Modelling and Forecasting Financial Data: Techniques of Nonlinear Dynamics / Edition 1 available in Hardcover
- Pub. Date:
- Springer US
Over the last decade, dynamical systems theory and related nonlinear methods have had a major impact on the analysis of time series data from complex systems. Recent developments in mathematical methods of state-space reconstruction, time-delay embedding, and surrogate data analysis, coupled with readily accessible and powerful computational facilities used in gathering and processing massive quantities of high-frequency data, have provided theorists and practitioners unparalleled opportunities for exploratory data analysis, modelling, forecasting, and control.Until now, research exploring the application of nonlinear dynamics and associated algorithms to the study of economies and markets as complex systems is sparse and fragmentary at best. 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.
Table of ContentsList 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.