The traditional exchange rate models, which are based on some form of equilibrium value, do offer an important and useful long-term guide towards exchange rate prediction. Indeed, without these long-term signposts, currency strategists might be quite lost in seeking to predict exchange rates past one year out. As a result, in terms of their usefulness to specific types of currency market practitioner, corporations, strategic “real money” investors or “macro” hedge funds with a multi-month or even multi-year perspective would probably find them most valuable as an analytical tool. On the other hand, these traditional exchange rate models are unlikely to be of more than passing interest or use to short-term speculators or interbank dealers whose time frame is far shorter.

The apparent weaknesses of traditional exchange rate models, I would suggest, adds to this case that such an integrated currency strategy framework be adopted. It has to be said that to date, when faced with the unsatisfying results that the traditional exchange rate models have produced as far as predicting exchange rates is concerned, the economic community has for the most part either ignored these inconvenient results or declared that it is impossible to forecast short-term exchange rate moves as they are determined by the so-called “random walk” theory. Occasionally, there has been a paper, illuminating in both its honesty as well as its intellectual acumen, which has “fessed up” to both the failure of these models as predictive tools and a lack of understanding as to why that may be the case. The majority of the time, however, the reaction to the obvious question has been denial or the random walk excuse. According to the latter, since traditional exchange rate models do not appear able to predict short-term exchange rate moves, it must follow logically that such short-term exchange rate moves cannot in fact be predicted at all and must therefore follow a “random walk” path, suggesting an equal probability of appreciating or depreciating over time. Fortunately, however, recent developments in technical and capital flow analysis have achieved significantly better results in predicting exchange rates than the random walk would imply. Thus, the correct approach to analysing and predicting exchange rates would seem to be to use market-based approaches such as technical and flow analysis for short-term exchange rate moves and the traditional exchange rate models for medium- to long-term predictions. This is certainly not the whole story in trying to create an integrated framework for analysing currencies, but it forms a good start in our understanding of how we should approach exchange rates.

Building on this, going forward, it seems logical to assume that traditional exchange rate models should be modified to suit the modern structure of currency market flows. More specifically, trade flows, which form the premise behind the PPP, Balance of Payments and External Balance Approaches, were once seen as the main driver of currency market overall flow. However, nowadays, they make up only around 1–2% of the USD1.2 trillion in daily volume going through the currency market. Hence, as the overall importance of trade to total market flow has declined, so to a degree has the relevance of those exchange rate models that rely solely on shifts in trade flow patterns. Meanwhile, just as the pre-eminence of trade flows has declined, so the importance of portfolio flows has grown exponentially as barriers to capital have been lifted over the past two decades. The Portfolio Balance Approach is clearly an attempt to focus on asset markets and specifically the bond market as a driver of exchange rates, yet this model remains unsatisfactory as a predictor of exchange rates for the reasons given. In order to try to get to a better answer of exchange rate movement over the short term, we have to define the main flow drivers of exchange rates:

1. “Speculative” flow (without an underlying attached asset)
2. Equity flow
3. Fixed income flow
4. Direct investment flow
5. Trade flow

By far, speculative flow is the main driver of exchange rates over the short term. It is not sufficient to suggest that speculative flows follow a “random walk” for the simple reason that both technical and flow analysis have discovered consistent patterns in short-term exchange rates which should not exist under random walk theory. Within asset market flow, equity and fixed income flows continue to do battle for pre-eminence. For instance, from 1998 to mid-2000, net inflows to the US equity markets were a key driver of dollar strength. Equally, as the US equity market began to falter, the resulting equity outflows from the US market weighed on the US dollar. Eventually, however, these flows were more than made up for by fixed income inflows to the US fixed income markets as the Federal Reserve continued to cut interest rates to support the economy. Direct investment is also an increasingly important driver of exchange rates, both in the developed economies and in the emerging markets, as barriers to inward investment have also fallen away. In 2001, the top five performing currencies in the world against the USD were the Mexican peso, Polish zloty, Czech koruna, Hungarian forint and Peruvian sol, all of which benefited from substantial direct investment inflows which had a significant impact on their exchange rates.

The importance of all of these flow types continues to fluctuate in line with market trends. What is clear however, is that until there is a specific exchange rate model which focuses on the main flow dynamic of the currency market, namely speculative flow, it is unlikely that exchange rate models in general will be able to improve upon their current accuracy to any significant degree. In the next chapter, this is in fact what we will try and do—to build a simple exchange rate model which focuses on speculative flow. In addition, we also examine how to use “currency economics”, or the bits of economic theory that are relevant to the currency market, in a practical manner for currency forecasting, trading and investing.

January, 2003