Does U.S. News Impact Asian Emerging Markets? Evidence from Nonparametric Causality-in-Quantiles Test

The objective of this paper is to employ the recently proposed nonparametric causality-in-quantiles test to analyse whether US news on inflation and unemployment causes returns and volatility of seven emerging Asian stock markets (India, Indonesia, South Korea, Philippines, Singapore, Taiwan and Thailand), based on daily data over the period of November 1st, 1994 to June 24th, 2014. 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 relationship between stock returns and the US news variables, implying that the Granger causality tests based on a linear framework, which in any event showed no evidence of predictability, is likely to suffer from misspecification. Therefore, relying on the robust k-th order nonparametric causality-in-quantiles test, we find that there is evidence that US news affect returns and/or volatility of six out of the seven stock markets, with these effects clustered around the tails of the conditional distribution of returns and volatility. Our results imply that inflation and unemployment surprises in the US affects the Asian stock markets when they are either in bear or bull modes. In general, our results highlight the importance of modeling nonlinearity and studying entire conditional distributions of stock returns and volatility to draw correct inferences.