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|Title:||Critical assessment of option pricing methods using Artificial Neural Networks||Authors:||Andreou, Panayiotis
Martzoukos, Spiros H.
|Keywords:||Economics;Neural networks;Electronic trading;Black-Scholes formula;Trading strategies;Federal Reserve;Risk assessment;Financial markets||Category:||Economics and Business||Field:||Social Sciences||Issue Date:||2002||Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 2415 LNCS, 2002, Pages 1131-1136||Journal:||Lecture Notes in Computer Science||Conference:||International Conference on Artificial Neural Networks||Abstract:||In this paper we compare the predictive ability of the Black-Scholes Formula (BSF) and Artificial Neural Networks (ANNs) to price call options by exploiting historical volatility measures. We use daily data for the S&P 500 European call options and the underlying asset and furthermore, we employ nonlinearly interpolated risk-free interest rate from the Federal Reserve board for the period 1998 to 2000.Using the best models in each sub-period tested, our preliminary results demonstrate that by using historical measures of volatility, ANNs outperform the BSF.In addition, the ANNs performance improves even more when a hybrid ANN model is utilized. Our results are significant and differ from previous literature. Finally, we are currently extending the research in order to: a) incorporate appropriate implied volatility per contract with the BSF and ANNs and b) investigate the applicability of the models using trading strategies.||URI:||https://ktisis.cut.ac.cy/handle/10488/14562||ISBN:||978-354044074-1||ISSN:||03029743||Rights:||© Springer-Verlag||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια /Conference papers - poster -presentation|
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