An Empirical Study on Forecasting the Stock Volatility in the Option Pricing Model by Using the Neural Networks

• Main Authors: Chung, Chenz-Chi, 鍾澄吉
• Other Authors: An-Pin Chen
• Format: Others
• Language: zh-TW
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/75916254603267408126

碩士 === 國立交通大學 === 資訊管理研究所 === 86 === Traditionally, the Black-Scholes model is a useable evaluation method for optionpricing. However, there exist some impractical assumptions in the Black-Scholes Model. Therefore, the evaluated prices would be reconsidered that may generated from the practiced data. Usually, in all input variables considered from B-S model, the volatility is the most difficulty part to understand. Thus, if a practical model would be applied, the volatility should be precisely estimated as the first step to be done.However recent studies of the field in the artificial intelligence reflect that neural networks have the ability of learning and performing high-speed calculations. Also with it''s parallel processing and tolerance of faults, it''s prediction ability has become gradually an accepted level. So this research will attempt to forecast the future volatility of prices by neural networks.This empirical research is to simulate options transactions that randomly selected from 30 companies in Taiwan. In this research, the neural network with genetic algorithm should be applied to forecast the volatility of each stock and the result would be compared with the output from the traditional B-S method. The research periods is starting from 1995 to 1997 three years.The research shows that the output from the forecast module by the neural network is superior to that from the traditional B-S method no matter in interpreter capability or in practical hedge strategy operation. This means that the new method provided in this thesis can assist the investor to make more precisely investment decision and hedge strategy.