Contents

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Introduction 1

Part I Investigation Methods of Complex and Chaotic Nonlinear Dynamics

1 Nonlinear Theory 15

1.1 Dynamical Systems 18

1.1.1 Differential EquationandDifferenceEquation 18

1.1.2 Solution-Trajectory of a Dynamical System 18

1.2 Autonomous and Non-Autonomous Flows, Fixed-Point 20

1.2.1 Definition of a Flow 20

1.2.2 Continuous and Discrete System 21

1.2.3 Definition of a Fixed Point and a Stable Fixed Point 21

1.3 Introduction to the Resolution of Nonlinear Dynamical Systems.. 22

1.3.1 LinearizationofNonlinearModels 22

1.3.2 Linearization Generalized to All Nonlinear Models 23

1.4 Resolution of the Zero-Input Form 24

1.4.1 SolutionoftheGeneral State-SpaceForm 25

1.5 Existence and Uniqueness of Differential System Solutions 26

1.5.1 Lipschitz Condition 26

1.6 Stability of a Dynamical System 27

1.7 Floquet Theory 28

1.7.1 Stability, Floquet Matrix and Eigenvalues 28

1.7.2 Transitions Stemming from the Linear Stability

Loss in Dissipative Systems 32

1.8 TheBifurcationConcept 33

1.8.1 Codimension-1BifurcationsofFixed Points 33

1.8.2 Subcritical BifurcationsofFixed Points 35

1.8.3 Codimension-1BifurcationsofPeriodicOrbits 35

1.9 HopfBifurcation 36

1.9.1 Codimension-1HopfBifurcation 36

1.9.2 CuspandGeneralizedHopfBifurcations 39

1.10 ExamplesofDynamical System Resolution 41

1.10.1 A Stable System 41

1.10.2 An UnstableSystemwithaSaddle Point 42

1.11 Typology of Second-Order Linear Systems 43

1.11.1 Eigenvalues Interpretation 44

1.11.2 Some Representations in the Phase-Plane 44

1.11.3 Behavior Summary of Second-Order Linear Systems ... 46

1.12 Examples of Nonlinear System Resolution 49

1.12.1 A (Bilinear) Nonlinear System and a Saddle-Point 49

1.12.2 PitchforkBifurcation 50

1.12.3 Supercritical HopfBifurcation 51

1.13 Poincare-BendixsonTheorem 55

1.13.1 BendixsonCriterion 55

1.14 Center Manifold Theorem 56

1.15 DefinitionsofChaos 57

1.16 Invariant Sets and Attractors 59

1.16.1 Definition of an Attractor 60

1.16.2 StrangeAttractor 60

1.17 Some Nonlinear Dynamical Systems with Their Associated Attractors 62

1.18 Conservative and Dissipative Systems 70

1.19 Hamiltonian and Optimal Growth Model 71

1.19.1 The Optimal Growth Model with Infinite Horizon 72

1.20 Torus and Combination of Basic Frequencies 72

1.21 Quasiperiodic Route to Chaos (Ruelle Takens), and Landau T" Tori 73

1.21.1 DescriptionofBoth AlternativeScenarios 73

1.21.2 Experimental Illustrations 75

1.21.3 Circle Map, Mode-Locking and Arnold Tongue 77

1.22 An Approach of KAM Theory: Invariant Torus and Chaos 80

1.22.1 KAM Torus: Irrational Rotation Number 83

1.23 Approach of Dynamical Systems by Means of Pendulums and Oscillators 85

1.24 Navier-Stokes Equations of Flows, Attractors and Invariant Measures 89

1.24.1 Navier-Stokes Equations: Basic Model 89

1.24.2 Navier-Stokes Dynamics: Invariant Ergodic Measures, Characteristic Exponents and Hilbert Spaces 91

1.25 The Three-Body Problem (H. Poincare) 98

1.26 The Poincare Section 100

1.26.1 PeriodicSolution 101

1.26.2 QuasiperiodicSolution 102

1.26.3 AperiodicSolution 103

1.26.4 SomeExamples 103

1.27 From Topological Equivalence of Flows Towards the Poincare Map 103

1.27.1 Rotation Number, Orientation-Preserving Diffeomorphism and Topological Equivalence ofFlows 103

1.27.2 Poincare Map (First Return Map) and Suspension 107

1.28 Lyapunov Exponent 109

1.28.1 DescriptionofthePrinciple 110

1.28.2 Lyapunov Exponent Calculation 111

1.28.3 Other Writing and Comment 111

1.28.4 Interpretation of X 112

1.29 Measure of Disorder: Entropy and Lyapunov Characteristic Exponent 113

1.30 Basic Concepts of Nonlinear Theory Illustrated by Unidimensional Logistic Equation: The Paradigm of a Nonlinear Model 114

