International Macroeconomics and Finance: Theory and Empirical Methods Phần 6 docx

6.4. APPARENT VIOLATIONS OF RATIONALITY 183

6.4 Apparent Violations of Rationality

Weíve seen that there are important dimensions of the data that the Lu￾cas model with CRRA utility cannot explain.8 What other approaches

have been taken to explain deviations from uncovered interest parity?

This section covers the peso problem approach and the noise trader

paradigm. Both approaches predict that market participants make sys￾tematic forecast errors. In the peso problem approach, agents have ra￾tional expectations but donít know the true economic environment with

certainty. In the noise trading approach, some agents are irrational.

Before tackling these issues, we want to have some evidence that

market participants actually do make systematic forecast errors. So we

first look at a line of research that studies the properties of exchange

rate forecasts compiled by surveys of actual foreign exchange market

participants. The subjective expectations of market participants are

key to any theory in international finance. The rational expectations

assumption conveniently allows the economic analyst to model these

subjective expectations without having to collect data on peopleís ex￾pectations per se. If the rational expectations assumption is wrong, its

violation may be the reason that underlies asset-pricing anomalies such

as the deviation from uncovered interest parity.

7Backus, Gregory, and Telmer [4] investigate the lower volatility bound (6.28)

implied by data on the U.S. dollar prices of the Canadian-dollar, the deutsche￾mark, the French-franc, the pound, and the yen. They compute the bound for an

investor who chases positive expected profits by defining forward exchange payoffs

on currency i as Iit(Fi,t − Si,t+1)/Si,t where Iit = 1 if Et(fi,t − si,t+1) > 0 and

Iit = 0 otherwise. The bound computed in the text does not make this adjustment

because it is not a prediction of the Lucas model where investors may be willing

to take a position that earns expected negative profit if it provides consumption

insurance. Using the indicator adjustment on returns lowers the volatility bound

making it more difficult for the asset pricing model to match this quarterly data

set.

8The failure of the model to generate sufficiently variable risk premiums to ex￾plain the data cannot be blamed on the CRRA utility function. Bekaert [9] obtains

similar results with utility specifications where consumption exhibits durability and

when utility displays ëhabit persistenceí.

184CHAPTER 6. FOREIGN EXCHANGE MARKET EFFICIENCY

Properties of Survey Expectations

Instead of modeling the subjective expectations of market participants

as mathematical conditional expectations, why not just ask people what

they think? One line of research has used surveys of exchange rate fore￾casts by market participants to investigate the forward premium bias

(deviation from UIP). Froot and Frankel [65], study surveys conducted

by the Economistís Financial Report from 6/81ó12/85, Money Market

Services from 1/83ó10/84, and American Express Banking Corpora￾tion from 1/76ó7/85, Frankel and Chinn [58] employ a survey compiled

monthly by Currency Forecastersí Digest from 2/88 through 2/91, and

Cavaglia et. al. [23] analyze forecasts on 10 USD bilateral rates and 8

deutschemark bilateral rates surveyed by Business International Cor￾poration from 1/86 to 12/90. The survey respondents were asked to

provide forecasts at horizons of 3, 6, and 12 months into the future.

The salient properties of the survey expectations are captured in

two regressions. Let àse (117)⇒ t+1 be the median of the survey forecast of the

log spot exchange rate st+1 reported at date t. The first equation is the

regression of the survey forecast error on the forward premium

∆sàe

t+1 − ∆st+1 = α1 + β1(ft − st) + ²1t+1. (6.29)

If survey respondents have rational expectations, the survey forecast er￾ror realized at date t+1 will be uncorrelated with any publicly available

at time t and the slope coefficient β1 in (6.29) will be zero.

The second regression is the counterpart to Famaís decomposition

and measures the weight that market participants attach to the forward

premium in their forecasts of the future depreciation

∆sàe

t+1 = α2 + β2(ft − st) + ²2,t+1. (6.30)

Survey respondents perceive there to be a risk premium to the extent

that β2 deviates from one. That is because if a risk premium exists,

it will be impounded in the regression error and through the omitted

variables bias will cause β2 to deviate from 1.

Table 6.4 reports selected estimation results drawn from the litera￾ture. Two main points can be drawn from the table.

1. The survey forecast regressions generally yield estimates of β1

that are significantly different from zero which provides evidence

6.4. APPARENT VIOLATIONS OF RATIONALITY 185

Table 6.4: Empirical Estimates from Studies of Survey Forecasts

Data Set

Economist MMS AMEX CFD BICóUSD BICóDEM

Horizon: 3-months

β1 2.513 6.073 ñ ñ 5.971 1.930

t(β1 = 1) 1.945 2.596 ñ ñ 1.921 -0.452

t(β2 = 1) 1.304 -0.182 ñ 0.423 1.930 0.959

t-test 1.188 -2.753 ñ -2.842 5.226 -1.452

Horizon: 6-months

β1 2.986 ñ 3.635 ñ 5.347 1.841

t(β1 = 1) 1.870 ñ 2.705 ñ 2.327 -0.422

β2 1.033 ñ 1.216 ñ 1.222 0.812

t(β2 = 1) 0.192 ñ 1.038 ñ 1.461 -4.325

Horizon: 12-months

β1 0.517 ñ 3.108 ñ 5.601 1.706

t(β1 = 1) 0.421 ñ 2.400 ñ 3.416 0.832

β2 0.929 ñ 0.877 1.055 1.046 0.502

t(β2 = 1) -0.476 ñ -0.446 0.297 0.532 -6.594

Notes: Estimates from the Economist, Money Market Services, and American Ex￾press surveys are from Froot and Frankel [65]. Estimates from the Currency

Forecastersí Digest survey are from Frankel and Chinn [58], and estimates from the

Business International Corporation (BIC) survey from Cavaglia et. al. [23]. BICó

USD is the average of individual estimates for 10 dollar exchange rates. BICóDEM

is the average over 8 deutschemark exchange rates.