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Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis

Author

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  • Matteo Bonato

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

  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey)

  • 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 use an international dataset on 5-minutes interval intraday data covering nine leading markets and regions to construct measures of realized volatility, realized jumps, realized skewness, and realized kurtosis of returns of international Real Estate Investment Trusts (REITs) over the daily period of September, 2008 to August, 2020. We study out-of-sample the predictive value of realized skewness and realized kurtosis for realized volatility over and above realized jumps, where we also differentiate between measures of ``good" realized volatility and ``bad" realized volatility. We find that realized skewness and realized kurtosis significantly improve forecasting performance at a daily, weekly, and monthly forecast horizon, and that their contribution to forecasting performance outweighs in terms of significance the contribution of realized jumps. Our results have important implications for investors and policymakers.

Suggested Citation

  • Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis," Working Papers 202114, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202114
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    3. Salisu, Afees A. & Gupta, Rangan & Bouri, Elie, 2023. "Testing the forecasting power of global economic conditions for the volatility of international REITs using a GARCH-MIDAS approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 303-314.
    4. Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021. "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, vol. 104(C).
    5. Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023. "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, vol. 29(14), pages 1579-1597, September.
    6. Matteo Foglia & Vasilios Plakandaras & Rangan Gupta & Elie Bouri, 2023. "Multi-Layer Spillovers between Volatility and Skewness in International Stock Markets Over a Century of Data: The Role of Disaster Risks," Working Papers 202337, University of Pretoria, Department of Economics.
    7. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
    8. Elie Bouri & Rangan Gupta & Asingamaanda Liphadzi & Christian Pierdzioch, 2024. "Forecasting Stock Returns Volatility of the G7 Over Centuries: The Role of Climate Risks," Working Papers 202424, University of Pretoria, Department of Economics.
    9. Shixuan Wang & Rangan Gupta & Matteo Bonato & Oguzhan Cepni, 2022. "The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks," Working Papers 202219, University of Pretoria, Department of Economics.
    10. Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024. "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 123-135.
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    More about this item

    Keywords

    REITs; International data; Realized volatility; Forecasting;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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