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 language | English |
|---|---|
| Article number | 112560 |
| Journal | Journal of Computational and Applied Mathematics |
| Volume | 369 |
| DOIs | |
| Publication status | Accepted/In press - 2019 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Computational Mathematics
- Applied Mathematics
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A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters. / Huang, Shian Chang; Chiou, Chei Chang; Chiang, Jui Te; Wu, Cheng Feng.
In: Journal of Computational and Applied Mathematics, Vol. 369, 112560, 01.05.2020.Research output: Contribution to journal › Article
TY - JOUR
T1 - A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters
AU - Huang, Shian Chang
AU - Chiou, Chei Chang
AU - Chiang, Jui Te
AU - Wu, Cheng Feng
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075365832&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075365832&partnerID=8YFLogxK
U2 - 10.1016/j.cam.2019.112560
DO - 10.1016/j.cam.2019.112560
M3 - Article
AN - SCOPUS:85075365832
VL - 369
JO - Journal of Computational and Applied Mathematics
JF - Journal of Computational and Applied Mathematics
SN - 0377-0427
M1 - 112560
ER -