Overview

Historical and recent developments at international ?nancial markets show that it is easy to loose money, while it is dif?cult to predict future developments and op- mize decision-making towards maximizing returns and minimizing risk. One of the reasons of our inability to make reliable predictions and to make optimal decisions is the growing complexity of the global economy. This is especially true for the f- eign exchange market (FX market) which is considered as one of the largest and most liquid ?nancial markets. Its grade of ef?ciencyand its complexityis one of the starting points of this volume. From the high complexity of the FX market, Christian Ullrich deduces the - cessity to use tools from machine learning and arti?cial intelligence, e.g., support vector machines, and to combine such methods with sophisticated ?nancial mod- ing techniques. The suitability of this combination of ideas is demonstrated by an empirical study and by simulation. I am pleased to introduce this book to its - dience, hoping that it will provide the reader with interesting ideas to support the understanding of FX markets and to help to improve risk management in dif?cult times. Moreover, I hope that its publication will stimulate further research to contribute to the solution of the many open questions in this area.

Full Product Details

Author:   Christian Ullrich
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2009 ed.
Volume:   623
Dimensions:   Width: 15.50cm , Height: 1.20cm , Length: 23.50cm
Weight:   0.454kg
ISBN:  

9783642004940

ISBN 10:   3642004946
Pages:   207
Publication Date:   12 June 2009
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

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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