Abstract
We examine the ARCH-GARCH models for the forecasting of the bond price time series provided by VUB bank and make comparisons the forecast accuracy with the class of RBF neural network models. A limited statistical or computer science theory exists on how to design the architecture of RBF networks for some specific nonlinear time series, which allows for exhaustive study of the underlying dynamics, and determination of their parameters. To illustrate the forecasting performance of these approaches the learning aspects of RBF networks are presented and an application is included. We show a new approach of function estimation for nonlinear time series model by means of a granular neural network based on Gaussian activation function modelled by cloud concept. In a comparative study is shown, that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.
Highlights
Over the past ten years academics of computer science have developed new soft techniques based on latest information technologies such as soft, neural and granular computing to help predict future values of high frequency financial data which are observations on financial variables taken daily or at a finer time scale, and are often irregularly spaced over time
The relevant lag structure of potential inputs was analysed using traditional statistical tools, i.e. using the autocorrelation function (ACF), partial autocorrelation function (PACF) and the Akaike/Bayesian information criterion (AIC/BIC)(see Ref. 8): we looked to determine the maximum lag for which the PACF coefficient was statistically significant and the lag given the minimum AIC
To improve the abstraction ability of soft Radial Basic Function (RBF) neural networks with architecture depicted in Figure 5, we replaced the standard Gaussian activation function of RBF neurons with functions based on the normal cloud concept
Summary
Over the past ten years academics of computer science have developed new soft techniques based on latest information technologies such as soft, neural and granular computing to help predict future values of high frequency financial data which are observations on financial variables taken daily or at a finer time scale, and are often irregularly spaced over time (see Ref.). For example volatility modelling provides a simple approach to calculating value at risk of a financial position in risk management. It plays an important role in asset allocations under the mean-variance framework. This paper discusses and compares the forecasts of volatility models which are derived from competing statistical and Radial Basic Function (RBF) neural network (NN) specifications. Most developed statistical (econometric) models assume a nonlinear relationship among variables, for example the exponential and power GARCH models and exponential autoregressive models. These are model-driven approaches based on a specific type relation among the variables.
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