Articlegold2023

Option Pricing Using LSTM: A Perspective of Realized Skewness

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Stochastic processes and financial applications Financial Markets and Investment Strategies Stock Market Forecasting Methods

Abstract

Deep learning has drawn great attention in the financial field due to its powerful ability in nonlinear fitting, especially in the studies of asset pricing. In this paper, we proposed a long short-term memory option pricing model with realized skewness by fully considering the asymmetry of asset return in emerging markets. It was applied to price the ETF50 options of China. In order to emphasize the improvement of this model, a comparison with a parametric method, such as Black-Scholes (BS), and machine learning methods, such as support vector machine (SVM), random forests and recurrent neural network (RNN), was conducted. Moreover, we also took the characteristic of heavy tail into consideration and studied the effect of realized kurtosis on pricing to prove the robustness of the skewness. The empirical results indicate that realized skewness significantly improves the pricing performance of LSTM among moneyness states except for in-the-money call options. Specifically, the LSTM model with realized skewness outperforms the classical method and other machine learning methods in all metrics.

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References

1973The Pricing of Options and Corporate Liabilities 1993A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options 1973Theory of Rational Option Pricing 1976Option pricing when underlying stock returns are discontinuous 2017Deep learning with long short-term memory networks for financial market predictions 2000Conditional Skewness in Asset Pricing Tests 2017Stock price prediction using LSTM, RNN and CNN-sliding window model 1995THE GARCH OPTION PRICING MODEL

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2023Option pricing using deep learning approach based on LSTM-GRU neural networks: Case of London stock exchange

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