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Document Type:Latin Dissertation
Language of Document:English
Record Number:53492
Doc. No:TL23446
Call number:‭1456779‬
Main Entry:Domnic P. Ojala
Title & Author:Options pricing: A comparative analysis of Black-Scholes model, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS)Domnic P. Ojala
College:State University of New York at Binghamton
Date:2008
Degree:M.S.
student score:2008
Page No:183
Abstract:Black-Scholes are inaccurate due to its unrealizable restrictive assumptions. This research tries to apply nonparametric approaches particularly; Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) in an attempt to estimate options prices. Nonparametric approaches have the ability to minimize or eliminate the restrictive assumptions inherent in parametric approaches. The objective of this research is to apply ANNs, ANFIS, and Black-Scholes to estimate prices of S&P 500 index options. To conducted error analysis on results of ANNs, ANFIS, and Black-Scholes using options prices of S&P 500 index options data as base model. To recommend an ideal model for estimating in-the-money (ITM), near-the-money (NTM), and out-of-the-money (OTM) call options. Research data was from historicaloptiondata.com. Data was analyzed; models were designed using ANNs, ANFIS, and Black-Scholes. Error analyses were conducted; results showed that ANNs outperformed ANFIS, Black-Scholes for ITM. Black-Scholes outperformed ANNs, ANFIS for NTM. Finally, ANFIS outperformed ANNs, Black-Scholes for OTM.
Subject:Applied sciences; Artifical neural networks; Adaptive neurofuzzy inference; Black-Scholes model; Options pricing; Systems design; Artificial intelligence; 0790:Systems design; 0800:Artificial intelligence
Added Entry:N. Nagarur
Added Entry:State University of New York at Binghamton