1.30.1 A Simple Dynamic Equation Which Contains a Subjacent "Deterministic Chaos" 115

1.30.2 FixedPoints 115

1.30.3 LogisticOrbit 119

1.30.4 Sensitive Dependence on Initial Conditions 120

1.30.5 Poincare Sections of the Logistic Equation 122

1.30.6 First-ReturnMap 123

1.30.7 Solutions and Stability of the Model 124

1.30.8 Stability Theorem Applied to Logistic Equation 124

1.30.9 Generalization of the Stability of (Point) Solutions of the Quadratic Map: Generic Stability 125

1.30.10 BifurcationDiagram 125

1.30.11 Monotonic or Oscillatory Solution, Stability Theorem .. 125

1.30.12 Lyapunov Exponent Applied to the Logistic Map 126

1.31 Coupled Logistic Maps and Lce's 126

1.31.1 Period-Doubling, Bifurcations and Subharmonic Cascade 129

1.31.2 Subharmonic Cascade, Accumulation Point 134

1.31.3 StableCyclesandSuper-StableCycles 136

1.31.4 CobwebDiagram 136

1.31.5 BifurcationMeasureorFeigenbaumConstant 141

1.31.6 Iterative Functions of the Logistic Equation 142

1.32 The Bifurcation Paradox: The Final State is Predictable if the Transition is Fast Enough 143

1.32.1 Probability of a Final State and Speed of Transition 143

1.32.2 Variation of the Control Parameter of the Perturbated Logistic Equation 144

1.33 Hyperbolicity and Kolmogorov Capacity Dimension 146

1.33.1 TheCantorSet 147

1.33.2 Finite System and Non-Intersection ofPhase Trajectories 149

1.33.3 Hyperbolicity: Contradiction Between Dissipative

System and Chaos Solved by the Capacity Dimension .. 149

1.33.4 ChaoticAttractorin a SystemofDimension1 152

1.33.5 MeasureoftheComplexityLevel ofAttractors 153

1.34 NonlinearityandHyperbolicity 153

1.34.1 Homoclinic Tangle and Smale Horseshoes Map 153

1.34.2 Smale Horseshoe: Structural Stability 154

1.34.3 HyperbolicSet (AnosovDiffeomorphisms) 157

1.34.4 SymbolicDynamics 158

1.34.5 Properties of the Smale Horseshoe Map 158

1.34.6 Folding and Unfolding Mechanism: Horseshoe and Symbolic Dynamics (Symbolic Coding) 160

1.34.7 Smale-Birkhoff Homoclinic Theorem 161

1.34.8 Hyperbolicity and Hartman-Grobman Theorem: HyperbolicNonlinearFixedPoints 163

1.34.9 HyperbolicStructure 168

1.34.10 Homoclinic Orbit and Perturbation: Melnikov 174

1.34.11 Shilnikov Phenomenon: Homoclinic Orbit in R3 180

1.35 Transitionsand Routesto Chaos 182

1.35.1 Transition to Chaos Through Intermittency 182

1.35.2 Saddle Connections ("Blue Sky Catastrophes")

and Reminder About the Stability Boundaries 188

1.36 Temporal Correlation: Periodicity, Quasiperiodicity, Aperiodicity 199

1.37 Power Spectral Density 201

1.37.1 CharacterizationofDynamical Systems 201

1.37.2 Different Types of Spectra 203

1.38 Van der Pol Oscillator and Spectra 212

1.39 ReconstructionTheorems 220

1.39.1 Embedding, Whitney Theorem (1936) 220

1.39.2 Takens Theorem (1981): A Delay

Embedding Theorem 222

2 Delay Model, SSA and Brownian Motion 227

