Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models

  • Greg N. Gregoriou
  • Razvan Pascalau

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Forecasting Models

    1. Front Matter
      Pages 1-1
    2. Rafael Weiβbach, Wladyslaw Poniatowski, Guido Zimmermann
      Pages 3-17
    3. Zeno Adams, Roland Füss, Philipp Grüber, Ulrich Hommel, Holger Wohlenberg
      Pages 18-27
    4. Humphrey K. K. Tung, Michael C. S. Wong
      Pages 28-50
    5. Ben Tims, Ronald Mahieu
      Pages 51-73
    6. Nikos S. Thomaidis, Efthimios I. Roumpis, Vassilios N. Karavas
      Pages 74-96
    7. Laurence Copeland, Yanhui Zhu
      Pages 97-113
  3. Computational and Bayesian Methods

  4. Back Matter
    Pages 193-195

About this book


This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.


BAYES econometrics forecasting futures GARCH hedging optimization regression volatility

Editors and affiliations

  • Greg N. Gregoriou
    • 1
    • 2
  • Razvan Pascalau
    • 1
  1. 1.State University of New YorkPlattsburghUSA
  2. 2.EDHEC Business SchoolNiceFrance

Bibliographic information