Principles of Neural Model Identification, Selection and Adequacy With Applications in Financial Econometrics |
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| Author:
| Zapranis, Achilleas
Refenes, Apostolos-Paul |
| Series title: | Perspectives in Neural Computing Ser. |
| ISBN: | 978-1-85233-139-9 |
| Publication Date: | Jan 1999 |
| Publisher: | Springer
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| Book Format: | Paperback |
| List Price: | AUD $237.95 |
Book Description:
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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...
More DescriptionNeural 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.