2.1 Delay Model Applied to Logistic Equation (Medio) 228

2.1.1 Nonlinearities and Lags 228

2.1.2 Application to the Logistic Equation 230

2.2 Singular Spectrum Analysis 234

2.2.1 Singular Spectrum Analysis Principle:

"Windowing", Eigenvector and Projection 234

2.2.2 SSA Applied to the Logistic Equation with Delay Function 239

2.2.3 SSA Applied to a Financial Series (Cac40) 241

2.3 Fractional Brownian Motions 244

2.3.1 Brownian Motion and Random Walk 244

2.3.2 Capacity Dimension of a Fractional Brownian Motion .. 247

2.3.3 Introduction to Persistence and Loops Concepts 250

2.3.4 Comment on DS/TS Process and Brownian Motions 252

Part II Statistics of Complex and Chaotic Nonlinear Dynamics: Invariants and Rare Events

3 Nonlinear Processes and Discrimination 257

3.1 Reminders: Statistics and Probability 257

3.1.1 Random Experiment and Measurement 257

3.1.2 Reduction Principles of Estimators: Invariance Principle, Unbias Principle, Asymptotic Principle 258

3.1.3 Definition of a Process 259

3.1.4 Probability Law, Cumulative Distribution

Function, and Lebesgue Measure on R 259

3.1.5 Integra with Respect to a Measure 260

3.1.6 Density and Lebesgue Measure Zero 261

3.1.7 Random Variables and Transfer Formula 261

3.1.8 Some Laws of Probabilities 262

3.1.9 Autocovariance and Autocorrelation Functions 262

3.2 The ARMA Processes: Stock Markets and Random Walk 262

3.2.1 Reminders: ARMA Processes and Stationarity 263

3.2.2 Dickey-Fuller Tests Applied to French Stock

Index (Cac40) 266

3.2.3 Correlogram Analysis of the Cac40 Sample 270

3.2.4 Estimation of the Model 272

3.3 Econometrics of Nonlinear Processes 274

3.3.1 Stochastic Processes: Evolution of Linear

Modeling Towards Nonlinear Modeling 274

3.3.2 Non-Parametric Test of Nonlinearity: BDS Test of the Linearity Hypothesis Against an

Unspecified Hypothesis 275

3.4 The Non-Parametric Analysis of Nonlinear Models 277

3.4.1 Parametric Analysis: Identification and Estimation of Parametric Models 277

3.4.2 Non-ParametricAnalysis 278

3.4.3 Construction of a Non-Parametric Estimator ofDensity: From Windowing to Kernel Concept 278

3.4.4 Estimator of Density and Conditional Expectation of Regression Between Two Variables 281

3.4.5 Estimator of the Conditional Mode of a Dynamics 283

3.4.6 A First Estimator of Dynamics by Regression 283

3.4.7 Estimator by Polynomial Regression 284

3.4.8 Estimator by the k-Nearest Neighbors Method: KNN ... 284

3.4.9 Estimator by the Radial Basis Function Method: RBF .. 285

3.4.10 A Neural Network Model: Multi-Layer

Perceptron and Limit of Decision 286

3.5 First Statistical Tests of Validation of Chaotic Process Detection: Brock Test and LeBaron and Scheinkman

Random Mixture Test 294

3.5.1 Residual Test of Brock (1986) 295

3.5.2 Scheinkman and LeBaron Random Mixture Test (1989): The Test Weakly Rejects the Hypothesis of Deterministic Chaos and Always Regards

