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on Market Microstructure |
| By: | Steven Campbell; Philippe Bergault; Jason Milionis; Marcel Nutz |
| Abstract: | Passive liquidity providers (LPs) in automated market makers (AMMs) face losses due to adverse selection (LVR), which static trading fees often fail to offset in practice. We study the key determinants of LP profitability in a dynamic reduced-form model where an AMM operates in parallel with a centralized exchange (CEX), traders route their orders optimally to the venue offering the better price, and arbitrageurs exploit price discrepancies. Using large-scale simulations and real market data, we analyze how LP profits vary with market conditions such as volatility and trading volume, and characterize the optimal AMM fee as a function of these conditions. We highlight the mechanisms driving these relationships through extensive comparative statics, and confirm the model's relevance through market data calibration. A key trade-off emerges: fees must be low enough to attract volume, yet high enough to earn sufficient revenues and mitigate arbitrage losses. We find that under normal market conditions, the optimal AMM fee is competitive with the trading cost on the CEX and remarkably stable, whereas in periods of very high volatility, a high fee protects passive LPs from severe losses. These findings suggest that a threshold-type dynamic fee schedule is both robust enough to market conditions and improves LP outcomes. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.08152 |
| By: | Lucas Dyskant; Andre C. Siva; Bruno Sultanum |
| Abstract: | We propose a model of over-the-counter markets based on three trading methods: principal inventory, agency risk-free, and all-to-all (A2A) trading. Principal and agency trading occur through dealers. A2A trading occurs directly through customer-customer trading. The model predicts that A2A size can remain stable while principal and agency trading change. Higher inventory costs shifts trading from principal to agency and decrease dealers’ net positions. Bid-ask spreads can decrease even though transaction costs increase. High transaction costs can lead to multiple equilibria. The model shows how regulatory and technological changes affect trading choices, stability, and market indicators. |
| Keywords: | Over-the-counter markets, Intermediation costs, Liquidity, Corporate bond markets, Financial market regulations, Post-2008 regulations, Volcker rule |
| JEL: | D53 G12 G18 G28 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:unl:unlfep:wp675 |
| By: | Ivan Letteri |
| Abstract: | The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of thirteen diverse models to identify which approaches are most suitable for detecting potentially manipulative trading behaviours. An empirical evaluation, conducted via backtesting on a dataset of 26, 204 records from a major exchange, demonstrates that the top-performing model, Empirical Covariance (EC), achieves a 6.70% gain, significantly outperforming a standard Buy-and-Hold benchmark. These findings underscore the effectiveness of outlier-driven strategies and provide insights into the trade-offs between model complexity, trade frequency, and performance. This study contributes to the growing corpus of research on cryptocurrency market microstructure by furnishing a rigorous benchmark of anomaly detection models and highlighting their potential for augmenting algorithmic trading and risk management. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14960 |
| By: | Jianhao Su; Yanliang Zhang |
| Abstract: | The information investors acquire in asset markets has various forms. We refer to range information as information about the upper and lower bound which the payoff of an asset may reach in the future. This paper explores the market impacts of investors' asymmetrical acquisition of range information. Uninformed traders are inherently unable to directly obtain the private signal held by informed traders. This study shows that when range information is released to investors asymmetrically, uninformed traders who can only obtain rougher range information will not trade assets under the max-min ambiguity aversion criterion. Investors' asymmetrical acquisition of range information can cause that market liquidity and the sensitivity of market price to private signal vary continuously with the signal and noise trading volume. We also reveal that investors' asymmetrical acquisition of range information can increase market liquidity and the sensitivity of price under some conditions and decrease them under some other conditions. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09615 |
| By: | Zhuohan Wang; Carmine Ventre |
