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The difficulties of modeling and forecasting foreign exchange rates have been well known since early 1970's . One of the possible explanations for our inability to provide an accurate model is the structural changes over time , especially in emerging markets . The traditional regression techniques that assume constant parameters are incapable of capturing the changing dynamics over time . Consequently , most foreign exchange regression models are ineffective .
To better capture fundamental structural changes in a market , a moving block regression technique is recommended by the author . The moving block regression procedure utilizes sub -sample information , rather than the prevailing whole sample data that intends to increase regression efficiency with more observations . To find out the loss or gain of forecast efficiency , a Monte Carlo study is carried out under several different scenarios : data in compliance with the classic OLS assumptions , data with heteroscedasticity , data with autocorrelation , model with a missing variable , model with changing regression coefficients , and data with nonlinear relationships .
Simulation results show a trivial loss of out -of -sample forecast efficiency with the moving block regressions and a small trade -off in the presence of minor violations of the assumptions . However , there is a clear dominance of the moving block regressions over the traditional whole sample regressions in terms of forecasting efficiency when the violations of assumptions are serious , such as missing variable , changing coefficients , or nonlinear relations .
Then the moving block regressions are applied to exchange rates of six currencies against the U .S . dollar . The comparisons of forecasting residuals , both in -sample and out -of -sample , show a strong support for the moving block techniques , indicating the inevitable violations of regression assumptions in foreign exchange markets . |
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