Financial Markets as Stochastic Processes 296

3.6 Long Memory Processes 296

3.6.1 ARFIMA Process 298

3.7 Processes Developed from ARFIMA Process 302

3.7.1 GARMA Processes: To Integrate the Persistent

Periodic Behaviors of Long Memory 302

3.7.2 ARCH Processes Towards FIGARCH Processes: To Integrate the Persistence of Shocks in the Volatility of Long Memory Processes 303

3.8 Rejection of the "Random Walk" Hypothesis for Financial Markets: Lo and MacKinlay Test on the Variance of the NYSE (1988) 305

3.8.1 Specification of the Test: Variances of the Increments for Their Ratio and Difference 306

3.9 Estimation of the Fractional Integration Parameter d or the Hurst Exponent H of an ARFIMA(p,d,q) Process 312

3.9.1 General Information About Long Memory (LRD)

Estimations and Self-Similarity 312

3.10 Estimation of the Parameter d by the Spectral Methods ofanARFIMA Process 313

3.10.1 Estimation of d Based on the Form of the Spectral Density: Regression Method of the Geweke and Porter-Hudak Estimator (GPH: 1983) 313

3.10.2 Estimation of d by the Logarithm of the Power Spectrum: Estimator of Janacek (1982) 315

3.11 Abry-Veitch Estimator (1998) of the Hurst Exponent -Wavelet Analysis of Long Memory Processes: An Effective Approach of Scale Phenomena 317

4 Statistical and Topological Invariants and Ergodicity 329

4.1 The Measurement of a Deterministic Chaos Is Invariant in Time.. 329 4.1.1 Ergodic Theory and Invariant Measurement

Associated with a Dynamics 329

4.1.2 The Measure of Probability of a Deterministic

Chaotic System Is Invariant in Time 331

Part III Spectral and Time-Frequency Theories and Waveforms: Regularity and Singularity

5 Spectral and Time-Frequency Analyses and Signal Processing 343

5.1 Fourier Theory and Wavelets 343

5.1.1 Contribution of the Fourier Analysis to Regular and Stationary Series: An Approach of Linearities 343

5.1.2 Contribution of the Wavelet Analysis to Irregular and Non-Stationary Time Series: An Approach of Nonlinearities 346

