when faced with increased profit variance. |
| Keywords: | Algorithmic pricing; Market Making; Adverse Selection; Market Power; Reinforcement learning |
| JEL: | D43 G10 G14 |
| Date: | 2022–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:ebg:heccah:1459 |