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 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.

Full Product Details

Author:   Achilleas Zapranis ,  Apostolos-Paul N. Refenes
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   Softcover reprint of the original 1st ed. 1999
Dimensions:   Width: 15.50cm , Height: 1.00cm , Length: 23.50cm
Weight:   0.320kg
ISBN:  

9781852331399

ISBN 10:   1852331399
Pages:   190
Publication Date:   28 May 1999
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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