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On economic uncertainty, stock market predictability and nonlinear spillover effects

Stelios Bekiros, Rangan Gupta () and Clement Kyei

The North American Journal of Economics and Finance, 2016, vol. 36, issue C, 184-191

Abstract: This paper uses a k-th order nonparametric Granger causality test to analyze whether firm-level, 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, as linear modeling leads to misspecifications thus the results cannot be considered reliable. The nonparametric test reveals that in fact no predictability can be observed for the various measures of uncertainty i.e., firm-level, macroeconomic and economic policy uncertainty, vis-à-vis real stock returns. In turn, a profound causal predictability is demonstrated for the volatility series, with the exception of firm-level uncertainty. Overall our results not only emphasize the role of economic and firm-level uncertainty measures in predicting the 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.

Keywords: Economic policy; Stock markets; Nonlinear causality (search for similar items in EconPapers)
Date: 2016
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Working Paper: On Economic Uncertainty, Stock Market Predictability and Nonlinear Spillover Effects (2015)
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DOI: 10.1016/j.najef.2016.01.003

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