AI 中文总结
研究针对预测市场结构开发并估计波动率模型，结合Wright-Fisher期限解决与Glosten-Milgrom订单流机制，利用Kalshi合约数据验证其预测力强，优于普通ARCH/GARCH基准，且能提供可解释的测量框架。
AI 中文摘要
前瞻性波动率预测是衍生品定价、做市、风险管理和与波动率相关的交易策略的核心输入，ARCH和GARCH模型是常用工具。预测市场结构不同，价格是有界概率，收益是二元的，合约在已知期限结算。我们开发并估计了一个适用于二元预测市场的波动率模型，它结合了Wright-Fisher期限解决和Glosten-Milgrom订单流两个经济机制。通过大量Kalshi合约数据表明，这些结构变量具有强大预测能力，结构化模型优于普通ARCH/GARCH基准，还提供了可解释的测量框架。
英文摘要
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.
