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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/15952

Title: Combining Monte Carlo Filters with Support Vector Machines for 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: 2013-04-22T07:37:05Z
Publisher: Springer Berlin/Heidelberg
Abstract: This study proposes a hybrid model for online forecasting of option prices. The hybrid predictor combines a Monte Carlo filter with a support vector machine. The Monte Carlo filter (MCF) is used to infer the latent volatility and discount rate of the Black-Scholes model, and makes a subsequent prediction. The support vector machine is employed to capture the nonlinear residuals between the actual option prices and the MCF predictions. Taking the option transaction data on the Taiwan composite stock index, this study examined the forecasting accuracy of the proposed model. The performance of the hybrid model is superior to traditional extended Kalman filter models and pure SVM forecasts. The results can help investors to control and hedge their risks.
Relation: Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science, 4259: 607-616
Appears in Collections:[資訊管理學系所] 期刊論文

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