Principles of Neural Model Identification, Selection and Adequacy with Applications to Financial Econometrics (Perspectives in Neural Computing)

Zapranis, Achilleas, N.Refenes, Apostolos-Paul

ISBN 10: 1852331399 / ISBN 13: 9781852331399

Editore: Springer, 1999

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190 pp., paperback, previous owner's name neatly inked to the title page, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Codice articolo ZB1315384

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Titolo: Principles of Neural Model Identification, ...

Casa editrice: Springer

Data di pubblicazione: 1999

Legatura: Brossura

Condizione: Fine

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Riassunto:

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.

Contenuti:

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.