Principles of Neural Model Identification, Selection and Adequacy
Subtitle: With Applications to Financial Econometrics
By: by Achilleas Zapranis, Apostolos-Paul N. Refenes
Contributors: Refenes, Apostolos-Paul N.
Series: Perspectives in Neural Computing, 1431-6854; Perspectives in neural computing
Publisher: Springer London
Published: 1999
Place of publication: London
Language: eng
Format: [electronic resource] :
Number of pages: 190
ISBN-13: 9781447105596
Description: 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.
Table of contents: title: Introduction; title: Neural Model Identification; title: Review of Current Practice in Neural Model Identification; title: Neural Model Selection: Minimum Prediction Risk Principle; title: Variable Significance Testing; title: Model Adequacy Testing; title: Tactical Asset Allocation; title: Implied Volatility Forecasting for Options Pricing with Neural Nets; title: Appendix 1: Computing Network Derivatives; title: Appendix 2: Generating Random Deviates; title: Bibliography.