Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)


A Review of Neural Network Applications in Derivative Pricing, Hedging and Risk Management

Author(s): Sumit Kumar

Neural network algorithms are used to solve the problem of pricing options and to simulate how these financial derivatives don't behave in a straight line. A Neural Network may extract relevant information from large data sets. This study examines the research on the application of Artificial Neural Networks (ANN) to pricing of Financial Derivatives. To understand the application of Artificial Neural Network we analyzed 50 plus relevant journal articles and summarized the objective and primary result of those papers to further imply the usage of ANN into Pricing of Derivatives. We figured out that ANN has widely been used in Pricing and modelling of Financial Derivatives, it has been used to calculate Option Greeks. Option Greeks and other sensitivities are faster to compute using ANN with better accuracy and calibration results. We further put the application of ANN on the timescale and compared it with the evolution of computational power and computer memories to demonstrate that application of ANN is increasing with increasing memory and computation power of modern computers. This also helps predict in future that usage of ANN in pricing complex financial instruments will further go up. Based on this study, it was determined that Artificial Neural Networks are an excellent method for predicting the global stock markets. In order to understand the application of Artificial Neural Network.

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