A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters

Shian Chang Huang, Chei Chang Chiou, Jui Te Chiang, Cheng Feng Wu

Research output: Contribution to journalArticle

Abstract

Derivatives such as options are complex financial instruments. The risk in option trading leads to the demand of trading support systems for investors to control and hedge their risk. The nonlinearity and non-stationarity of option dynamics are the main challenge of option price forecasting. To address the problem, this study develops a multi-kernel adaptive filters (MKAF) for online option trading. MKAF is an improved version of the adaptive filter, which employs multiple kernels to enhance the richness of nonlinear feature representation. The MKAF is a fully adaptive online algorithm. The strength of MKAF is that the weights to the kernels are simultaneous optimally determined in filter coefficient updates. We do not need to design the weights separately. Therefore, MKAF is good at tracking nonstationary nonlinear option dynamics. Moreover, to reduce the computation time in updating the filter, and prevent overadaptation, the number of kernels is restricted by using coherence-based sparsification, which constructs a set of dictionary and uses a coherence threshold to restrict the dictionary size. This study compared the new method with traditional ones, we found the performance improvement is significant and robust. Especially, the cumulated trading profits are substantially increased.

Original languageEnglish
Article number112560
JournalJournal of Computational and Applied Mathematics
Volume369
DOIs
Publication statusPublished - 2020 May 1

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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