Foreign Exchange Rates : A Research Overview of the Latest Prediction Techniques book cover
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Foreign Exchange Rates
A Research Overview of the Latest Prediction Techniques



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ISBN 9780367609917
February 7, 2021 Forthcoming by Routledge
96 Pages 11 B/W Illustrations

 
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Book Description

Predicting foreign exchange rates has presented a long-standing challenge for economists. However, the recent advances in computational techniques, statistical methods, newer datasets on emerging market currencies etc. offer some hope. While we are still unable to beat a driftless random walk model, there has been serious progress in the field. This book provides an in-depth assessment of the use of novel statistical approaches and machine learning tools in predicting foreign exchange rate movement.

Firstly, it offers a historical account of how exchange rate regimes have evolved over time, which is critical to understanding turning points in a historical time series. It then presents an overview of the previous attempts at modelling exchange rates, and how different methods fared during this process. At the core sections of the book, the author examines the time-series characteristics of exchange rates and how contemporary statistics and ML can be useful in improving predictive power, compared to previous methods used.

Exchange rate determination is an active research area and this book will appeal to graduate-level students of international economics, international finance, open economy macroeconomics and management. The book is written in a clear, engaging and straightforward way and will greatly improve access to this much-needed knowledge in the field.

Table of Contents

List of Figures

List of Tables

Preface

1. A Brief Introduction of The Global Foreign Exchange Market

2. Prominent Structural Models for Exchange Rate Determination

2.1. Interest Parity Rules

2.2. Purchasing Power Parity Rule

2.3. Monetary Model of Exchange Rates

2.4. Asset Pricing Approach to Exchange Rate Determination

2.5. A Common Problem of The Models

3. Nonlinearity of The Exchange Rates

4. Causes of Nonlinearity Of The Exchange Rates

4.1. Nonlinear Models in The Literature

4.1.1. ARCH and Its Variants

4.1.2. Regime Switches

4.1.3. Chaotic Dynamical Systems

5. Machine Learning

5.1. Bias-Variance Trade-off

5.2. Model Validation

5.2.1. Non-exhaustive Cross-validation Methods: Hold-out Method

5.2.2. Non-exhaustive Cross-validation Methods: K-fold Cross-validation

5.2.3. Non-exhaustive Cross-validation Methods: Stratified K-fold Cross-validation

5.2.4. Exhaustive Cross-validation Methods: Leave-p-out and Leave-one-out Cross-validations

5.3. Model Types: Supervised Models vs Unsupervised Models

5.3.1. Lasso, Ridge, and Elastic-Net Regressions

6. Comparing the Predictive Powers Of Models

6.1. The Fama Equation

6.2. Purely Time Series TAR and STR Models

6.3. Structural TAR and STR Models

6.4. Lasso, Ridge and Elastic Net Regressions

7. Should We Focus On Sign Estimations Rather Than Point Estimations?

7.1. CART Analysis

7.2. Decision Trees and Random Forests

7.3. Predicting the Sign of Exchange Rates with a Random Forest

8. Final Remarks

Bibliography

Index

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Author(s)

Biography

Arif Orçun Söylemez is an Associate Professor at the Economics Department of Marmara University, Istanbul, Turkey.