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130626 ||| eng |
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|a 9783642004957
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|a Ullrich, Christian
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|a Forecasting and Hedging in the Foreign Exchange Markets
|h Elektronische Ressource
|c by Christian Ullrich
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|a 1st ed. 2009
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2009, 2009
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| 300 |
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|a XVIII, 207 p. 43 illus
|b online resource
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|a Motivation -- Analytical Outlook -- Foreign Exchange Market Predictability -- Equilibrium Relationships -- Market Efficiency Concepts -- Views from Complexity Theory -- Conclusions -- Exchange Rate Forecasting with Support Vector Machines -- Statistical Analysis of Daily Exchange Rate Data. -- Support Vector Classification -- Description of Empirical Study and Results -- Exchange Rate Hedging in a Simulation/Optimization Framework -- Preferences over Probability Distributions -- Problem Statement and Computational Complexity -- Model Implementation -- Simulation/Optimization Experiments -- Contributions of the Dissertation -- Exchange Rate Forecasting with Support Vector Machines -- References -- Exchange Rate Hedging in a Simulation/Optimization Framework
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|a Mathematics in Business, Economics and Finance
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|a Finance
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|a Artificial Intelligence
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|a Macroeconomics and Monetary Economics
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|a IT in Business
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|a Social sciences / Mathematics
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|a Artificial intelligence
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|a Financial Economics
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|a Macroeconomics
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|a Business information services
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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|a Lecture Notes in Economics and Mathematical Systems
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|a 10.1007/978-3-642-00495-7
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|u https://doi.org/10.1007/978-3-642-00495-7?nosfx=y
|x Verlag
|3 Volltext
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|a 339
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|a 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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