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on Market Microstructure |
| By: | Daojing Zhai |
| Abstract: | Informed traders are supposed to need anonymity: they profit by hiding among the uninformed. A decentralized exchange now publishes the counterparty. Every committed order, cancellation, rejection, and fill carries a persistent pseudonymous wallet address. We reconstruct the full-depth limit order book from a record of 17.1 billion messages and 14.3 million aggressive orders by 147, 113 wallets, covering $84.3 billion in taker notional. We report three findings. First, informativeness is a persistent wallet attribute. Wallets ranked by the price movement following their aggressive orders retain that ordering across adjacent ten-day windows, with a rank correlation of 0.52. Second, the ranking predicts returns. Adding the live activity of the highest-ranked wallets to a standard anonymous benchmark of prices, quotes, and order flow raises the out-of-sample R2 for one-second returns to 12.31%, a 13.2% gain (t = 9.2) that is 1.6 times the largest of 200 activity-matched placebo cohorts. Third, measured at realized trades rather than at every sampled moment, the increment grows from 1.43 to 2.47 percentage points of R2. Public wallet histories therefore carry short-horizon price information that anonymous order-book data leave unmeasured. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04373 |
| By: | Vincent Yinjun-Wang; Madeleine Udell |
| Abstract: | Transaction costs can make or break a trading strategy, particularly in relative-value trading of commodity and macro markets, where edges are a few basis points. Price impact is a central component of transaction cost. Price impact models usually include self-impact (a trade in a contract moves that contract's price) but omit two well-documented effects: cross-impact (a trade in one contract also moves the prices of related contracts) and transient impact (price impact decays over time, so an unwind recovers part of the entry cost). A model without these effects overprices the impact of relative-value trades, whose correlated legs are built and unwound over days, and so forgoes potentially profitable trades. This paper models both effects with a convex quadratic cost. In each period, a positive semidefinite matrix built from volatility, volume, and correlation forecasts couples trades across contracts. A power-law decay kernel then couples trades across periods. The resulting cost admits no price manipulation even when liquidity varies over the planning horizon. The model is demonstrated empirically on calendar spread trading of crude oil futures around the commodity index roll. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.04712 |
| By: | Dobrislav Dobrev; Ernst Schaumburg |
| Abstract: | This paper develops a model-free measurement and inference framework for high-frequency cross-market trading activity and provides empirical evidence of its importance in financial markets. We represent trading activity in two markets as temporal point processes and count the number of time bins in which both processes register activity at a given lag. Under the null of cross-process independence, without global stationarity, a Chen–Stein approximation yields Poisson convergence in total variation for this simple bin-based measure of cross-activity under mild regularity conditions. A local-stationarity framework for marginal intensities, allowing finitely many jumps, then yields a feasible blockwise estimator of the null Poisson mean. The resulting feasible asymptotic theory leads to three easy-to-implement statistical tests for independence at a given lag. It further yields consistent score-driven identification of dependence lags caused by lagged common components in market activity, characteristic of linked order executions across markets. This novel identification framework exploits that common components induce a singular line mass and Poisson score divergence at latency-determined dependence lags. Monte Carlo experiments with nonstationary superpositions of Hawkes processes confirm satisfactory test size, power, and lag-identification performance in finite samples. Empirical validation using transaction data for U.S. Treasury and equity cash-futures markets from 2010 to 2024 reveals sharply localized cross-activity dependencies at lags matching microwave latency, while not rejecting independence at more distant lags. High-frequency cross-market trading generally intensifies during market stress episodes and exhibits pronounced intraday surges after FOMC announcements, confirming its role in information propagation and efficient price discovery across linked markets. |
| Keywords: | High-frequency trading; cross-market activity; temporal point processes; nonstationarity; Chen–Stein method; Poisson approximation; lead–lag identification; market microstructure. |
| JEL: | C12 C14 C32 C41 C46 C58 G12 G14 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2026-011 |
| By: | Muqiao Huang; Ruodu Wang; Yiyun Wang |
| Abstract: | We study equilibria in a closed, fee-free constant-function market maker (CFMM) economy with two assets and two traders. An interior state is a unilateral no-trade equilibrium exactly when the CFMM marginal price equals both traders' marginal rates of substitution. For an interior initial state, individually rational unilateral equilibria are Pareto optimal relative to the fixed CFMM invariant. A weak representative agent is obtained at each fixed equilibrium by weighted sup-convolution, whereas a state-independent strong representative agent exists exactly when traders share a common homothetic preference. Every interior feasible state is reachable through finitely many valid trades, and alternating utility-maximizing trades converge to a Pareto optimal unilateral equilibrium. We also derive conditions under which trading order produces a first-mover advantage or disadvantage in the first round. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.23915 |
| By: | Demetrio Lacava; Paolo Santucci de Magistris |
| Abstract: | Market efficiency relies fundamentally on stable liquidity. Consequently, forecasting liquidity dynamics is a priority for both investors and regulators. We introduce a new tail-risk metric, Illiquidity-at-Risk (IlliQaR), designed to quantify the magnitude of extreme liquidity dry-ups. Relying upon the realized Amihud (a precise illiquidity measurement derived from high-frequency data as the ratio of realized volatility to trading volume) we assess the predictive power of various linear and non-linear econometric models, with a specific focus on the impact of discontinuous jump components. Accounting for these jumps is essential for achieving accurate probability coverage and better IlliQaR predictions during periods of systemic stress, where standard continuous models systematically underestimate the severity of liquidity evaporation. Our empirical analysis, encompassing the S&P 500 index and a cross-section of 25 large U.S. equities, demonstrates that incorporating jumps significantly improves forecasts of illiquidity. Our results suggest that individual stock IlliQaR violations often cluster during periods of S&P 500 liquidity stress. This indicates that Illiquidity at Risk is not just a localized concern but a systemic one, where the main index acts as a leading indicator for extreme dry-ups in individual stock liquidity. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.00943 |