Overview

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.

Full Product Details

Author:   Abdol S. Soofi ,  Liangyue Cao
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   2
Dimensions:   Width: 15.50cm , Height: 2.60cm , Length: 23.50cm
Weight:   0.789kg
ISBN:  

9781461353102

ISBN 10:   1461353106
Pages:   488
Publication Date:   09 November 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

I Embedding Theory: Time-Delay Phase Space Reconstruction and Detection of Nonlinear Dynamics.- 1 Embedding Theory: Introduction and Applications to Time Series Analysis.- 2 Determining Minimum Embedding Dimension.- 3 Mutual Information and Relevant Variables for Predictions.- II Methods of Nonlinear Modelling and Forecasting.- 4 State Space Local Linear Prediction.- 5 Local Polynomial Prediction and Volatility Estimation in Financial Time Series.- 6 Kalman Filtering of Time Series Data.- 7 Radial Basis Functions Networks.- 8 Nonlinear Prediction of Time Series Using Wavelet Network Method.- III Modelling and Predicting Multivariate and Input-Output Time Series.- 9 Nonlinear Modelling and Prediction of Multivariate Financial Time Series.- 10 Analysis of Economic Time Series Using NARMAX Polynomial Models.- 11 Modeling dynamical systems by Error Correction Neural Networks.- IV Problems in Modelling and Prediction.- 12 Surrogate Data Test on Time Series.- 13 Validation of Selected Global Models.- 14 Testing Stationarity in Time Series.- 15 Analysis of Economic Delayed-Feedback Dynamics.- 16 Global Modeling and Differential Embedding.- 17 Estimation of Rules Underlying Fluctuating Data.- 18 Nonlinear Noise Reduction.- 19 Optimal Model Size.- 20 Influence of Measured Time Series in the Reconstruction of Nonlinear Multivariable Dynamics.- V Applications in Economics and Finance.- 21 Nonlinear Forecasting of Noisy Financial Data.- 22 Canonical Variate Analysis and its Applications to Financial Data.

Reviews

This book is truly a multidisciplinary effort, with contributors including economists, electrical engineers, physicists, mathematicians, and statisticians (myself and Jianming Ye). 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)

This book is truly a multidisciplinary effort, with contributors including economists, electrical engineers, physicists, mathematicians, and statisticians (myself and Jianming Ye). 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)

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