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 language | English |
|---|---|
| Title of host publication | Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings |
| Publisher | Springer Verlag |
| Pages | 607-616 |
| Number of pages | 10 |
| ISBN (Print) | 3540476938, 9783540476931 |
| Publication status | Published - 2006 Jan 1 |
| Event | 5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006 - Kobe, Japan Duration: 2006 Nov 6 → 2006 Nov 8 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4259 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006 |
|---|---|
| Country | Japan |
| City | Kobe |
| Period | 06-11-06 → 06-11-08 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Combining Monte Carlo filters with support vector machines for option price forecasting. / Huang, Shian-Chang; Wu, Tung-Kuang.
Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings. Springer Verlag, 2006. p. 607-616 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4259 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Combining Monte Carlo filters with support vector machines for option price forecasting
AU - Huang, Shian-Chang
AU - Wu, Tung-Kuang
PY - 2006/1/1
Y1 - 2006/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33845244372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845244372&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33845244372
SN - 3540476938
SN - 9783540476931
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 607
EP - 616
BT - Rough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings
PB - Springer Verlag
ER -