The Performance of Hybrid Artificial Neural Network Models for Option Pricing during Financial Crises

David Liu ； Siyuan Huang

《Journal of Data Science》 14卷1期 (2016/01) Pp. 1-17

https://doi.org/10.6339/JDS.201601_14(1).0001

並列摘要

this paper provides a novel research on the pricing ability of the hybrid ANNs based upon the Hang Seng Index Options spanning a period of from Nov, 2005 to Oct, 2011, during which time the 2007-20008 financial crisis had developed. We study the performances of two hybrid networks integrated with Black-Scholes model and Corrado and Su model respectively. We find that hybrid neural networks trained by using the financial data retained from a booming period of a market cannot have good predicting performance for options for the period that undergoes a financial crisis (tumbling period in the market), therefore, it should be cautious for researchers/practitioners when carry out the predictions of option prices by using hybrid ANNs. Our findings have likely answered the recent puzzles about NN models regarding to their counterintuitive performance for option pricing during financial crises, and suggest that the incompetence of NN models for option pricing is likely due to the fact NN models may have been trained by using data from improper periods of market cycles (regimes), and is not necessarily due to the learning ability and the flexibility of NN models.

並列關鍵字

option pricing ； hybrid artificial neural network ； financial crisis ； options