Neural Network Models Theory And Methodology

Neural networks are "data-driven self-adaptive methods in that there are few a priori assumptions about the models under study" (Zhang et al., 1998: 35). As a result, they are well suited to problems where economic theory is of little use. In addition, neural networks are universal approximators capable of approximating any continuous function (Hornik et al, 1989).

Many researchers are confronted with problems where important nonlinearities exist between the independent variables and the dependent variable. Often, in such circumstances, traditional forecasting methods lack explanatory power. Recently, nonlinear models have attempted to cover this shortfall. In particular, NNR models have been applied with increasing success to financial markets, which often contain nonlinearities (Dunis and Jalilov, 2002).

Table 1.10 Logitl EUR/USD returns estimation

Dependent Variable: BDR-USEURSP Method: ML - Binary Logit Sample(adjusted): 20 1459

Included observations: 1440 after adjusting endpoints Convergence achieved after 3 iterations Covariance matrix computed using second derivatives

Variable Coefficient Std. error z-Statistic Prob.
0 0

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