Statistical and RBF NN models: Providing forecasts and risk assessment

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JEL classification

• Econometric and Statistical Methods and Methodology: General
• Forecasting Models; Simulation Methods
• Criteria for Decision-Making under Risk and Uncertainty

Keywords

ARCH-GARCH models, forecasting accuracy, granular computing, soft neural networks, time series

Abstract

Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in decision making. In this paper, we develop forecasting models based on statistical (stochastic) methods, sometimes called as hard computing, and on a soft method using granular computing. We consider the accuracy of forecasting models as a measure for the risk evaluation. It is found that the risk estimation process based on soft methods is simplified and less critical to the question whether the data is true crisp or white noise.