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二元预测市场的结构性波动率预测

文章 arXiv papers · 作者: Weiye Xi et al.
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总结

本文为二元预测市场开发波动率预测框架。此类市场中的价格代表有界概率，合约在已知期限到达时结算。框架结合 Wright–Fisher 组件，模拟临近期限时不确定性如何被迫消解；并结合 Glosten–Milgrom 组件，将知情订单流、价差和成交量与波动率联系起来。研究还将这些结构性变量与 ARCH 和 GARCH 基准进行比较，并考虑加入残差 GARCH 动态。

对大量 Kalshi 合约的测试发现，结构性变量较普通 ARCH/GARCH 模型改善了预测表现；将其与残差 GARCH 动态结合后，整体预测表现最佳。该框架认为，接近五五开的价格和临近结算的合约具有更高波动率。研究还报告称，波动较平缓的经济类合约与事件更集中的体育类合约有所不同。按类别拟合并未持续改善样本外结果，因此这些发现支持在所研究类别之间迁移，但不能证明其在现有合约范围之外的表现。

核心观点

  • 二元合约的波动率既受期限驱动的结算过程影响，也受知情交易影响。
  • 模型将概率水平和距结算的时间作为结构性预测变量。
  • 在报告所述样本中，结构性模型优于普通 ARCH/GARCH 基准。
  • 加入残差 GARCH 动态后，研究中的整体预测表现最佳。
  • 不同合约类别的波动率模式有所不同，但按类别拟合并未持续改善样本外表现。

标签

波动率衍生品定价市场微观结构统计学风险管理
全文
# Volatility in Prediction Markets: A Structural Approach


# Volatility in Prediction Markets: A Structural Approach









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。