Forward-looking volatility forecasts are central inputs to derivatives pricing, market making, risk management, and volatility-linked trading strategies, with ARCH and GARCH models serving as the canonical workhorses. Such models are natural in standard asset markets, where prices are positive-valued stochastic processes and volatility is typically inferred from return dynamics. Prediction markets have a different structure: prices are bounded probabilities, payoffs are binary, and contracts resolve at known deadlines. We develop and estimate a volatility model tailored to binary prediction markets. The model combines two economic mechanisms: a Wright-Fisher deadline-resolution component, capturing how remaining binary uncertainty is forced to resolve over time, and a Glosten-Milgrom order-flow component, capturing volatility from informed trading as reflected in spreads and volume. Using a large panel of Kalshi contracts, we show that these structural variables carry substantial forecasting power. Plain ARCH/GARCH benchmarks are dominated by structural specifications; combining the structural model with residual GARCH dynamics gives the best overall forecasts. The model also provides an interpretable measurement framework: volatility is highest near fifty-fifty prices, rises near resolution, and varies across categories with the timing and discreteness of information arrival. Economics contracts are closer to smooth deadline-resolution dynamics, while sports contracts exhibit more event-concentrated, jump-like behavior. Across major categories, category-specific fitting does not systematically improve out-of-sample performance, suggesting that the structural specification transfers beyond the pooled headline result.
Weiye Xi, C. Moallemi, Mallesh M. Pai et al.· 0 citations
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) that evaluates split quality conditional on the pools actually used; a Full-Venue Optimum (FVO) that considers all available pools to quantify the value of broader pool access; and a Gas-Aware FVO (G-FVO) that augments FVO with gas costs to capture the trade-off between additional pool usage and gas expenditure. Computing these benchmarks at scale is enabled by a bisection-based algorithm for optimal routing across multiple pools for the same token pair. Two regularities emerge. First, information timeliness is crucial: moving from execution-time state to one-block lagged state optimization significantly raises mean shortfall and additional delays further degrade performance, albeit with diminishing increments; evaluated on the same stale snapshots, realized routes lie closer to optimal, indicating timing-mismatch as a key component. Second, inefficiency is heterogeneous and heavy-tailed: small trades suffer higher percentage losses, while a few extreme outliers dominate the aggregate dollar shortfalls. Finally, we demonstrate that sandwiching attacks drive a significant fraction of routing sub-optimality. Our benchmark protocol and algorithm offer a rigorous, reproducible basis for evaluating and improving information-timely, gas-aware routing.
The framework of loss-versus-rebalancing (LVR) is used to study how the total worst-case loss to the subsidizer is distributed across price states or over time and extended to dynamic liquidity management, showing that liquidity levels can be adjusted over time to implement a prescribed target expected cumulative loss schedule.
C. Moallemi, D. Robinson, Brian Z. Zhu· 0 citations
OS-Pruner is a lightweight plug-in framework that formulates chain-of-thought pruning as an optimal stopping problem that achieves 20-60\% reduction in generation length with minimal accuracy sacrifice on diverse reasoning benchmarks and base models.
Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias et al.· 0 citations