Clement Kyei

Does Geopolitical Risks Predict Stock Returns and Volatility of Leading Defense Companies? Evidence from a Nonparametric Approach

Abstract 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 24 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% 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.

Predictability of sustainable investments and the role of uncertainty: evidence from a non-parametric causality-in-quantiles test

ABSTRACT In this article, we examine sustainable investments returns predictability based on the U.S. Dow Jones Sustainability Index (DJSI) and a wide set of uncertainty and financial distress indicators for the period 2002:01–2014:12. To this end, we employ a novel non-parametric causality-in-quantile approach that captures non-linearities in returns distribution. Based on our findings we conclude that the aggregate economic policy uncertainty (EPU) indicator and some components have predictive ability for real returns of the U.S. sustainable investments index. Moreover, if we split our sample to before and after the global financial crisis our results suggest that predictors carry causal information for real returns only in the after-crisis period. Finally, some marginal evidence of predictability from sovereign debt is also observed at the lower and upper ends of the conditional distribution of the real returns of sustainable investments. Our results might entail policy implications for investors and market authorities.

A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

ABSTRACT The popular sentiment-based investor index SBW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, SPLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, SBW and SPLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither SBW nor SPLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.

Predicting stock returns and volatility with investor sentiment indices : a reconsideration using a nonparametric causality-in-quantiles test

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely SBW and SPLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only SPLS 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., (2015), in fact, both SBW and SPLS can predict stock returns and its volatility, with SPLS being a relatively stronger predictor of excess returns during bear and bull regimes, and SBW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.

The relationship between oil and agricultural commodity prices in south africa: a quantile causality approach

ABSTRACT:The increase in agricultural commodity prices in the recent past has renewed interest in ascertaining the factors that drive agricultural commodity prices. Though a number of factors are possible, higher oil prices are thought to be the major factor driving up agricultural commodity prices, especially as the demand for biofuels production increases. However, empirical evidence of this relationship remain ambiguous and largely depends on the method used. For this reason, there is a need to examine the relationship in the context of different methodologies. Furthermore, information on how South African commodity prices respond to world oil price shocks is less certain. A good understanding of the factors that drive local commodity prices will assist in making sound agricultural policies. In this paper, the Granger causality test is applied to the mean to investigate the causality between oil prices and agricultural (soya beans, wheat, sunflower and corn) commodity prices in South Africa. Daily data spanning from 19 April 2005 to 31 July 2014 is used for Brent crude oil, corn, wheat, sunflower and soya beans prices. Agricultural commodity prices were obtained from the Johannesburg Stock Exchange, and the series of Brent crude oil prices from the U.S. Department of Energy. Results from the linear causality test indicate that oil prices do not influence agricultural commodity prices. However, owing to structural breaks and nonlinear dependence between the variables of study, these results are misleading. As an alternative, the nonparametric test of Granger causality in quantiles, as proposed by Jeong, Härdle and Song (2012) is used. Through this test, we not only look at causality beyond the mean estimates but also accounts for the structural breaks and nonlinear dependence present in the data. Additionally, the method becomes more instructive in the case where the distribution of variables has fat tails. The findings show that the effect of changes in oil prices on agricultural commodity prices vary across the different quantiles of the conditional distribution. The highest impact is not at the median, and the impact on the tails is lower compared to the rest of the distribution. The analysis shows that the relationship between oil prices and agricultural commodity prices depends on specific phases of the market, and therefore contradicts the neutrality hypothesis that oil prices do not cause agricultural commodity prices in South Africa. This implies that policies to stabilize domestic agricultural commodity prices must consider developments in the world oil markets.