将配对交易适应于博彩市场：美国大选案例研究
Adapting Pairs Trading to Gambling Markets A Case Study of the U.S. Presidential Election
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中文总结 AI 辅助
本研究将配对交易思想应用于政治博彩市场，通过潜在Ornstein-Uhlenbeck过程建模候选人联合隐含概率，结合参数自助法预测界和Bradley-Terry型模型，在2024年美国大选数据上验证了该框架的有效性。
中文摘要 AI 辅助
配对交易利用相关资产之间关系的均值回归特性。我们将这一思想应用于政治博彩市场，通过一个潜在的Ornstein-Uhlenbeck过程来建模两位主要政党候选人的联合隐含概率，该过程的均值回归水平随时间变化，且观测值包含加性噪声。模型参数通过状态空间似然从定期采样的赔率数据中估计，连续的重复值由单个保留观测表示，而经过的采样间隔数在连续时间转移中得以保留。参数自助法上预测界识别联合隐含概率可能下降的信号时间，一个无截距的Bradley-Terry型模型选择特定候选人的赔率报价。候选选择模型在2020年美国总统选举数据上训练，并在2024年数据上进行样本外评估。2024年分析产生了130个信号，经验一步覆盖率为95.1%，平均合成赔率价格收益为1.86%，未年化的每笔交易Sharpe型比率为1.12。这些收益是无摩擦的描述性数量，而非可执行的博彩交易所利润。结果支持该集成框架作为双候选人选举市场概念验证的有效性。
英文摘要
Pairs trading exploits mean reversion in the relationship between related assets. We adapt this idea to political betting markets by modelling the combined implied probability of the two major-party nominees with a latent Ornstein-Uhlenbeck process whose mean-reversion level varies over time and whose observations contain additive noise. Model parameters are estimated from regularly sampled odds data using a state-space likelihood, with consecutive repeated values represented by a single retained observation and the elapsed number of sampling intervals preserved in the continuous-time transition. Parametric-bootstrap upper prediction bounds identify signal times at which the combined implied probability is likely to decline, and a no-intercept Bradley-Terry-type model selects the candidate-specific odds quote. The candidate-selection model is trained on 2020 U.S. presidential-election data and evaluated out of sample on 2024 data. The 2024 analysis produced 130 signals, empirical one-step coverage of 95.1%, a mean synthetic odds-price return of 1.86%, and an unannualized per-trade Sharpe-type ratio of 1.12. These returns are frictionless descriptive quantities rather than executable betting-exchange profits. The results support the integrated framework as a proof of concept for two-candidate electoral markets.
发表机构
- University of St Andrews（圣安德鲁斯大学）
机构由 AI 辅助整理，请以论文原文为准。