5.1.3 A Statistical Theory of the Time-Frequency

Analysis Remains to Be Developed 348

5.2 A Brief Typology of Information Transformations in Signal

Analysis 350

5.2.1 Fourier, Wavelet and Hybrid Analyses 350

5.3 The Fourier Transform 350

5.3.1 Fourier Series and Fourier Transform 350

5.3.2 Interpretation of Fourier Coefficients 352

5.4 The Gabor Transform: A Stage Between the Short Term

Fourier Transform and the Wavelet Transform 354

5.4.1 The Gabor Function 354

5.4.2 The Gabor Transform with a Sliding Window:

The "Gabor Wavelet" 355

5.5 The Wavelet Transform 356

5.5.1 A Wavelet y Is a Function of Zero Average, i.e. Zero-Integral: J+~v(t)dt = 0 356

5.5.2 Wavelets and Variable-Window 357

5.5.3 The Wavelet Transform 357

5.5.4 Wavelet Transform and Reconstruction 361

5.6 Distinction of Different Window Mechanisms by Type of Transformation 367

5.7 Wavelet Transform of Function or Time Series 367

5.7.1 The Wavelets Identify the Variations of a Signal 367

5.7.2 Continuous Wavelet Transform 369

5.7.3 Discrete Wavelet Transform 370

5.7.4 Wavelet Models: "Gauss Pseudo-Wavelet", Gauss-Derivative, Morlet and Sombrero 370

5.8 Aliasing and Sampling 373

5.9 Time-Scale Plane (b,a), Cone of Influence 374

5.9.1 Cone of Influence and Time-Scale Plane 374

5.9.2 Time-Frequency Plane 376

5.10 Heisenberg Boxes and Time-Frequency Plane 376

5.10.1 Concept of Time-Frequency Atom: Concept ofWaveform Family 377

5.10.2 Energy Density, Probability Distribution and Heisenberg Boxes 378

5.10.3 Spectrogram, Scalogram and Energy Conservation 381

5.10.4 Reconstruction Formulas of Signal: Stable and Complete Representations 383

5.11 Wiener Theory and Time-Frequency Analysis 384

5.11.1 Introduction to the Correlogram-Periodogram

Duality: Similarities and Resemblances Researches 384

5.11.2 Elements of Wiener Spectral Theory and Extensions 388

5.12 The Construction of Orthonormal Bases and Riesz Bases 399

5.12.1 Signal of Finite Energy 399

5.12.2 Reminders: Norms and Banach Spaces 399

5.12.3 Reminders: Inner Products and Hilbert Spaces 400

5.12.4 Orthonormal Basis 400

5.12.5 Riesz Basis, Dual Family and Biorthogonality 401

5.12.6 Orthogonal Projection 402

5.12.7 The Construction of Orthonormal Basis and Calculation of the "Detail" Coefficient on Dyadic Scale 402

5.13 Concept of Frames 403

5.13.1 The Fourier Transform in L2 (R) 403

5.13.2 Frames 405

5.13.3 Tiling of the Time-Frequency Plane by Fourier and Wavelets Bases 408

5.14 Linear and Nonlinear Approximations of a Signal by Projection on an Orthonormal Basis 409

5.14.1 General Framework of the Linear Approximation and Karhunen-Loeve Optimal Basis 409

5.14.2 Nonlinear Approximation and Adaptive Basis Dependent on the Signal: Regularity and Singularity ... 410

5.14.3 Donoho and Johnstone Nonlinear Estimation: Algorithm with Threshold 417

5.14.4 Nonlinear Estimators are More Efficient to Minimize the Bayesian Risk: Optimization byMinimax 418

5.14.5 Approximation by the "Matching Pursuit":

A General Presentation 420

5.14.6 Comparison of Best Bases and Matching Pursuits 426

5.15 The Multiresolution Analysis Notion 427

5.15.1 (Quadratic) Conjugate Mirror Filter 427

5.15.2 Multiresolution Analysis 428

5.16 Singularity and Regularity of a Time Series: Self-Similarities, Multifractals and Wavelets 432

5.16.1 Lipschitz Exponent (or Holder Exponent): Measurement of Regularity and Singularity by

Means of the Holder Functions a(t) 432

5.16.2 n Wavelet Vanishing Moments and Multiscale Differential Operator of Order n 434

5.16.3 Regularity Measures by Wavelets 435

5.16.4 Detection of Singularities: The Maxima of the Modulus of Wavelet Transform are Associated with the Singularities 436

5.16.5 Self-Similarities, Wavelets and Fractals 437

5.16.6 Spectrum of Singularity: Multifractals, Fractional Brownian Motions and Wavelets 440

5.17 The Continuous Wavelet Transform 447

5.17.1 Application to a Stock Exchange Index: Cac40 447

5.18 Wigner-Ville Density: Representation of the Fourier and Wavelet Atoms in the Time-Frequency Plane 456

5.18.1 Cohen's Class Distributions and Kernels of Convolution 459

5.19 Introduction to the Polyspectral Analysis

(for the Nonlinearities) 462

5.19.1 Polyspectral Analysis Definition for Random

Processes with Zero-Average 463

5.19.2 Polyspectra and Nonlinearities 464

5.20 Polyspectral and Wavelet Bicoherences 466

5.20.1 A New Tool of Turbulence Analysis: Wavelet Bicoherence 466

5.20.2 Compared Bicoherences: Fourier and Wavelet 474

5.21 Arguments in Favor of Wavelet Analysis Compared to Fourier Analysis 475

5.21.1 Signal Deformation by Diffusion of Peaks, Discontinuities and Errors in the Fourier Transform 475

5.21.2 Wavelets are Better Adapted to the Signal by Respecting Discontinuities and Peaks, Because they Identify the Variations 476

