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-in-quantiles 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 (1 st 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 causalityin-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.

We use the k-th order nonparametric causality test at monthly frequency over the period of 1985:1 to 2016:06 to analyze whether geopolitical risks can predict movements in stock returns and volatility of twenty-four global defense firms. The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the mild evidence of causality obtained under the standard Granger tests cannot be relied upon. When we apply the nonparametric test, we find that there is no evidence of predictability of stock returns of these defense companies emanating from the geopolitical risk measure. However, the geopolitical risk index does predict realized volatility in 50 percent of the companies. Our results indicate that while global geopolitical events over a period of time is less likely to predict returns, such global risks are more inclined in affecting future risk profile of defense firms.

This paper uses a k-th order nonparametric Granger causality test to analyze whether firmlevel, economic policy and macroeconomic uncertainty indicators predict movements in real stock returns and their volatility. Linear Granger causality tests show that whilst economic policy and macroeconomic uncertainty indices can predict stock returns, firm-level uncertainty measures possess no predictability. However, given the existence of structural breaks and inherent nonlinearities in the series, we employ a nonparametric causality methodology, since the linear model is misspecified and the results emanating from it cannot be considered reliable. The nonparametric test reveals that, in fact, there is in general no predictability from the various measures of uncertainties, i.e., firm-level, macroeconomic, and economic policy uncertainty, for real stock returns. In turn, the predictability is concentrated in the volatility of real stock returns, except under the case of firm-level uncertainty. Thus, our results not only emphasize the role of economic and firm-level uncertainty measures in predicting volatility of stock returns, but also presage against using linear models which are likely to suffer from misspecification in the presence of parameter instability and nonlinear spillover effects.JEL Codes: C32, C58, G10, G17

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S BW and S PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only S PLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality-in-quantiles model of Balcilar et al., (forthcoming), in fact, both S BW and S PLS can predict stock returns and its volatility, with S PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.

Highlights• We analyze the predictability of stock return and its volatility of Hong Kong, Malaysia and South Korea • We base on the measure of domestic and global economic policy uncertainties (EPU) • Linear Granger causality tests fail to find evidence of predictability from EPU • A nonparametric causality-in-quantiles test, however, finds strong evidence of causality • Nonparametric test is found to be more robust relative to the standard linear causality test. AbstractThis paper analyzes whether we can predict stock return and its volatility of Hong Kong, Malaysia and South Korea based on measures of domestic and global (China, the European Area, Japan, and the US) economic policy uncertainties (EPU). While, linear Granger causality tests fail to find evidence of predictability, barring the case of South Korean EPU predicting its own stock returns, when we use a nonparametric causality-in-quantiles test, strong evidence of causality is detected from the EPUs for stock return volatility of Malaysia, and both returns and volatility at certain parts of the conditional distributions for South Korea. There is no evidence of predictability from domestic and global EPUs for return and volatility of the Hong Kong stock market. Given the statistical evidence of nonlinearity in our data set, we consider the results from the nonparametric test as more robust relative to the standard linear causality test. * We would like to thank anonymous referees for many helpful comments. However, any remaining errors are solely ours.