Problem
Research questions and friction points this paper is trying to address.
Pairs Trading
Political Betting Markets
Mean Reversion
Ornstein-Uhlenbeck Process
U.S. Presidential Election
Adapting Pairs Trading to Gambling Markets A Case Study of the U.S. Presidential Election
🤖 AI Summary
研究通过将隐含概率建模为随时间变化的Ornstein-Uhlenbeck过程，适应政治赌市中的配对交易策略，以2020和2024年美国总统选举数据验证了模型的有效性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pairs Trading
Ornstein-Uhlenbeck Process
Political Betting Markets
Bradley-Terry Model
🔎 Similar Papers
No similar papers found.
