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This topic is of high relevance due to the fact that many currently available mathematical market risk assessment models contain many limitations for their effective use. However, these limitations are often not feasible, what leads to a decrease in forecast accuracy. To avoid this, more accurate models are necessary. Neural network-based models can show a more precise result due to their basic property – nonlinearity.
The interest in neural networks re-emerged only after some important theoretical results were attained in the early eighties and new hardware developments increased the processing capacities.
Artificial neural networks can be most adequately characterised as «computational models» with particular properties such as the ability to adapt or learn, to generalise, or to cluster or organise data, and which operation is based on parallel processing.
The task of this paper is to build a model that can enable us to assess a market risk for a company.
The primary goal of this paper is to determine a lower bound of the yield to be forecast by the neural network model with a certain level of significance. Current actual yields will be fed to the neural network output, and some factors will be fed to the neural network input.