Market Microstructure

Modelling crypto markets by multi-agent reinforcement learning ArXiv ID: 2402.10803 “View on arXiv” Authors: Unknown Abstract Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance’s daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with reinforcement learning (RL) techniques in order to model crypto markets. This integration is designed to emulate, with a bottom-up approach to complexity inference, both individual and collective agents, ensuring robustness in the recent volatile conditions of such markets and during the COVID-19 era. A key feature of our model also lies in the fact that its autonomous agents perform asset price valuation based on two sources of information: the market prices themselves, and the approximation of the crypto assets fundamental values beyond what those market prices are. Our MAS calibration against real market data allows for an accurate emulation of crypto markets microstructure and probing key market behaviors, in both the bearish and bullish regimes of that particular time period. ...

Optimal Automated Market Makers: Differentiable Economics and Strong Duality ArXiv ID: 2402.09129 “View on arXiv” Authors: Unknown Abstract The role of a market maker is to simultaneously offer to buy and sell quantities of goods, often a financial asset such as a share, at specified prices. An automated market maker (AMM) is a mechanism that offers to trade according to some predetermined schedule; the best choice of this schedule depends on the market maker’s goals. The literature on the design of AMMs has mainly focused on prediction markets with the goal of information elicitation. More recent work motivated by DeFi has focused instead on the goal of profit maximization, but considering only a single type of good (traded with a numeraire), including under adverse selection (Milionis et al. 2022). Optimal market making in the presence of multiple goods, including the possibility of complex bundling behavior, is not well understood. In this paper, we show that finding an optimal market maker is dual to an optimal transport problem, with specific geometric constraints on the transport plan in the dual. We show that optimal mechanisms for multiple goods and under adverse selection can take advantage of bundling, both improved prices for bundled purchases and sales as well as sometimes accepting payment “in kind.” We present conjectures of optimal mechanisms in additional settings which show further complex behavior. From a methodological perspective, we make essential use of the tools of differentiable economics to generate conjectures of optimal mechanisms, and give a proof-of-concept for the use of such tools in guiding theoretical investigations. ...

The puzzle of Carbon Allowance spread ArXiv ID: 2405.12982 “View on arXiv” Authors: Unknown Abstract A growing number of contributions in the literature have identified a puzzle in the European carbon allowance (EUA) market. Specifically, a persistent cost-of-carry spread (C-spread) over the risk-free rate has been observed. We are the first to explain the anomalous C-spread with the credit spread of the corporates involved in the emission trading scheme. We obtain statistical evidence that the C-spread is cointegrated with both this credit spread and the risk-free interest rate. This finding has a relevant policy implication: the most effective solution to solve the market anomaly is including the EUA in the list of European Central Bank eligible collateral for refinancing operations. This change in the ECB monetary policy operations would greatly benefit the carbon market and the EU green transition. ...

Exploring the Impact: How Decentralized Exchange Designs Shape Traders’ Behavior on Perpetual Future Contracts ArXiv ID: 2402.03953 “View on arXiv” Authors: Unknown Abstract In this paper, we analyze traders’ behavior within both centralized exchanges (CEXs) and decentralized exchanges (DEXs), focusing on the volatility of Bitcoin prices and the trading activity of investors engaged in perpetual future contracts. We categorize the architecture of perpetual future exchanges into three distinct models, each exhibiting unique patterns of trader behavior in relation to trading volume, open interest, liquidation, and leverage. Our detailed examination of DEXs, especially those utilizing the Virtual Automated Market Making (VAMM) Model, uncovers a differential impact of open interest on long versus short positions. In exchanges which operate under the Oracle Pricing Model, we find that traders primarily act as price takers, with their trading actions reflecting direct responses to price movements of the underlying assets. Furthermore, our research highlights a significant propensity among less informed traders to overreact to positive news, as demonstrated by an increase in long positions. This study contributes to the understanding of market dynamics in digital asset exchanges, offering insights into the behavioral finance for future innovation of decentralized finance. ...

Stylized Facts and Market Microstructure: An In-Depth Exploration of German Bond Futures Market ArXiv ID: 2401.10722 “View on arXiv” Authors: Unknown Abstract This paper presents an in-depth analysis of stylized facts in the context of futures on German bonds. The study examines four futures contracts on German bonds: Schatz, Bobl, Bund and Buxl, using tick-by-tick limit order book datasets. It uncovers a range of stylized facts and empirical observations, including the distribution of order sizes, patterns of order flow, and inter-arrival times of orders. The findings reveal both commonalities and unique characteristics across the different futures, thereby enriching our understanding of these markets. Furthermore, the paper introduces insightful realism metrics that can be used to benchmark market simulators. The study contributes to the literature on financial stylized facts by extending empirical observations to this class of assets, which has been relatively underexplored in existing research. This work provides valuable guidance for the development of more accurate and realistic market simulators. ...

