A new modular approximate Bayesian inferential framework is proposed that enables fast calculation of probabilistic predictions of future option prices. We exploit multiple information sources, including daily spot returns, high-frequency spot data and option prices. A benefit of this modular Bayesian approach is that it allows us to work with the theoretical option pricing model, without needing to specify an arbitrary statistical model that links the theoretical prices to their observed counterparts. We show that our approach produces accurate probabilistic predictions of option prices in realistic scenarios and, despite not explicitly modelling pricing errors, the method is shown to be robust to their presence. Predictive accuracy based on the Heston stochastic volatility model, with predictions produced via rapid real-time updates, is illustrated empirically for short-maturity options.

翻译：本文提出了一种新的模块化近似贝叶斯推理框架，能够快速计算未来期权价格的概率预测。我们利用了多种信息源，包括日度现货收益率、高频现货数据以及期权价格。这种模块化贝叶斯方法的优势在于，它允许我们在理论期权定价模型的基础上直接工作，而无需额外设定连接理论价格与观测价格的任意统计模型。研究表明，该方法在实际场景中能够生成准确的期权价格概率预测，且尽管未显式建模定价误差，该方法被证明对其存在具有鲁棒性。基于Heston随机波动率模型，通过快速实时更新生成预测的短期期权预测精度得到了实证展示。