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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.
List of Figures. List of Tables. Preface. Contributing Authors. Introduction; A.S. Soofi, Liangyue Cao. Part I: Embedding Theory: Time-Delay Phase Space Reconstruction and Detection of Nonlinear Dynamics. 1. Embedding Theory: Introduction and Applications to Time Series Analysis; F. Strozzi, J.M. Zaldivar. 2. Determining Minimum Embedding Dimension; Liangyue Cao. 3. Mutual Infomation and Relevant Variables for Predictions; B. Pompe. Part. II: Methods of Nonlinear Modelling and Forecasting. 4. State Space Local Linear Prediction; D. Kugiumtzis. 5. Local Polynomial Prediction and Volatility Estimation in Financial Time Series; Zhan-Qian Lu. 6. Kalman Filtering of Time Series Data; D.M. Walker. 7. Radial Basis Functions Networks; A. Braga, et al. 8. Nonlinear Prediction of Time Series Using Wavelet Network Method; Liangyue Cao. Part III: Modelling and Predicting Multivariate and Input-Output Time Series. 9. Nonlinear Modelling and Prediction of Multivariate Financial Time Series; Liangyue Cao. 10. Analysis of Economic Time Series Using NARMAX Polynomial Models; L.A. Aquirre, A. Aguirre. 11. Modeling dynamical systems by Error Correction Neural Networks; H.-G. Zimmermann, et al. Part IV: Problems in Modelling and Prediction. 12. Surrogate Data Test on Time Series; D. Kugiumtzis. 13. Validation of Selected Global Models; C. Letellier. 14. Testing Stationarity in Time Series; A. Witt, J. Kurths. 15. Analysis of Economic Delayed-Feedbak Dynamics; H.U. Voss, J. Kurths. 16. Global Modeling and Differential Embedding; J. Maquet, et al. 17. Estimation of Rules Underlying Fluctuating Data; S. Siegert, et al. 18. Nonlinear Noise Reduction; R. Hegger, et al. 19. Optimal Model Size; Jianming Ye. 20. Influence of Measured Time Series in the Reconstruction of Nonlinear Multivariable Dynamics; C. Letellier, L.A. Aguirre. Part. V: Applications in Economics and Finance. 21. Nonlinear Forecasting of Noisy Financial Data; A.S. Soofi, L. Cao. 22. Canonical Variate Analysis and its Applications to Financial Data; B. Pilgram, et al. Index.
Show moreModelling 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.
List of Figures. List of Tables. Preface. Contributing Authors. Introduction; A.S. Soofi, Liangyue Cao. Part I: Embedding Theory: Time-Delay Phase Space Reconstruction and Detection of Nonlinear Dynamics. 1. Embedding Theory: Introduction and Applications to Time Series Analysis; F. Strozzi, J.M. Zaldivar. 2. Determining Minimum Embedding Dimension; Liangyue Cao. 3. Mutual Infomation and Relevant Variables for Predictions; B. Pompe. Part. II: Methods of Nonlinear Modelling and Forecasting. 4. State Space Local Linear Prediction; D. Kugiumtzis. 5. Local Polynomial Prediction and Volatility Estimation in Financial Time Series; Zhan-Qian Lu. 6. Kalman Filtering of Time Series Data; D.M. Walker. 7. Radial Basis Functions Networks; A. Braga, et al. 8. Nonlinear Prediction of Time Series Using Wavelet Network Method; Liangyue Cao. Part III: Modelling and Predicting Multivariate and Input-Output Time Series. 9. Nonlinear Modelling and Prediction of Multivariate Financial Time Series; Liangyue Cao. 10. Analysis of Economic Time Series Using NARMAX Polynomial Models; L.A. Aquirre, A. Aguirre. 11. Modeling dynamical systems by Error Correction Neural Networks; H.-G. Zimmermann, et al. Part IV: Problems in Modelling and Prediction. 12. Surrogate Data Test on Time Series; D. Kugiumtzis. 13. Validation of Selected Global Models; C. Letellier. 14. Testing Stationarity in Time Series; A. Witt, J. Kurths. 15. Analysis of Economic Delayed-Feedbak Dynamics; H.U. Voss, J. Kurths. 16. Global Modeling and Differential Embedding; J. Maquet, et al. 17. Estimation of Rules Underlying Fluctuating Data; S. Siegert, et al. 18. Nonlinear Noise Reduction; R. Hegger, et al. 19. Optimal Model Size; Jianming Ye. 20. Influence of Measured Time Series in the Reconstruction of Nonlinear Multivariable Dynamics; C. Letellier, L.A. Aguirre. Part. V: Applications in Economics and Finance. 21. Nonlinear Forecasting of Noisy Financial Data; A.S. Soofi, L. Cao. 22. Canonical Variate Analysis and its Applications to Financial Data; B. Pilgram, et al. Index.
Show moreI 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.
Springer Book Archives
"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)
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