Are markets rational? an interview with Roman Frydman, whose book with Michael D. Goldberg, Imperfect Knowledge Economics: Exchange Rates and Risk, was recently published by Princeton University Press

International Economy, The,  Spring, 2008  

Tags: knowledge, Princeton University

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TIE: Your book leads an assault against the rational expectations school of economics. What is wrong with rational expectations?

Frydman: To put it simply, despite its name, Rational Expectations Hypothesis (REH) has nothing to do with rational forecasting.

The basic problem with REH models can be traced back to the widespread belief among contemporary economists that economic models can't be considered scientific unless, except for a purely random error term, they generate exact predictions of outcomes. Obviously, forecasts of asset prices drive outcomes in financial markets. But if we acknowledge this, and yet insist on exact predictions, we must come up with an exact model of how market participants think about the future. That's a tall order. And REH goes even further. It supposes that market participants forecast according to the exact model that economist themselves create.

This is even odder than it sounds. After all, economists' bread and butter is the search for alternative models. But if a particular economist's model were to capture rational forecasting, the use of other models would be irrational,

Interestingly, John Muth, who proposed REH in the early 1960s, was well aware of the danger that the term "rational expectations" might suggest some notion of individual rationality. But, despite his warning, REH is commonly viewed as the way to model rational forecasting. As such, REH is not just of academic interest. It is widely used as the centerpiece of sophisticated models to price derivative products and risk in financial markets.

TIE: So is not the failure of sophisticated finance models the telltale that RICH is seriously flawed?

Frydman: Spot on. We should have expected REH models to fail in financial markets. After all, outcomes in these markets are primarily driven by forecasts, and REH is a particularly poor forecasting model. Many of our colleagues cite REH models' failures as evidence that markets are populated by irrational traders. In fact, rational individuals in real-world markets do not follow pre-existing rules and procedures, let alone exact forecasting rules implied by economists' models. So the failures of REH models do not prove that markets are irrational. They simply show that economists have a wrong model of rationality. Neither REH models nor, for that matter, any other quantitative model, can capture exactly how profit-seeking market participants forecast the future.

TIE: You argue that economic forecasting is not a science. The forecaster is more like an entrepreneur who relies on intuition and judgment. Where do quantitative methods fit in?

Frydman: Quantitative models have a place in the forecaster's toolbox, but the choice among them and how they are used must involve subjective judgments. To begin with, quantitative models contain a number of unknowns that must be estimated. But, because an economy's structure changes, it is unclear how much past data a forecaster should use to come up with statistical estimates of the unknowns in his model. Even the most sophisticated statistical techniques would not automatically pinpoint when the last structural break had occurred. Of course, the choice among various alternative models also requires subjective judgments. Unsurprisingly, even when it comes to the past, let alone forecasting the future, interpretations vary among individuals depending on their personal knowledge, experience, and intuitions.

Consider the problem of forecasting exchange rates. Many market participants no doubt use quantitative models to form exact forecasts of the future exchange rate, for example, that in a week a euro will cost $1.50. After all, currency traders must decide on their market position at each point in time. But, although they may base their trading on exact predictions, they do not arrive at such predictions by relying solely on quantitative models, much less the same model in every time period. Instead, they often combine quantitative models with their own insights concerning other traders' behavior, the historical record on exchange rate fluctuations, and their evaluation of the impact of past and future decisions by policy officials. And, because they act on the basis of different experiences, interpretations of the past, and intuitions about the future, they adopt different strategies for forecasting exchange rates.

TIE: So how should economists operate in a world of imperfect knowledge?

Frydman: Economists are trained early on to believe that models that do not generate exact predictions are not worthy of consideration. But the opposite is true. To be useful, economic models must be consistent with the basic fact that participants hold diverse views about the future. How this diversity translates into prices over time must be left for the markets to determine. No mathematical model can hope to mimic exactly what markets do.

For example, euro bulls and bears have diametrically opposed forecasts. Despite drastic differences in how they form their forecasts, however, the ways in which they revise their forecasting strategies may share certain qualitative features. Imperfect Knowledge Economics (IKE) makes use of such regularities and shows how they help us understand the tendency for the exchange rate to move away from parity in some periods of time and revert back to this benchmark in others. Such qualitative predictions are admittedly less ambitious than aiming for exact predictions. But our book shows that economists can learn more about markets if we ask less of our models.