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Investor Happiness and Predictability of the Realized Volatility of Oil Price

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  • Matteo Bonato

    (Department of Economics and Econometrics, University of Johannesburg, P.O. Box 524 Auckland Park, Johannesburg, South Africa
    IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France)

  • Konstantinos Gkillas

    (Department of Business Administration, University of Patras, University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria 0002, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O. Box 700822, 22008 Hamburg, Germany)

Abstract

We use the the heterogeneous autoregressive realized volatility (HAR-RV) model to analyze both in sample and out-of-sample whether a measure of investor happiness predicts the daily realized volatility of oil-price returns, where we use high-frequency intraday data to measure realized volatility. Full-sample estimates reveal that realized volatility is significantly negatively linked to investor happiness at a short forecast horizon. Similarly, out-of-sample results indicate that investor happiness significantly improves the accuracy of forecasts of realized volatility at a short forecast horizon. Results for a medium and a long forecast horizon are insignificant. We argue that our results shed light on the role played by speculation in oil products and the potential function of oil-related products as a hedge against risks in traditional financial assets.

Suggested Citation

  • Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, Open Access Journal, vol. 12(10), pages 1-11, May.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4309-:d:362539
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    Cited by:

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    2. Afees A. Salisu & Rangan Gupta & Riza Demirer, 2021. "Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model," Working Papers 202121, University of Pretoria, Department of Economics.
    3. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
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    5. Jiawen Luo & Riza Demirer & Rangan Gupta & Qiang Ji, 2021. "Forecasting Oil and Gold Volatilities with Sentiment Indicators Under Structural Breaks," Working Papers 202130, University of Pretoria, Department of Economics.

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    More about this item

    Keywords

    investor happiness; oil market; realized volatility; forecasting;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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