Demystifying the Meese-Rogoff Puzzle

I. Moosa · K. Burns

Dec 2014 · Springer

About this ebook

For the past 30 years international monetary economists have believed that exchange rate models cannot outperform the random walk in out-of-sample forecasting as a result of the 1983 paper written by Richard Meese and Kenneth Rogoff. Marking the culmination of their extensive research into the Meese-Rogoff puzzle, Moosa and Burns challenge the orthodoxy by demonstrating that the naïve random walk model can be outperformed by exchange rate models when forecasting accuracy is measured by metrics that do not rely exclusively on the magnitude of forecasting error. The authors present compelling evidence, supported by their own measure: the 'adjusted root mean square error', to finally solve the Meese-Rogoff puzzle and provide a new alternative. Demystifying the Meese-Rogoff Puzzle will appeal to academics with an interest in exchange rate economics and international monetary economics. It will also be a useful resource for central banks and financial institutions.

Ratings and reviews

In this book, Moosa and Burns ‘demystify’ one of the oldest puzzles in International Finance, that being that conventional exchange rate models cannot outperform the naïve random walk model in a simple out-of-sample forecasting exercise. In this comprehensive analysis, Moosa and Burns re-examine this ‘myth’, and show that trying to outperform the random walk should not be the yardstick used to judge the performance of conventional exchange rate models. Instead, they show that the performance of these exchange rate models should be assessed using other criteria, such as correctly predicting the direction of change and the level of profitability (or more generally, utility), the so-called ‘adjusted’ root mean squared error, the use of the Fair and Schiller (1990), and comparing the actual forecast to the perfect forecast. Moosa and Burns then analyse every one of the six explanations usually offered as the reason why the exchange rate models have failed, these being: 1). The stochastic movements in the underlying parameters; 2). Model misspecification; 3). The effect of nonlinearities; 4). Simultaneous equation bias; 5). Sampling errors, and 6). Modelling expectations. They conclude by showing that the random walk cannot be beaten if the forecasting performance of exchange rate models is judged by conventional measures of forecasting accuracy, but can be beaten if the forecasting performance is measured by the accuracy of direction and profitability. Given the thorough analysis undertaken by Moosa and Burns, this book will be the final authoritative word on conventional exchange rate models beating the random walk model in a forecasting exercise. George B. Tawadros Royal Melbourne Institute of Technology

Moose

About the author

Imad Moosa is Professor of Finance at the Royal Melbourne Institute of Technology (RMIT), Australia. He has also held previous appointments at Monash and La Trobe Universities, Australia, and the University of Sheffield, UK. Prior to working in academia he held professional positions at the International Monetary Fund and in investment banking. He has published 16 books and over 200 papers.

Kelly Burns is a Research Fellow at Curtin Graduate School of Business, Australia. She has held previous appointments at the Australian Bureau of Statistics, the Department of Premier and Cabinet, the Department of Justice, and the Colonial First State. In 2002, she graduated from La Trobe University, Australia, and was the recipient of the Dean's Medal for Economics.