Merged LSTM-MLP for option valuation
提出一种融合LSTM和MLP的深度学习模型，无需显式估计波动率即可对期权定价，基于2015-2022年标普500看涨期权数据验证了其统计精度和经济收益优于传统模型。
Traditional option pricing models rely on estimates of expected volatility. The true volatility is not directly observable and must hence be estimated, inevitably with error. Any measurement errors immediately translate into inaccurate pricing, leading to potential losses for economic agents trading options for hedging or speculative purposes. This paper proposes a novel merged LSTM-MLP model for option pricing that circumvents the need for an explicit volatility estimate, leading to more accurate valuations. Through extensive out-of-sample testing on S&P500 call options data from 2015 to 2022 we document the statistical accuracy and economic benefits of the model when compared to relevant benchmarks. The superior performance is enabled by the combined LSTM-MLP architecture, which simultaneously utilizes both time series data and the cross-section of observed option characteristics in a deep learning neural network that accurately captures the complex price dynamics. The results are consistent over time and robust across option moneyness and time-to-expiry.