An Impulsive Delay Discrete Stochastic Neural Network Fractional-Order Model and Applications in Finance
In this paper, we propose a new tool for modeling and analysis in finance, introducing an impulsive discrete stochastic neural network (NN) fractional-order model. The main advantages of the proposed approach are: (i) Using NNs which can be trained without the restriction of a model to derive parameters and discover relationships, driven and shaped solely by the nature of the data; (ii) using fractional-order differences, whose nonlocal property makes the fractional calculus a suitable tool for modeling actual financial systems; (iii) using impulsive perturbations, which give an opportunity to control the dynamic behavior of the model; (iv) including a stochastic term, which allows to study the effect of noise disturbances generally existing in financial assets; (v) taking into account the existence of time delayed influences. The modeling approach proposed in this paper can be applied to investigate macroeconomic systems.
M. Bohner and I. M. Stamova, "An Impulsive Delay Discrete Stochastic Neural Network Fractional-Order Model and Applications in Finance," Filomat, vol. 32, no. 18, pp. 6339-6352, University of Nis, Jan 2018.
The definitive version is available at https://doi.org/10.2298/FIL1818339B
Mathematics and Statistics
Keywords and Phrases
Applications in finance; Delay; Discrete stochastic neural network; Fractional-order system; Impulsive control; Stability
International Standard Serial Number (ISSN)
Article - Journal
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01 Jan 2018