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A Note on Investor Happiness and the Predictability of Realized Volatility of Gold

Author

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  • Matteo Bonato

    (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa and 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.B. 700822, 22008 Hamburg, Germany)

Abstract

We apply the heterogeneous autoregressive realized volatility (HAR-RV) model to examine the importance of investor happiness in predicting the daily realized volatility of gold returns. We estimate daily realized volatility by employing intraday data providing both in-sample and out-of sample predictions. Our in-sample results reveal that realized volatility is negatively linked to investor happiness. Moreover, our out-of-sample results show that extending the HAR-RV model to include investor happiness significantly improves the accuracy of forecasts of realized volatility at short- and medium-run forecast horizons.

Suggested Citation

  • Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "A Note on Investor Happiness and the Predictability of Realized Volatility of Gold," Working Papers 202004, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202004
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    Cited by:

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    2. Salisu, Afees A. & Gupta, Rangan & Karmakar, Sayar & Das, Sonali, 2022. "Forecasting output growth of advanced economies over eight centuries: The role of gold market volatility as a proxy of global uncertainty," Resources Policy, Elsevier, vol. 75(C).
    3. Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Energies, MDPI, vol. 14(23), pages 1-18, December.
    5. 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.
    6. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    7. Sheng, Xin & Kim, Won Joong & Gupta, Rangan & Ji, Qiang, 2023. "The impacts of oil price volatility on financial stress: Is the COVID-19 period different?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 520-532.
    8. Sami Ben Jabeur & Rabeh Khalfaoui & Wissal Ben Arfi, 2021. "The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning," Post-Print hal-03797577, HAL.
    9. Li, Sufang & Xu, Qiufan & Lv, Yixue & Yuan, Di, 2022. "Public attention, oil and gold markets during the COVID-19: Evidence from time-frequency analysis," Resources Policy, Elsevier, vol. 78(C).
    10. Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian & Shahzad, Syed Jawad Hussain, 2020. "The predictive power of oil price shocks on realized volatility of oil: A note," Resources Policy, Elsevier, vol. 69(C).
    11. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    12. Gupta, Rangan & Pierdzioch, Christian, 2022. "Climate risks and forecastability of the realized volatility of gold and other metal prices," Resources Policy, Elsevier, vol. 77(C).
    13. Văn, Lê & Bảo, Nguyễn Khắc Quốc, 2022. "The relationship between global stock and precious metals under Covid-19 and happiness perspectives," Resources Policy, Elsevier, vol. 77(C).
    14. Yan, Juan & Haroon, Muhammad, 2023. "Financing efficiency in natural resource markets mobilizing private and public capital for a green recovery," Resources Policy, Elsevier, vol. 85(PB).
    15. Rangan Gupta & Christian Pierdzioch, 2023. "Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    16. Xolani Sibande & Rangan Gupta & Riza Demirer & Elie Bouri, 2023. "Investor Sentiment and (Anti) Herding in the Currency Market: Evidence from Twitter Feed Data," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 56-72, January.
    17. Guliyev, Hasraddin & Mustafayev, Eldayag, 2022. "Predicting the changes in the WTI crude oil price dynamics using machine learning models," Resources Policy, Elsevier, vol. 77(C).
    18. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    19. Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers," Energies, MDPI, vol. 14(14), pages 1-15, July.
    20. Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022. "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, vol. 114(C).
    21. Elie Bouri & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Infectious Diseases, Market Uncertainty and Oil Market Volatility," Energies, MDPI, vol. 13(16), pages 1-8, August.
    22. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    23. Chu, Xiaojun & Wan, Xinmin & Qiu, Jianying, 2023. "The relative importance of overnight sentiment versus trading-hour sentiment in volatility forecasting," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).

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    More about this item

    Keywords

    Investor Happiness; Gold; 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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