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

Principles of Neural Model Identification, Selection and Adequacy

With Applications to Financial Econometrics

Paperback

Published: 28th May 1999
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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 stochastic 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.

Introductionp. 1
Neural Model Identificationp. 19
Review of Current Practice in Neural Model Identificationp. 37
Neural Model Selection: the Minimum Prediction Risk Principlep. 59
Variable Significance Testing: a Statistical Approachp. 75
Model Adequacy Testingp. 113
Neural Networks in Tactical Asset Allocation: a Case Studyp. 119
Conclusionsp. 157
Computation of Network Derivativesp. 161
Generating Random Normal Deviatesp. 175
Referencesp. 177
Indexp. 183
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9781852331399
ISBN-10: 1852331399
Series: Perspectives in Neural Computing
Audience: General
Format: Paperback
Language: English
Number Of Pages: 190
Published: 28th May 1999
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.27 x 15.49  x 1.25
Weight (kg): 0.31