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The Predictive Power of Industrial Electricity Usage Revisited: Evidence from Nonparametric Causality Tests

Author

Listed:
  • Matteo Bonato

    (Department of Economics and Econometrics, University of Johannesburg, South Africa)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, USA)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, South Africa)

Abstract

Da et al. (2015b) report that the industrial electricity usage growth rate carries predictive ability over stock returns up to one year. Using the recently developed nonparametric causality test by Nishiyama et al. (2011), we show that the predictive power of industrial electricity usage can be explained by an “industry effect” that is transmitted via the volatility channel. We argue that the countercyclical premium associated with industrial electricity usage growth is driven by the industry components that drive stock reversals, thus resulting in the negative relationship between today’s industrial electricity usage and stock returns in the future. The findings are in line with the notion that the returns on industry portfolios are informative about macroeconomic fundamentals and suggest that the informational value of industrial electricity usage as a business cycle variable may be an artifact of return reversals driven by past industry performance

Suggested Citation

  • Matteo Bonato & Riza Demirer & Rangan Gupta, 2016. "The Predictive Power of Industrial Electricity Usage Revisited: Evidence from Nonparametric Causality Tests," Working Papers 201679, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201679
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    References listed on IDEAS

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    Cited by:

    1. Karmakar, Sayar & Demirer, Riza & Gupta, Rangan, 2021. "Bitcoin mining activity and volatility dynamics in the power market," Economics Letters, Elsevier, vol. 209(C).

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    More about this item

    Keywords

    Asset Returns; Industry; Realized Volatility; Nonlinear Causality;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets

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