Skip to main content
SpringerLink
Log in
Book cover
  • Book
  • © 1999

Principles of Neural Model Identification, Selection and Adequacy

With Applications to Financial Econometrics

Authors:

  1. Achilleas Zapranis

    1. London Business School, London, UK

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Apostolos-Paul N. Refenes

    1. London Business School, London, UK

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Comes with an Internet site containing data from the case study and demonstration software
  • Provides the reader with a practical tool to address a specific problem in developing financial applications

Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

Sections

Buy it now

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

This is a preview of subscription content, log in via an institution to check for access.

Table of contents (8 chapters)

  1. Front Matter

    Pages i-ix
    PDF

  2. Introduction

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 1-18

  3. Neural Model Identification

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 19-35

  4. Review of Current Practice

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 37-57

  5. Neural Model Selection: the Minimum Prediction Risk Principle

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 59-74

  6. Variable Significance Testing: a Statistical Approach

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 75-112

  7. Model Adequacy Testing

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 113-118

  8. Neural Networks in Tactical Asset Allocation: a Case Study

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 119-155

  9. Conclusions

    • Achilleas Zapranis, Apostolos-Paul N. Refenes
    Pages 157-159

  10. Back Matter

    Pages 161-190
    PDF
Back to top

About this book

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.
Back to top

Keywords

Back to top

Authors and Affiliations

  • London Business School, London, UK

    Achilleas Zapranis, Apostolos-Paul N. Refenes

Back to top

Bibliographic Information

Back to top

Publish with us

Policies and ethics

Back to top
Access via your institution

Buy it now

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Search

Navigation