Why Exchange Rate Models Finally Work

In 1983, Richard Meese and Kenneth Rogoff published a paper that embarrassed an entire field. They compared structural exchange rate models — models built on interest rates, money supply, income differentials — against a simple random walk: the prediction that tomorrow's exchange rate will equal today's. The structural models lost. At every horizon, the random walk was as good or better.

This result, known as the Meese-Rogoff puzzle, held for almost two decades. FX models had no edge. The implication was uncomfortable: if exchange rates cannot be predicted by macroeconomic fundamentals, what exactly is macro-driven FX analysis doing?

What Changed

The puzzle did not disappear because economists built better models. It disappeared because the world changed — specifically, because central banks changed.

The 1980s and early 1990s were characterized by inconsistent inflation regimes. Central banks in most developed countries were still establishing credibility, inflation targets were either absent or routinely breached, and the relationship between monetary policy and the price level was noisy. In that environment, rate differentials were weak signals: you could not trust that a rate gap would persist, or that inflation would behave in the way the models assumed.

The credible inflation targeting era that followed — adopted by New Zealand in 1990, the United Kingdom in 1992, Sweden and Canada in 1993, and gradually by most advanced economies over the next decade — changed the regime. When a central bank commits credibly to a 2% target and actually hits it over multiple cycles, the relationship between policy rates and inflation becomes predictable. And when that relationship is predictable, rate differentials become a reliable signal of currency direction.

The Engel-Wu Finding

Charles Engel and Steve Wu (2021) tested exchange rate models across G10 pairs using data that extends well into the credible inflation targeting era. Their finding: at the 12-month horizon, structural models outperform the random walk with statistical reliability. The out-of-sample R-squared values are modest but robust, and the directional accuracy is enough to generate economically significant returns.

The factors that work: interest rate differentials, yield curve slope, and inflation expectations differentials. Notably, these are exactly the signals that post-1990s central bank behavior made more stable and predictable. The models did not get smarter. The underlying relationships they were trying to capture became more stable.

What This Means for the Framework

The 4xForecaster framework is built for the horizon where models work: the medium-term directional bias. Daily updates do not mean daily trading signals. They mean a continuously updated assessment of where structural forces are pointing, for positions held over weeks to months.

When the rate differential layer of the framework shows a widening spread favoring the dollar, that signal has documented out-of-sample predictive power — not because markets are inefficient, but because the macro regime since the 1990s has been one where rate differentials reliably transmit to exchange rates over a 6–12 month lag.

The puzzle is solved. The models work. The question is now which signals to weight and how to read them in sequence. That is what the framework article covers in full, and what the rate differentials article unpacks for the most foundational signal layer.