5.21.3 Wavelets are Adapted to Non-Stationary Signals 477

5.21.4 Signal Energy is Constant in the Wavelet Transform 477

5.21.5 Wavelets Facilitate the Signal "Denoizing" 477

5.21.6 Wavelets are Less Selective in Frequency than the Fourier Transform 477

5.21.7 The Hybrid Transformations Allow an Optimal Adaptation to Transitory Complex Signals 478

6 The Atomic Decompositions of Signals 479

6.1 A Hybrid Transformation: Evolution of the "Matching

Pursuit" Towards the Mallat and Zhang Version 479

6.1.1 Construction of Sinusoid and Wavelet Packets 480

6.1.2 Reminders About the Time-Frequency Atoms 483

6.1.3 Reminders About the Matching Pursuit 484

6.1.4 Improvement of the Algorithm 486

6.1.5 Mallat and Zhang Version of Matching Pursuit with Dictionaries of Time-Frequency Atoms g7 487

6.2 Applications of the Different Versions of the "Matching

Pursuit" to a Stock-Exchange Index: Cac40 495

6.2.1 Matching Pursuit: Atomic Decomposition with Fourier Dictionary 495

6.2.2 Matching Pursuit: Atomic Decomposition with Wavelet Dictionary 497

6.2.3 An Application of the Mallat and Zhang "Matching Pursuit" Version: An Adaptive Atomic Decomposition of a Stock-Exchange Index with Dictionaries of Time-Frequency Atoms g7 499

6.3 Ramsey and Zhang Approach of Stock Market Crises by Matching Pursuit with Time-Frequency Atom

Dictionaries: High Intensity Energy Periods 503

6.3.1 The Dirac Function Would Allow to Distinguish Isolated and Intense Explosions: Internal Shocks and External Shocks 504

