Combining Monte Carlo filters with support vector machines for option price forecasting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationRough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings
PublisherSpringer Verlag
Pages607-616
Number of pages10
ISBN (Print)3540476938, 9783540476931
DOIs
Publication statusPublished - 2006 Jan 1
Event5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006 - Kobe, Japan
Duration: 2006 Nov 62006 Nov 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4259 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006
CountryJapan
CityKobe
Period06-11-0606-11-08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Huang, S. C., & Wu, T. K. (2006). Combining Monte Carlo filters with support vector machines for option price forecasting. In Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings (pp. 607-616). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4259 LNAI). Springer Verlag. https://doi.org/10.1007/11908029_63