A market resilient data-driven approach to option pricing
基于无套利理论提出数据驱动的集成方法预测期权价格，通过领域自适应和特征缩放提升异常样本预测精度，实证显示均方根误差低于基准模型的三分之一。
In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. Through a specific scaling, suitable for financial time series data, we obtain a feature representation that is indistinguishable for samples coming from different domains. This provides an advantage over conventional models when predicting atypical out-of-sample test data. The success of an implementation of this idea is shown using some real market data. The root mean squared error in prediction turns out to be less than one-third of that for the benchmark model. We further report several experimental results for critically examining the predictive performance of the derived pricing models.