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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/1865

Title: Combining Monte Carlo Filters with Support Vector Machines for Option Price Forecasting
Authors: Shian-Chang Huang;Tung-Kuang Wu
Contributors: 企業管理學系
Date: 2006
Issue Date: 2010-11-15T07:43:24Z
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: Lecture Notes in Computer Science, 4259:607-616, DOI:10.1007/11908029_63
Appears in Collections:[企業管理學系] 期刊論文

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