Principles of Neural Model Identification, Selection and Adequacy: With Applications to Financial Econometrics

by Achilleas Zapranis, Apostolos-Paul N. Refenes

Overview

Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling. However a major weakness is the lack of established procedures for testing mis-specified models and the statistical significance of the various parameters which have been estimated. This is particularly important in the majority of financial applications where the data generating processes are dominantly shastic and only partially deterministic. Based on the latest, most significant developments in estimation theory, model selection and the theory of mis-specified models, this volume develops neural networks into an advanced financial econometrics tool for non-parametric modelling. It provides the theoretical framework required, and displays the efficient use of neural networks for modelling complex financial phenomena. Unlike most other books in this area, this one treats neural networks as statistical devices for non-linear, non-parametric regression analysis.

Product Details

ISBN-13:	9781852331399
Publisher:	Springer London
Publication date:	06/22/1999
Series:	Perspectives in Neural Computing
Edition description:	Softcover reprint of the original 1st ed. 1999
Pages:	190
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

1 Introduction.- 2 Neural Model Identification.- 3 Review of Current Practice in Neural Model Identification.- 4 Neural Model Selection: the Minimum Prediction Risk Principle.- 5 Variable Significance Testing: a Statistical Approach.- 6 Model Adequacy Testing.- 7 Neural Networks in Tactical Asset Allocation: a Case Study.- 8 Conclusions.- Appendices.- A Computation of Network Derivatives.- B Generating Random Normal Deviates.- References.