Forecasting and Hedging in the Foreign Exchange Markets (Google eBook)

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Springer Science & Business Media, May 30, 2009 - Business & Economics - 225 pages
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The growing complexity of many real world problems is one of the biggest challenges of our time. The area of international finance is one prominent example where decision making is often fraud to mistakes, and tasks such as forecasting, trading and hedging exchange rates seem to be too difficult to expect correct or at least adequate decisions. From the high complexity of the foreign exchange market and related decision problems, the author derives the necessity to use tools from Machine Learning and Artificial Intelligence, e.g. Support Vector Machines, and to combine such methods with sophisticated financial modelling techniques. The suitability of this combination of ideas is demonstrated by an empirical study and by simulation.
  

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Contents

104 Kernel Selection
91
105 Cross Validation
94
106 Benchmark Models
95
107 Evaluation Procedure
97
1072 Operational Evaluation
98
108 Numerical Results and Discussion
99
Exchange Rate Hedging in a SimulationOptimization Framework
104
Introduction
105

313 Empirical Evidence
17
314 Explanations for Deviations from PPP
19
32 Interest Rate Parity IRP Theorem
21
322 Uncovered Covered Interest Rate Parity UIP
22
324 Explanations for Deviations from IRP
24
Market Efficiency Concepts
27
42 Speculative Efficiency
28
Views from Complexity Theory
29
52 Calculating Fixed Point Market Equilibrium
32
522 Computational Complexity of Decentralized Equilibrium
33
523 AdaptiveInductive Learning of Rational Expectations Equilibria
34
53 Computational Difficulties with Efficiency
35
532 Computational Complexity of Arbitrage
37
Conclusions
38
Exchange Rate Forecasting with Support Vector Machines
39
Introduction
40
Statistical Analysis of Daily Exchange Rate Data
47
821 Stationarity
50
822 Normal Distribution
53
823 Linearity
54
824 Heteroskedasticity
57
825 Nonlinearity
61
826 Results
63
Support Vector Classification
64
93 Supervised Learning
67
94 Structural Risk Minimization
69
95 Support Vector Machines
71
952 Kernel Functions
72
953 Optimal Separating Hyperplane
73
954 Generalized Optimal Separating Hyperplane
79
955 Generalization in High Dimensional Feature Space
81
Description of Empirical Study and Results
83
Input Data Selection
84
102 SVM Model
89
Preferences over Probability Distributions
117
1212 Plain Vanilla Option
119
1213 Straddle
121
122 Formal Relationship Between Firm and Capital Market Expectations
122
123 Specification of Probability Distribution Function
123
124 Expected Utility Maximization and ThreeMoments Ranking
124
1242 Preference Structures over Utility Functions
126
1244 Increasing Wealth Preference
127
1246 Ruin Aversion Preference
128
125 Specification of Utility Function
129
Problem Statement and Computational Complexity
132
132 Computational Complexity Considerations
135
1322 Complexity of Stochastic Combinatorial Optimization
137
1323 Objective Function Characteristics
139
Model Implementation
141
142 Simulation Model
142
Equilibrium
143
Nonlinear Mean Reversion
145
Gaussian Random Walk
148
1425 Aggregation of Components
150
1426 Calibration of Parameters
151
143 Optimization Model
152
1432 Scatter Search and Path Relinking
154
SimulationOptimization Experiments
163
1522 Data Inputs and Parameters
164
1523 Evaluation Procedure
170
153 Results
173
1532 Ex Post Performance
176
Contributions of the Dissertation
180
Exchange Rate Forecasting with Support Vector Machines
183
Exchange Rate Hedging in a SimulationOptimization Framework
185
References
190
References
191
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