6.4 Comments About Time-Frequency Analysis 505

Part IV Economic Growth, Instability and Nonlinearity

7 Evolution of Economic Growth Models 509

7.1 Growth and Distribution in the Neoclassical Framework 511

7.1.1 Aggregates and National Income 511

7.1.2 Neo-Classical Production Function and Diminishing Returns 512

7.1.3 Conditions of the Optimal Combination of the Factors K and L 513

7.1.4 Optimal Combination of Factors, and Tendency

Towards a Zero Profit in the Long Term 514

7.1.5 The Ground Rent and the Ricardo Growth Model 517

7.1.6 The Expansion Path and the Limitation of the Nominal National Income Growth 519

7.1.7 Stationary Per Capita Income of the Solow Model in the Long Term 520

7.2 Linear Technical Relations Outside the Neo-Classical Theory Framework of the Distribution: Von Neumann

Model of Semi-Stationary Growth (1946) 521

7.2.1 Primacy of the Organization of "Technical Processes" .. 521

7.2.2 PresentationoftheVon NeumannModel 521

7.2.3 The Optimal Path and the Golden Rule in the Von Neumann Model 525

7.2.4 Comments About Von Neumann and Solow Models 528

7.3 Stability, Stationarity and Diminishing Returns of the Capital: The Solow Model (1956) 531

7.3.1 Diminishing Returns and Stationarity of the Per

Capita Product 531

7.3.2 The Reference Model 531

7.3.3 Introduction of the Technological Progress into the Solow Model and Balanced Growth Path 539

7.3.4 Evolution of the Solow Model and the Neo-Classical Models 540

7.4 Introduction of Externalities, and Instability: Endogenous

Growth Theory 543

7.4.1 Interrupted Growth in the Solow Model and Long-Term Stationarity 543

7.4.2 Introduction of Positive Externalities 544

7.4.3 Endogenous Growth Without Externality 548

7.5 Incentive to the Research by Profit Sharing: The Romer

Model (1986-1990) 548

7.5.1 Basic Components of the Romer Model 549

7.5.2 Imperfect Competition, Externalities and R&D Optimality: The Reconciliation in the Romer Model . . . . 553

7.5.3 Romer Model and Transfer of Technology

Between Countries 555

7.6 Nonlinearities and Effect of Economic Policies in the Endogenous Growth Models 558

7.6.1 AK Model: The Limit Case of Solow Model for a = 1 558

7.6.2 Linearities and Endogenous Growth 559

7.6.3 Externalities and AK Models 561

7.6.4 Nonlinearities and Effect of Economic Policies in Endogenous Growth Models: Transitory or PermanentEffects 563

7.7 Basin of Instability and Saddle-Point: Optimal Growth

Model of Ramsey without Technological Progress 563

7.7.1 Intertemporal Choices and Utility Function 563

7.7.2 The Production Function 564

7.7.3 Mechanism of Optimization, and Trajectories 566

7.8 Basin of Instability and Saddle-Point: Optimal Growth

Model of Cass Koopmans Ramsey with Technological Progress .. 569

7.8.1 Enterprises and Production Function 569

7.8.2 Households and Maximization of the Utility

Function Under the Budget Constraint 570

7.8.3 Dynamics and Balanced Growth Path 573

7.8.4 Comments About the Trajectories and Maximization of the Level of Consumption 578

7.8.5 Equilibria and Instability of Solutions 579

7.8.6 Endogenous Growth Without Externality, and Saddle Point 580

7.9 Day Model (1982): Logistic Function, Periodic and Chaotic Behaviors 581

7.9.1 TheModel 581

7.9.2 From Dynamics of Capital Towards Logistic Function .. 582

7.9.3 Periodic and Chaotic Solutions of the Dynamics of k ... 583

7.10 Day-Lin Model (1992): Imperfect Information and Strange Attractor 583

7.10.1 Imperfect Information, Price Uncertainty and Adaptive Expectations 584

7.10.2 Chaotic Growth and Intertemporal Non-Optimality 586

7.11 The Instability of Stock Markets, and Random Processes:

Model of Portfolio Choice 588

7.11.1 Dynamics of Accumulation of k and m 589

7.11.2 The Solution is a Saddle-Point 590

7.12 Goodwin's Cyclical Growth Model 592

7.13 Catastrophe Theory and Kaldor Model 594

7.14 Overlapping Generations Models: Cycles, Chaos 597

7.14.1 Benhabib-Day (1982) 598

7.14.2 Grandmont (1985) 599

7.15 Optimal Growth Models: Convergence, Cycles, Chaos 600

7.15.1 Boldrin-Woodford (1990) 601

7.15.2 Turnpike Theorem (and Anti-Turnpike Theorem) 602

7.15.3 Benhabib-Nishimura Optimal Growth Model

(1979): Equilibrium Limit Cycle 603

7.16 Nonlinearities and Cycle Theory 605

7.16.1 Nonlinearities and Chaos Theory 605

