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on Market Microstructure |
| By: | Christian Keller; Michael C. Tseng |
| Abstract: | We generalize the seminal framework of Kyle (1985) to a many-asset setting, bridging the gap between informed-trading theory and modern trading practices. Specifically, we formulate an infinite-dimensional Bayesian trading game in which the informed trader's private information may concern arbitrary aspects of the cross-sectional payoff structure across a continuum of traded assets. In this general setting, we obtain a parsimonious equilibrium characterized by a single scalar fixed point, yielding closed-form characterizations of equilibrium trading strategy, price impact within and across markets, and the informational efficiency of equilibrium prices. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.21125 |
| By: | Sunghun Ko |
| Abstract: | We introduce a new class of automated market maker (AMM), the \emph{partially active automated market maker} (PA-AMM). PA-AMM divides its reserves into two parts, the active and the passive parts, and uses only the active part for trading. At the top of every block, such a division is done again to keep the active reserves always being \(\lambda\)-portion of total reserves, where \(\lambda \in (0, 1]\) is an activeness parameter. We show that this simple mechanism reduces adverse selection costs, measured by loss-versus-rebalancing (LVR), and thereby improves the wealth of liquidity providers (LPs) relative to plain constant-function market makers (CFMMs). As a trade-off, the asset weights within a PA-AMM pool may deviate from their target weights implied by its invariant curve. Motivated by the optimal index-tracking problem literature, we also propose and solve an optimization problem that balances such deviation and the reduction of LVR. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.09887 |
| By: | Alimoradian, Behzad; Barigou, Karim (Université catholique de Louvain, LIDAM/ISBA, Belgium); Eyraud-Loisel, Anne |
| Abstract: | In this paper, we examine the implications of a large trader’s activity and its market impact when pricing and hedging a large derivatives position. We highlight the issue of market manipulation in this context, providing examples to illustrate how the trader can manipulate the payoff. To understand how a large trader’s knowledge influences their actions, we introduce a problem reduction by considering the perspective of an insider trader, who is aware of the large trader’s trading policy but does not incur transaction costs. Through this framework, we establish the existence of information-neutral probability measures, under which the discounted asset is a martingale process for the insider trader. We then develop a derivatives hedging policy for the large trader that accounts for both transaction costs and the permanent impact of their hedging activities, while mitigating market manipulation. The paper concludes with numerical results that showcase the optimal delta-hedging strategy for an out-of-the-money call option, comparing it to the Black-Scholes model. |
| Keywords: | Option Pricing ; Market Impact ; Illiquid Markets ; Transaction Costs ; Stochastic Optimal Control |
| Date: | 2025–02–14 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2025002 |
| By: | Simon Finster; Paul W. Goldberg; Edwin Lock; Matilde Tullii |
| Abstract: | We explore stability and fairness considerations in decentralized networked markets with bilateral contracts, building on the trading networks framework [Hatfield et al., 2013]. In our trading network game, we show that a well-defined subset of Nash equilibria can be supported as competitive equilibria. Considering an offer-based trading dynamic as well as a stochastic price clock market, we prove new convergence results to Nash equilibrium and competitive equilibrium, providing a rationale for stability properties in decentralized, dynamic trading networks. Turning to the tension between fairness and (core) stability, we prove several negative results: inessential agents always receive zero utility in any core outcome, and even essential agents can get zero utility in all core outcomes. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.20868 |
| By: | Joel Hasbrouck; Julian Ma; Fahad Saleh; Caspar Schwarz-Schilling |
| Abstract: | We demonstrate market inefficiency in cryptoasset markets. Our approach examines investments that share a dominant risk factor but differ in their exposure to a secondary risk. We derive equilibrium restrictions that must hold regardless of how investors price either risk. Our empirical results strongly reject these necessary equilibrium restrictions. The rejection implies market inefficiency that cannot be attributed to mispriced risk, suggesting the presence of frictions that impede capital reallocation. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.20771 |
| By: | Pieter Nel (Department of Economics, University of Pretoria); Renee van Eyden (Department of Economics, University of Pretoria) |
| Abstract: | Does media sentiment create artificial volatility, or do stock markets efficiently filter media sentiment as noise? This study tests these hypotheses using daily data (1994-2024) across the S&P 500, Dow Jones, and NASDAQ. Principal Component Analysis decomposes four uncertainty measures into fundamental uncertainty (PC1) and media-amplified supply sentiment (PC2). EGARCH modeling reveals that media sentiment mutes rather than amplifies volatility contradicting behavioral finance predictions. Time Varying Granger causality tests suggests no causality from uncertainty variables to volatility, but volatility has a causal relationship with fundamental uncertainty. The asymmetric relationship demonstrates that information flows from stock markets to uncertainty sentiment, not uncertainty sentiment to stock markets. These findings support rational updating hypothesis where investors observe volatility and correctly infer elevated uncertainty, rather than being misled by media sentiment. |
| Keywords: | Media sentiment, EGARCH modeling, Principal component analysis, Time-varying causality |
| JEL: | G41 C58 E44 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202605 |