CECPApr 6

Toward Black Scholes for Prediction Markets: A Unified Kernel and Market Maker's Handbook

arXiv:2510.1520510.61 citations
Predicted impact top 77% in CE · last 90 daysOriginality Highly original
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This provides a standardized framework for market makers in prediction markets to quote and hedge belief risks, addressing volatility and jump risks as these markets scale with institutional participation.

The paper tackles the lack of a unifying stochastic kernel for prediction markets, proposing a logit jump-diffusion model that treats traded probabilities as risk-neutral martingales and provides quotable risk factors like belief volatility and jump intensity; in experiments, it reduces short-horizon belief-variance forecast error compared to baselines.

Prediction markets, such as Polymarket, aggregate dispersed information into tradable probabilities, but they still lack a unifying stochastic kernel comparable to the one options gained from Black-Scholes. As these markets scale with institutional participation, exchange integrations, and higher volumes around elections and macro prints, market makers face belief volatility, jump, and cross-event risks without standardized tools for quoting or hedging. We propose such a foundation: a logit jump-diffusion with risk-neutral drift that treats the traded probability p_t as a Q-martingale and exposes belief volatility, jump intensity, and dependence as quotable risk factors. On top, we build a calibration pipeline that filters microstructure noise, separates diffusion from jumps using expectation-maximization, enforces the risk-neutral drift, and yields a stable belief-volatility surface. We then define a coherent derivative layer (variance, correlation, corridor, and first-passage instruments) analogous to volatility and correlation products in option markets. In controlled experiments on synthetic risk-neutral paths and real event data, the model reduces short-horizon belief-variance forecast error relative to diffusion-only and probability-space baselines, supporting both causal calibration and economic interpretability. Conceptually, the logit jump-diffusion kernel supplies an implied-volatility analogue for prediction markets: a tractable, tradable language for quoting, hedging, and transferring belief risk across venues such as Polymarket.

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