A new approximate Bayesian inferential framework is proposed that exploits multiple information sources -- daily spot returns, high-frequency spot data and option prices -- and enables fast calculation of probabilistic predictions of future option prices. This approach operates directly from the theoretical option pricing model, and does not require an explicit statistical model, or likelihood, for the observed option prices. We demonstrate that our approach produces accurate probabilistic option-price predictions in realistic scenarios and, despite not explicitly modelling option-pricing errors via a statistical model, the method is shown to be robust to the presence of such errors. Predictive accuracy based on the Heston option pricing model is illustrated empirically for short-maturity options, with the rapidity of real-time updates of the predictive distributions highlighted.

翻译：提出了一种新的近似贝叶斯推断框架，该框架利用多个信息源——日度现货收益率、高频现货数据及期权价格——并能够快速计算未来期权价格的概率预测。该方法直接基于理论期权定价模型运行，无需为观测到的期权价格构建显式统计模型或似然函数。我们证明，该方法能在现实场景中生成准确的期权价格概率预测，且尽管未通过统计模型显式建模期权定价误差，该方法仍对这类误差的存在具有鲁棒性。基于Heston期权定价模型的预测准确性通过短期期权实证验证，同时突显了预测分布实时更新的快速性。