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On Economic Uncertainty, Stock Market Predictability and Nonlinear Spillover Effects

Author

Listed:
  • Stelios Bekiros

    () (IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

  • Clement Kyei

    () (Department of Economics, University of Pretoria)

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, 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.

Suggested Citation

  • Stelios Bekiros & Rangan Gupta & Clement Kyei, 2015. "On Economic Uncertainty, Stock Market Predictability and Nonlinear Spillover Effects," Working Papers 201508, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201508
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    References listed on IDEAS

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    1. Jones, Paul M. & Olson, Eric, 2013. "The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model," Economics Letters, Elsevier, vol. 118(1), pages 33-37.
    2. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    3. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    4. Kang, Wensheng & Ratti, Ronald A., 2013. "Oil shocks, policy uncertainty and stock market return," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 305-318.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    6. Gupta, Rangan & Hammoudeh, Shawkat & Modise, Mampho P. & Nguyen, Duc Khuong, 2014. "Can economic uncertainty, financial stress and consumer sentiments predict U.S. equity premium?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 367-378.
    7. Chang, Tsangyao & Chen, Wen-Yi & Gupta, Rangan & Nguyen, Duc Khuong, 2015. "Are stock prices related to the political uncertainty index in OECD countries? Evidence from the bootstrap panel causality test," Economic Systems, Elsevier, vol. 39(2), pages 288-300.
    8. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    9. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, Elsevier.
    10. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    11. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
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    Cited by:

    1. repec:eee:jmacro:v:55:y:2018:i:c:p:128-145 is not listed on IDEAS
    2. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2017. "Oil price shocks and policy uncertainty: New evidence on the effects of US and non-US oil production," Energy Economics, Elsevier, vol. 66(C), pages 536-546.
    3. Bekiros, Stelios & Gupta, Rangan & Majumdar, Anandamayee, 2016. "Incorporating economic policy uncertainty in US equity premium models: A nonlinear predictability analysis," Finance Research Letters, Elsevier, vol. 18(C), pages 291-296.
    4. repec:eee:riibaf:v:45:y:2018:i:c:p:293-306 is not listed on IDEAS
    5. repec:eee:touman:v:63:y:2017:i:c:p:3-9 is not listed on IDEAS
    6. Christou, Christina & Cunado, Juncal & Gupta, Rangan & Hassapis, Christis, 2017. "Economic policy uncertainty and stock market returns in PacificRim countries: Evidence based on a Bayesian panel VAR model," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 92-102.
    7. repec:eee:phsmap:v:493:y:2018:i:c:p:107-115 is not listed on IDEAS
    8. 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.
    9. repec:eee:ecmode:v:69:y:2018:i:c:p:301-312 is not listed on IDEAS
    10. repec:eee:phsmap:v:490:y:2018:i:c:p:203-211 is not listed on IDEAS
    11. repec:eee:jrpoli:v:55:y:2018:i:c:p:244-252 is not listed on IDEAS
    12. Kang, Wensheng & Perez de Gracia, Fernando & Ratti, Ronald A., 2017. "Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 344-359.
    13. Chuliá, Helena & Gupta, Rangan & Uribe, Jorge M. & Wohar, Mark E., 2017. "Impact of US uncertainties on emerging and mature markets: Evidence from a quantile-vector autoregressive approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 178-191.
    14. Nicholas Apergis & Matteo Bonato & Rangan Gupta & Clement Kyei, 2016. "Does Geopolitical Risks Predict Stock Returns and Volatility of Leading Defense Companies? Evidence from a Nonparametric Approach," Working Papers 201671, University of Pretoria, Department of Economics.
    15. repec:eee:eneeco:v:68:y:2017:i:c:p:1-18 is not listed on IDEAS
    16. Mehmet Balcilar & Deven Bathia & Riza Demirer & Rangan Gupta, 2017. "Credit Ratings and Predictability of Stock Returns and Volatility of the BRICS and the PIIGS: Evidence from a Nonparametric Causality-in-Quantiles Approach," Working Papers 201719, University of Pretoria, Department of Economics.
    17. Duca, John V. & Saving, Jason L., 2018. "What drives economic policy uncertainty in the long and short runs: European and U.S. evidence over several decades," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 128-145.
    18. repec:wsi:afexxx:v:12:y:2017:i:04:n:s2010495217500166 is not listed on IDEAS

    More about this item

    Keywords

    Economic policy; stock markets; nonlinear causality;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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