Latent Dynamics of Prediction-Market Price Paths

Prediction markets price real-world events as continuously traded probabilities, yet the high-frequency process by which those prices form has not been studied at order-book resolution, because no public dataset of a prediction venue's live order book has existed. This thesis introduces such a dataset — a purpose-built, gap-audited, millisecond-resolution corpus of the 250 most active Polymarket markets, captured continuously and through each market's resolution — and uses it to characterise and model the latent dynamics of prediction-market price paths.

Two pre-registered probes establish the governing structure. A natural model — a regime-switching model of price variance — is decisively falsified: apparent volatility regimes do not persist at any timescale. Instead, the price is sticky and reprices in clustered bursts, and the persistent structure lives in the intensity of repricing — how often the market moves — which is strongly clustered and non-Poisson on every market tested.

The thesis's contribution is a generative model of this repricing intensity as a latent hot/cold state process (a Markov-modulated Poisson process), extended so that the intensity is modulated by time-to-resolution — the defining feature of prediction markets, in which every price is absorbed at $0 or $1 by a bounded, approximately known deadline. The model is inferred by variational methods and validated on held-out markets and against realised outcomes via the complete path-to-resolution samples in the corpus.