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UDC 004.056:004.738.5

DOI: 10.15507/0236-2910.027.201701.021-026



Sergey A. Sannikov
Postgraduate Student, Chair of Applied Mathematics, Differential Equations and Theoretical Mechanics, National Research Mordovia State University (68 Bolshevistskaya St., Saransk 430005, Russia), ORCID: http://orcid.org/0000-0002-6135-6954, This email address is being protected from spambots. You need JavaScript enabled to view it.

Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”.
Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network) and variables affecting on the model (permanent disturbance).
Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut.
Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.

Keywords: MBPN model, volatility, mean square estimation, neural network, stock market option

For citation: Sannikov SA. Neural networks for stock market option pricing. Vestnik Mordovskogo universiteta = Mordovia University Bulletin. 2017; 1(27):21-26. DOI: 10.15507/0236-2910.027.201701.021-026

The author have read and approved the final manuscript.


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