Abstract:
Recent studies have analysed the ability of measures of uncertainty to
predict movements in macroeconomic and financial variables. The objective of
this paper is to employ the recently proposed nonparametric causality-inquantiles
test to analyse the predictability of returns and volatility of sixteen
U.S. dollar-based exchange rates (for both developed and developing countries)
over the monthly period of 1999:01–2012:03, based on information provided by
a news-based measure of relative uncertainty, i.e., the differential between
domestic and U.S. uncertainties. The causality-in-quantile approach allows us
to test for not only causality-in-mean (1st moment), but also causality that may
exist in the tails of the joint distribution of the variables. In addition, we are
also able to investigate causality-in-variance (volatility spillovers) when causality in the conditional-mean may not exist, yet higher order interdependencies
might emerge. We motivate our analysis by employing tests for nonlinearity.
These tests detect nonlinearity, as well as the existence of structural
breaks in the exchange rate returns, and in its relationship with the EPU
differential, implying that the Granger causality tests based on a linear framework
is likely to suffer from misspecification. The results of our nonparametric
causality-in-quantiles test indicate that for seven exchange rates EPU differentials
have a causal impact on the variance of exchange rate returns but not on
the returns themselves at all parts of the conditional distribution. We also find
that EPU differentials have predictive ability for both exchange rate returns as
well as the return variance over the entire conditional distribution for four
exchange rates.
