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Exchange
Rate Models
All the news
that fits
It has often been
claimed that the best predictor of next period's spot exchange rate is
the current spot rate. Researchers have found that such a random walk
model outperforms 'structural' models of the exchange rate, in which the
exchange rate is explained by other economic variables, even when the
structural forecasts are performed using actual (rather than expected)
future values of the explanatory variables.
In recent years economic theory has emphasized the distinction between
anticipated and unanticipated movements in economic variables. The
exchange rate, for example, can be viewed as an asset price which is
highly sensitive to new information. In Discussion Paper No. 188,
Research Fellow Christian Wolff applies this distinction to
structural exchange rate models, emphasizing the relationship between
unanticipated movements (or 'innovations') in the exchange rate and
innovations in the model's explanatory variables.
Wolff begins with a 'structural' model in which the spot exchange rate
depends on the expected change in the exchange rate and on a set of
explanatory variables which reflect market fundamentals, such as foreign
and domestic money supplies, real income, interest rates and inflation
rates. The expected change in the exchange rate is included because
domestic and foreign residents may be induced to move into or out of the
currency as they expect its value to rise or fall.
This model can be rewritten so that the innovation in the spot
rate depends on the innovations in the explanatory variables.
Wolff estimates these (unobservable) innovations in the explanatory
variables using a system of vector autoregressions (VARS), in which each
explanatory variable is regressed on lagged values of itself and the
other explanatory variables. The innovations, or 'news', are the
residuals from these regressions. Wolff calculates the innovations for
the expected change in the exchange rate using the current spot rate as
a proxy for the expected future spot rate. This assumes that changes in
the spot rate are entirely unpredictable, but Wolff argues that this is
borne out by the empirical experience of floating rates.
Wolff then estimates the 'news' model by regressing the spot exchange
rate on its own lagged value and the innovations in the explanatory
variables, using monthly data from the period 1973- 84. He finds that
for the dollar/yen and dollar/sterling exchange rates, forecasts from
the structural 'news' model compare favourably with those from a random
walk model for forecast horizons of up to six or twelve months, although
the structural model performs less well compared to the random walk as
the forecast horizon recedes.
Wolff notes that his results may be sensitive to the method used to
calculate the innovations. But adding other variables that might be
thought to convey news about the US economy did not improve the
forecasts. Wolff also notes that estimation of the news model depends on
the validity of the empirical measures of news that are employed:
information subsequently available to the researcher may not have been
contemporaneously observable by the market.
Nevertheless, Wolff concludes from these results that structural models
of exchange rate determination have failed to outperform the random walk
in previous research because these models were not properly tested in a
news framework.
Exchange Rates, Innovations and Forecasting
Christian Wolff
Discussion
Paper No. 188, July 1987 (ATE)
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