Modelling and Forecasting Financial Data

1. Front Matter

   Pages i-xxviii

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2. Introduction

   1. Introduction

      • Abdol S. Soofi, Liangyue Cao

      Pages 1-8

3. Embedding Theory: Time-Delay Phase Space Reconstruction and Detection of Nonlinear Dynamics

   1. Front Matter

      Pages 9-9

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   2. Embedding Theory: Introduction and Applications to Time Series Analysis

      • F. Strozzi, J. M. Zaldivar

      Pages 11-42

   3. Determining Minimum Embedding Dimension from Scalar Time Series

      • Liangyue Cao

      Pages 43-60

   4. Mutual Information and Relevant Variables for Predictions

      • Bernd Pompe

      Pages 61-92

4. Methods of Nonlinear Modelling and Forecasting

   1. Front Matter

      Pages 93-93

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   2. State Space Local Linear Prediction

      • D. Kugiumtzis

      Pages 95-113

   3. Local Polynomial Prediction and Volatility Estimation in Financial Time Series

      • Zhan-Qian Lu

      Pages 115-135

   4. Kalman Filtering of Time Series Data

      • David M. Walker

      Pages 137-157

   5. Radial Basis Functions Networks

      • A. Braga, A. C. Carvalho, T. Ludermir, M. de Almeida, E. Lacerda

      Pages 159-178

   6. Nonlinear Prediction of Time Series Using Wavelet Network Method

      • Liangyue Cao

      Pages 179-195

5. Modelling and Predicting Multivariate and Input-Output Time Series

   1. Front Matter

      Pages 197-197

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   2. Nonlinear Modelling and Prediction of Multivariate Financial Time Series

      • Liangyue Cao

      Pages 199-211

   3. Analysis of Economic Time Series Using Narmax Polynomial Models

      • Luis Antonio Aguirre, Antonio Aguirre

      Pages 213-235

   4. Modeling Dynamical Systems by Error Correction Neural Networks

      • Hans-Georg Zimmermann, Ralph Neuneier, Ralph Grothmann

      Pages 237-263

6. Problems in Modelling and Prediction

   1. Front Matter

      Pages 265-265

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   2. Surrogate Data Test on Time Series

      • D. Kugiumtzis

      Pages 267-282

   3. Validation of Selected Global Models

      • C. Letellier, O. Ménard, L. A. Aguirre

      Pages 283-302

   4. Testing Stationarity in Time Series

      • Annette Witt, Jürgen Kurths

      Pages 303-325

Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters.
Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.

• Finance
• calculus
• complex systems
• data analysis
• dynamical systems
• dynamics
• economics
• forecasting
• modeling
• nonlinear dynamics
• time series
• time series analysis
• volatility

"This book is truly a multidisciplinary effort, with contributors including economists, electrical engineers, physicists, mathematicians, and statisticians (myself and Jianming Yé). Although there are many books on nonlinear dynamic techniques, Modelling and Forecasting Financial Data is distinguished by its concerted efforts on practical relevance in financial and economic applications."
(Z.-Q. John Lu, National Institute of Standards and Technology in Technometrics, 46:1 (February 2004)

• Department of Economics, University of Wisconsin-Platteville, USA

  Abdol S. Soofi

• Department of Mathematics, University of Western Australia, Australia

  Liangyue Cao

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