| Abstract: | Modeling limit order books (LOBs) dynamics is a fundamental problem in market microstructure research. In particular, generating high-dimensional volume snapshots with strong temporal and liquidity-dependent patterns remains a challenging task, despite recent work exploring the application of Generative Adversarial Networks to LOBs. In this work, we propose a conditional \textbf{Diff}usion model for the generation of future LOB \textbf{Volume} snapshots (\textbf{DiffVolume}). We evaluate our model across three axes: (1) \textit{Realism}, where we show that DiffVolume, conditioned on past volume history and time of day, better reproduces statistical properties such as marginal distribution, spatial correlation, and autocorrelation decay; (2) \textit{Counterfactual generation}, allowing for controllable generation under hypothetical liquidity scenarios by additionally conditioning on a target future liquidity profile; and (3) \textit{Downstream prediction}, where we show that the synthetic counterfactual data from our model improves the performance of future liquidity forecasting models. Together, these results suggest that DiffVolume provides a powerful and flexible framework for realistic and controllable LOB volume generation. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.08698 |
| By: | Ying Peng; Yifan Zhang; Xin Wang |
| Abstract: | In this paper, we mainly focus on the prediction of short-term average return directions in China's high-frequency futures market. As minor fluctuations with limited amplitude and short duration are typically regarded as random noise, only price movements of sufficient magnitude qualify as statistically significant signals. Therefore data imbalance emerges as a key problem during predictive modeling. From the view of data distribution imbalance, we employee the mean-uncertainty logistic regression (mean-uncertainty LR) classification method under the sublinear expectation (SLE) framework, and further propose the mean-uncertainty support vector machines (mean-uncertainty SVM) method for the prediction. Corresponding investment strategies are developed based on the prediction results. For data selection, we utilize trading data and limit order book data of the top 15 liquid products among the most active contracts in China's future market. Empirical results demonstrate that comparing with conventional LR-related and SVM-related imbalanced data classification methods, the two mean-uncertainty approaches yields significant advantages in both classification metrics and average returns per trade. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.06914 |
| By: | Hardhik Mohanty; Giovanni Zaarour; Bhaskar Krishnamachari |
| Abstract: | Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic proactive market maker. We examine the behavior of funding fees and transaction costs across varying liquidity conditions. Using simulations and modeling, we demonstrate that liquidity providers can aim to achieve a net positive PnL by employing effective hedging strategies, even in challenging environments characterized by low liquidity and high transaction costs. Additionally, we provide insights into the incentives that drive liquidity providers to support the growth of everlasting option markets and highlight the significant benefits these instruments offer to traders as a reliable and efficient financial tool. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.07068 |
| By: | Makoto Takahashi |
| Abstract: | The study examines the interaction between returns and order flow imbalances (differences between buy and sell orders), constructed from the best bid and offer files of S&P 500 E-mini futures contract, using a structural vector autoregressive model. The intraday variation in market activity is considered by applying the model for each short interval each day, whereas the endogeneity due to time aggregation is handled by estimating the structural parameters via the identification through heteroskedasticity. The estimation results show that significant endogeneity exists and that the estimated parameters and impulse responses exhibit significant intraday variations, reflecting intense or mild order submission activities. Further, the estimated parameters change around macroeconomic news announcements, suggesting inactive order submission periods exist when they occur. Overall, such announcement effects are mostly explained by the order submission activities reflecting the public information. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.06788 |
| By: | Can Gao (University of St.Gallen; Swiss Finance Institute; Swisss Institute for Banking and Finance); Brandon Yueyang Han (Robert H. Smith School of Business, University of Maryland) |
| Abstract: | We demonstrate the asset pricing implications of investors’ belief heterogeneity in the frequency of news arrival and its joint impact with heterogeneous beliefs about news content. Investors trade volatility derivatives against each other to speculate on the rate of news arrival: greater disagreement of this kind gives rise to more extreme derivative positions. When disagreement about news arrival frequency is low, volatility exhibits mean reversion because extreme optimists and pessimists incur substantial wealth losses amid intense market swings. In contrast, high disagreement about the news arrival rate leads to volatility persistence. When news is absent in such environments, volatility sellers dominate, and extreme payoffs are underweighted in the formation of market expectations, resulting in lower implied volatility. In this context, “no news” effectively becomes good news for risky asset valuations. |
| JEL: | G11 G12 D83 D84 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2561 |