7.16.2 Real Business Cycle Theory and Concept of Shock 606

8 Efficiency and Random Walk 609

8.1 Market Efficiency and Random Walk: Stock Market

Growth and Economic Growth 609

8.1.1 Stock Exchange: Perfect Competition Market 609

8.1.2 Stock Exchange: Advanced Indicator of Economic Activity 610

8.1.3 Indicators of Value Creation 612

8.1.4 Corporate Governance: Market Imperfection Factors ... 613

8.1.5 Modigliani-Miller Theorem: Neutrality ofFinance on Market Perfection 614

8.1.6 Role of Expectations on Equilibria and Markets: ExpectationConcepts 615

8.1.7 The Lucas Critique of Rational Expectations and the Superneutrality of Economic Policies 619

8.1.8 Rational Bubbles and Sunspots Models 623

8.1.9 Efficiency and Instability of Financial Markets:

A Non-Probabilisable Universe 628

8.1.10 The Question of the Imperfection, Inefficiency and Non-Random Walk of Stock Markets 631

Conclusion 633

Postface 637

A Mathematics 641

A.1 Relations, Metrics, Topological Structures 641

A. 1. 1 Relations and Diffeomorphisms 643

A.1.2 Metric Spaces and Topological Spaces 646

A.2 PreHilbert, Hilbert and Banach Spaces 655

A.2.1 Normed Spaces 655

A.2.2 PreHilbert Spaces 655

A.2.3 Banach Spaces and Hilbert Spaces 656

A.2.4 Differentiable Operators 657

A.2.5 Banach Fixed-Point Theorem 657

A.2.6 Differential and Integral Operators 658

A.3 Complex Number Field, Holomorphic Functions and Singularities 658

A.3. 1 Complex Number 660

A.3.2 Construction of the Field C of Complex Numbers 661

A.3.3 Geometrical RepresentationofComplexNumbers 662

A.3.4 Operations in the Gauss Complex Plane 663

A.3.5 Algebraic Closure of C 664

A.3.6 Alembert-GaussTheorem 665

A.3.7 Exponential, Logarithm in C 665

A.3.8 Others Properties of C, and Topology Theorem of C 666

A.3.9 Riemann Sphere (Compactification) 666

A.3.10 Holomorphic Function, Cauchy-Riemann

Conditions and Harmonic Function 667

A.3.11 Singularity of Holomorphic Functions, Laurent

Series and Meromorphic Function 670

A.4 Surfaces and Manifolds 673

A.4.1 Closed Surfaces, Surfaces with Boundary 673

A.4.2 Classification of Closed Surfaces 676

A.4.3 Orientability and Topological Invariance 678

A.4.4 Connectivity Number 679

A.4.5 Riemann Surfaces 679

A.4.6 Manifolds and Differentiable Topology 681

A.5 Topology 683

A.6 Geometry and Axioms 685

A.6.1 AbsoluteGeometry 685

A.6.2 Euclidean and Non-Euclidean Metrics 687

A.6.3 Affine and Projective Planes 691

A.6.4 Prooective Metric 693

A.6.5 Order and Orientation 693

A.7 Series Expansions 696

A.7.1 Taylor Polynomials and Remainders 696

A.7.2 Applications to Local Extrema 698

A.7.3 Taylor Series 698

A.7.4 Analytic Functions 699

A.7.5 Binomial Series 700

A.8 Distribution Theory 701

A.8.1 DerivationofDistributions 703

A.8.2 Multiplication 703

A.8.3 Support of Distributions 703

A.8.4 Convolution of Distributions 704

A.8.5 Applications to Partial Differential Equations with Constant Coefficients 704

A.8.6 Use of Elementary Solutions 705

A.9 Approximation Theory 706

A.9.1 Best Approximations 707

A. 10 InterpolationTheory 709

A. 10.1 Lagrange Method 709

A.10.2 Newton-Gregory method 709

A.10.3 Approximation by Interpolation Polynomials 710

A. 11 Numerical Resolution of Equations 711

A.11.1 Simple Iterative Methods 711

A.11.2 Newton-Raphson Method 711

A. 11.3 Linear Interpolation Method (Regula Falsi) 712

A. 11.4 Horner's Schema 712

A.11.5 GraeffeMethod 713

A.12 Second-Order Differential Equations 713

A.12.1 General Resolution of Linear Differential

Equations of Second-Order 714

A. 12.2 Resolution of Linear Homogeneous Equations 714

A.12.3 Particular Solution of a Non-Homogeneous Equation ... 715

A.12.4 Linear Differential Equations of Second-Order with Constant Coefficients 715

A.13 OtherReminders 717

A.13.1 Basic Reminders in Mathematics and Statistics 717

Bibliography 723

Index 733

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Insiders Online Stocks Trading Tips

Insiders Online Stocks Trading Tips

We Are Not To Be Held Responsible If Your Online Trading Profits Start To Skyrocket. Always Been Interested In Online Trading? But Super-Confused And Not Sure Where To Even Start? Fret Not! Learning It Is A Cakewalk, Only If You Have The Right Guidance.

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