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Does country risks predict stock returns and volatility? Evidence from a nonparametric approach

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  • Suleman, Tahir
  • Gupta, Rangan
  • Balcilar, Mehmet

Abstract

We use the k-th order nonparametric causality test at monthly frequency over the period of 1984:1–2015:12 to analyze whether aggregate country risk, and its components (economic, financial and political) can predict movements in stock returns and volatility of eighty-three developed and developing economies. The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the weak evidence of causality obtained under the standard Granger tests cannot be relied upon. When we apply the nonparametric test, we find that, while there is no evidence of predictability of squared stock returns barring one case, at times, there are nearly 50 percent of the countries where the aggregate risks and its components tend to predict stock returns and realized volatility.

Suggested Citation

  • Suleman, Tahir & Gupta, Rangan & Balcilar, Mehmet, 2017. "Does country risks predict stock returns and volatility? Evidence from a nonparametric approach," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1173-1195.
  • Handle: RePEc:eee:riibaf:v:42:y:2017:i:c:p:1173-1195
    DOI: 10.1016/j.ribaf.2017.07.055
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    1. Mehmet Balcilar & Stelios Bekiros & Rangan Gupta, 2017. "The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method," Empirical Economics, Springer, vol. 53(3), pages 879-889, November.
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    Keywords

    Country risks; Returns; Volatility; Nonparametric higher-order causality;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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