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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/15982

Title: A Hybrid Unscented Kalman Filter and Support Vector Machine Model in Option Price Forecasting
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Authors: Huang, Shian-Chang;Wu, Tung-Kuang
Contributors: 資訊管理學系
Date: 2006
Issue Date: 2013-04-22T07:37:32Z
Publisher: Springer Berlin/Heidelberg
Abstract: This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black- Scholes formula, while the SVM is employed to capture the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can also help investors for reducing their risk in online trading.
Relation: Advances in Natural Computation, Lecture Notes in Computer Science, 4221: 303-312
Appears in Collections:[資訊管理學系所] 期刊論文

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