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on Market Microstructure |
| By: | Andrey Urusov; Rostislav Berezovskiy; Anatoly Krestenko; Andrei Kornilov |
| Abstract: | Since the launch of Uniswap and other AMM protocols, the DeFi industry has evolved from simple constant product functions with uniform liquidity distribution across the entire price axis to more advanced mechanisms that allow Liquidity Providers (LPs) to concentrate capital within selected price ranges. This evolution has introduced new research challenges focused on optimizing capital allocation in Decentralized Exchanges (DEXs) under dynamic market conditions. In this paper, we present a methodology for finding optimal liquidity provision strategies in DEXs within a specific family of $\tau$-reset strategies. The approach is detailed step by step and includes an original method for approximating historical liquidity within active pool ranges using a parametric model that does not rely on historical liquidity data. We find optimal LP strategies using a machine learning approach, evaluate performance over an out-of-time period, and compare the resulting strategies against a uniform benchmark. All experiments were conducted using a custom backtesting framework specifically developed for Concentrated Liquidity Market Makers (CLMMs). The effectiveness and flexibility of the proposed methodology are demonstrated across various Uniswap v3 trading pairs, and also benchmarked against an alternative backtesting and strategy development tool. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.15338 |
| By: | Perukrishnen Vytelingum; Rory Baggott; Namid Stillman; Jianfei Zhang; Dingqiu Zhu; Tao Chen; Justin Lyon |
| Abstract: | In this paper, we describe a novel agent-based approach for modelling the transaction cost of buying or selling an asset in financial markets, e.g., to liquidate a large position as a result of a margin call to meet financial obligations. The simple act of buying or selling in the market causes a price impact and there is a cost described as liquidity risk. For example, when selling a large order, there is market slippage -- each successive trade will execute at the same or worse price. When the market adjusts to the new information revealed by the execution of such a large order, we observe in the data a permanent price impact that can be attributed to the change in the fundamental value as market participants reassess the value of the asset. In our ABM model, we introduce a novel mechanism where traders assume orderflow is informed and each trade reveals some information about the value of the asset, and traders update their belief of the fundamental value for every trade. The result is emergent, realistic price impact without oversimplifying the problem as most stylised models do, but within a realistic framework that models the exchange with its protocols, its limit orderbook and its auction mechanism and that can calculate the transaction cost of any execution strategy without limitation. Our stochastic ABM model calculates the costs and uncertainties of buying and selling in a market by running Monte-Carlo simulations, for a better understanding of liquidity risk and can be used to optimise for optimal execution under liquidity risk. We demonstrate its practical application in the real world by calculating the liquidity risk for the Hang-Seng Futures Index. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.15296 |
| By: | San Román, Diego |
| Abstract: | Research in anthropology and neuroscience has shown that people have a cognitive limit on the number of stable relationships they can maintain. In this spirit, we consider a network formation game in which the cost of link formation is increasing in the agent's degree. In this class of games, as opposed to commonly studied games with a fixed cost of link formation, the order in which agents form the network (order of play) determines its final structure. In particular, we find that only certain orders of play can explain the formation of circle and complete bipartite networks. We also find that there is multiplicity of equilibria only when marginal costs of link formation are intermediate. Our results show as well that some orders of play are better than others for predicting the equilibrium structure when it is not unique, and that playing last is usually harmful. |
| Keywords: | sequential network formation, pairwise stability, order of play, costs of link formation increasing in degree |
| JEL: | C72 D85 |
| Date: | 2025–07–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125309 |
| By: | Alexander Kohlhas; Vladimir Asriyan |
| Abstract: | We document a substantial rise in the accuracy of U.S. firms' expectations since the early 2000s, closely linked to firm-size dynamics and consistent with major advances in data-processing technologies. To study the macroeconomic implications, we develop a model of information production, in which information enables firms to optimize their scale, product choice, and pricing strategies. While information enhances the efficiency of resource allocation, it also facilitates price discrimination. The laissez-faire equilibrium is inefficient, warrants corrective policy interventions, and advances in data-processing technologies have ambiguous effects on social welfare. Calibrating our model to U.S. firm-level data, we find that data-processing advances have significantly increased TFP over the past two decades (5.3-6.7%), primarily by helping firms determine their optimal scale. Yet, the welfare benefits of these improvements have been modest (0.1-2.1%). Restricting data use, especially by large firms, could trigger larger welfare gains. |
| Keywords: | price discrimination, misallocation, rent-extraction, information frictions, expectations, optimal policy, data economy, product choice, data regulation |
| JEL: | E10 E60 C53 D83 D84 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1486 |