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

研究成果: Article

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號112560
期刊Journal of Computational and Applied Mathematics
369
DOIs
出版狀態Published - 2020 五月 1

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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