August 01 2004 Inference on Predictability of Foreign Exchange Rates via Generalized Spectrum and Nonlinear Time Series Models Yongmiao Hong, Yongmiao Hong Search for other works by this author on: This Site Google Scholar Tae-Hwy Lee Tae-Hwy Lee Search for other works by this author on: This Site Google Scholar Author and Article Information Yongmiao Hong Tae-Hwy Lee Online Issn: 1530-9142 Print Issn: 0034-6535 © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology2004 The Review of Economics and Statistics (2004) 86 (3): 840. https://doi.org/10.1162/0034653041811716 Cite Icon Cite Permissions Share Icon Share MailTo Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Yongmiao Hong, Tae-Hwy Lee; Inference on Predictability of Foreign Exchange Rates via Generalized Spectrum and Nonlinear Time Series Models. The Review of Economics and Statistics 2004; 86 (3): 840. doi: https://doi.org/10.1162/0034653041811716 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThe Review of Economics and Statistics Search Advanced Search This content is only available as a PDF. © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology2004 Article PDF first page preview Close Modal You do not currently have access to this content.

It is often documented, based on autocorrelation, variance ratio, and power spectrum, that exchange rates approximately follow a martingale process. Because these data check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insignificant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange rate changes, and if so, whether nonlinear time series models admissible in the gap can outperform the martingale model in out-of-sample forecasts. Applying the generalized spectral tests of Hong to five major currencies, we find that the changes of exchange rates are often serially uncorrelated, but there exists strong nonlinearity in conditional mean, in addition to the well-known volatility clustering. To forecast the conditional mean, we consider the linear autoregressive, autoregressive polynomial, artificial neural network, and functional-coefficient models, as well as their combination. The functional coefficient model allows the autoregressive coefficients to depend on investment positions via a moving-average technical trading rule. We evaluate out-of-sample forecasts of these models relative to the martingale model, using four criteria—the mean squared forecast error, the mean absolute forecast error, the mean forecast trading return, and the mean correct forecast direction. White's reality check method is used to avoid data-snooping bias. It is found that suitable nonlinear models, particularly in combination, do have superior predictive ability over the martingale model for some currencies in terms of certain forecast evaluation criteria.