Merged LSTM-MLP for option valuation

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.