Modelling and Forecasting Financial Data: Techniques of Nonlinear Dynamics

Front Cover
Abdol S. Soofi, Liangyue Cao
Springer Science & Business Media, Mar 31, 2002 - Business & Economics - 488 pages
0 Reviews
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.
  

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

EMBEDDING THEORY INTRODUCTION AND APPLICATIONS TO TIME SERIES ANALYSIS
11
2 Embedding Theories
14
3 Chaotic Time Series Analysis
18
4 Examples of Applications in Economics
32
5 Conclusions
37
DETERMINING MINIMUM EMBEDDING DIMENSION FROM SCALAR TIME SERIES
43
2 Major existing methods
44
3 False nearest neighbor method
45
7 Conclusion
262
PROBLEMS IN MODELLING AND PREDICTION
265
SURROGATE DATA TEST ON TIME SERIES
267
1 The Surrogate Data Test
269
2 Implementation of the Nonlinearity Test
273
3 Application to Financial Data
276
4 Discussion
277
VALIDATION OF SELECTED GLOBAL MODELS
283

4 Averaged false nearest neighbor method
47
5 Examples
49
6 Summary
59
MUTUAL INFORMATION AND RELEVANT VARIABLES FOR PREDICTIONS
61
2 Theoretical Background
64
3 Mutual Information Analysis
69
4 Mutual Information Algorithm
72
5 Examples
78
6 Conclusions
88
Appendix
89
3 A Property of GMI
90
METHODS OF NONLINEAR MODELLING AND FORECASTING
93
STATE SPACE LOCAL LINEAR PREDICTION
95
1 Introduction
96
2 Local prediction
97
3 Implementation of Local Prediction Estimators on Time Series
104
4 Discussion
109
LOCAL POLYNOMIAL PREDICTION AND VOLATILITY ESTIMATION IN FINANCIAL TIME SERIES
115
2 Local polynomial method
117
3 Technical setup for statistical theory
119
4 Prediction methods
123
5 Volatility estimation
126
6 Risk analysis of AOL stock
128
7 Concluding remarks
132
KALMAN FILTERING OF TIME SERIES DATA
137
2 Methods
138
3 Examples
147
4 Summary
156
RADIAL BASIS FUNCTIONS NETWORKS
159
1 Introduction
160
2 Radial Functions
161
4 An example of using RBF for financial timeseries forecasting
172
5 Discussions
173
6 Conclusions
175
7 Acknowledgements
176
NONLINEAR PREDICTION OF TIME SERIES USING WAVELET NETWORK METHOD
179
2 Nonlinear predictive model
180
3 Wavelet network
181
4 Examples
185
5 Discussion and conclusion
192
MODELLING AND PREDICTING MULTIVARIATE AND INPUTOUTPUT TIME SERIES
197
NONLINEAR MODELLING AND PREDICTION OF MULTIVARIATE FINANCIAL TIME SERIES
199
2 Embedding multivariate data
200
3 Prediction and relationship
202
4 Examples
203
5 Conclusions and discussions
209
ANALYSIS OF ECONOMIC TIME SERIES USING NARMAX POLYNOMIAL MODELS
213
2 NARMAX Polynomial Models
216
3 Algorithms
220
4 Illustrative Results
223
5 Discussion
233
MODELING DYNAMICAL SYSTEMS BY ERROR CORRECTION NEURAL NETWORKS
237
1 Introduction
238
2 Modeling Dynamic Systems by Recurrent Neural Networks
239
3 Modeling Dynamic Systems by Error Correction
246
4 VariantsInvariants Separation
250
5 Optimal State Space Reconstruction for Forecasting
253
6 Yield Curve Forecasting by ECNN
260
1 Introduction
284
2 Bifurcation diagrams for model with parameter dependence
294
3 Synchronization
296
4 Conclusion
300
TESTING STATIONARITY IN TIME SERIES
303
2 Description of the tests
306
3 Applications
312
4 Summary and discussion
323
ANALYSIS OF ECONOMIC DELAYEDFEEDBACK DYNAMICS
327
1 Introduction
328
2 Noiselike behavior induced by a NerloveArrow model with time delay
329
3 A nonparametric approach to analyze delayedfeedback dynamics
332
4 Analysis of NerloveArrow models with time delay
336
5 Model improvement
337
6 Two delays and seasonal forcing
339
7 Analysis of the USA gross private domestic investment time series
341
8 The ACE algorithm
343
9 Summary and conclusion
345
GLOBAL MODELING AND DIFFERENTIAL EMBEDDING
351
2 Global modeling techniques
352
3 Applications to Experimental Data
367
4 Discussion on applications
369
5 Conclusion
371
ESTIMATION OF DETERMINISTIC AND STOCHASTIC RULES UNDERLYING FLUCTUATING DATA
375
2 Stochastic Processes
376
3 Dynamical Noise
378
5 Analysis Examples of Artificially Created Time Series
381
6 Scale Dependent Complex Systems
389
7 Financial Market
390
8 Turbulence
393
9 Conclusions
396
NONLINEAR NOISE REDUCTION
401
1 Noise and its removal
402
2 Local projective noise reduction
403
3 Applications of noise reduction
407
Noise reduction for economic data
413
OPTIMAL MODEL SIZE
417
2 Selection of Nested Models
419
General Estimation Procedures
420
4 Applications and Implementation Issues
425
INFLUENCE OF MEASURED TIME SERIES IN THE RECONSTRUCTION OF NONLINEAR MULTIVARIABLE DYNAMICS
429
2 Non equivalent observables
432
3 Discussions on applications
444
4 Conclusion
448
V APPLICATIONS IN ECONOMICS AND FINANCE
453
NONLINEAR FORECASTING OF NOISY FINANCIAL DATA
455
2 Methodology
457
3 Results
459
4 Conclusions
462
CANONICAL VARIATE ANALYSIS AND ITS APPLICATIONS TO FINANCIAL DATA
467
1 Nonlinear Markov Modelling
470
2 Implementation of Forecasting
473
3 The GARCH11t Model
474
4 Data Analysis
475
5 Empirical Results
476
6 Discussion
479
Index
485
Copyright

Common terms and phrases

References to this book