Neural Hawkes: Non-Parametric Estimation in High Dimension and Causality Analysis in Cryptocurrency Markets ArXiv ID: 2401.09361 “View on arXiv” Authors: Unknown Abstract We propose a novel approach to marked Hawkes kernel inference which we name the moment-based neural Hawkes estimation method. Hawkes processes are fully characterized by their first and second order statistics through a Fredholm integral equation of the second kind. Using recent advances in solving partial differential equations with physics-informed neural networks, we provide a numerical procedure to solve this integral equation in high dimension. Together with an adapted training pipeline, we give a generic set of hyperparameters that produces robust results across a wide range of kernel shapes. We conduct an extensive numerical validation on simulated data. We finally propose two applications of the method to the analysis of the microstructure of cryptocurrency markets. In a first application we extract the influence of volume on the arrival rate of BTC-USD trades and in a second application we analyze the causality relationships and their directions amongst a universe of 15 cryptocurrency pairs in a centralized exchange. ...

Randomized Signature Methods in Optimal Portfolio Selection ArXiv ID: 2312.16448 “View on arXiv” Authors: Unknown Abstract We present convincing empirical results on the application of Randomized Signature Methods for non-linear, non-parametric drift estimation for a multi-variate financial market. Even though drift estimation is notoriously ill defined due to small signal to noise ratio, one can still try to learn optimal non-linear maps from data to future returns for the purposes of portfolio optimization. Randomized Signatures, in contrast to classical signatures, allow for high dimensional market dimension and provide features on the same scale. We do not contribute to the theory of Randomized Signatures here, but rather present our empirical findings on portfolio selection in real world settings including real market data and transaction costs. ...

Scalable Agent-Based Modeling for Complex Financial Market Simulations ArXiv ID: 2312.14903 “View on arXiv” Authors: Unknown Abstract In this study, we developed a computational framework for simulating large-scale agent-based financial markets. Our platform supports trading multiple simultaneous assets and leverages distributed computing to scale the number and complexity of simulated agents. Heterogeneous agents make decisions in parallel, and their orders are processed through a realistic, continuous double auction matching engine. We present a baseline model implementation and show that it captures several known statistical properties of real financial markets (i.e., stylized facts). Further, we demonstrate these results without fitting models to historical financial data. Thus, this framework could be used for direct applications such as human-in-the-loop machine learning or to explore theoretically exciting questions about market microstructure’s role in forming the statistical regularities of real markets. To the best of our knowledge, this study is the first to implement multiple assets, parallel agent decision-making, a continuous double auction mechanism, and intelligent agent types in a scalable real-time environment. ...

Dealer Strategies in Agent-Based Models ArXiv ID: 2312.05943 “View on arXiv” Authors: Unknown Abstract This paper explores the utility of agent-based simulations in realistically modelling market structures and sheds light on the nuances of optimal dealer strategies. It underscores the contrast between conclusions drawn from probabilistic modelling and agent-based simulations, but also highlights the importance of employing a realistic test bed to analyse intricate dynamics. This is achieved by extending the agent-based model for auction markets by \cite{“Chiarella.2008”} to include liquidity providers. By constantly and passively quoting, the dealers influence their own wealth but also have ramifications on the market as a whole and the other participating agents. Through synthetic market simulations, the optimal behaviour of different dealer strategies and their consequences on market dynamics are examined. The analysis reveals that dealers exhibiting greater risk aversion tend to yield better performance outcomes. The choice of quote sizes by dealers is strategy-dependent: one strategy demonstrates enhanced performance with larger quote sizes, whereas the other strategy show a better results with smaller ones. Increasing quote size shows positive influence on the market in terms of volatility and kurtosis with both dealer strategies. However, the impact stemming from larger risk aversion is mixed. While one of the dealer strategies shows no discernible effect, the other strategy results in mixed outcomes, encompassing both positive and negative effects. ...

An explanation for the distribution characteristics of stock returns ArXiv ID: 2312.02472 “View on arXiv” Authors: Unknown Abstract Observations indicate that the distributions of stock returns in financial markets usually do not conform to normal distributions, but rather exhibit characteristics of high peaks, fat tails and biases. In this work, we assume that the effects of events or information on prices obey normal distribution, while financial markets often overreact or underreact to events or information, resulting in non normal distributions of stock returns. Based on the above assumptions, we propose a reaction function for a financial market reacting to events or information, and a model based on it to describe the distribution of real stock returns. Our analysis of the returns of China Securities Index 300 (CSI 300), the Standard & Poor’s 500 Index (SPX or S&P 500) and the Nikkei 225 Index (N225) at different time scales shows that financial markets often underreact to events or information with minor impacts, overreact to events or information with relatively significant impacts, and react slightly stronger to positive events or information than to negative ones. In addition, differences in financial markets and time scales of returns can also affect the shapes of the reaction functions. ...