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Do Oil-Price Shocks Predict the Realized Variance of U.S. REITs?

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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
    Copenhagen Business School, Department of Economics, Porcelænshaven 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 examine, using aggregate and sectoral U.S. data for the period 2008-2020, the predictive power of disentangled oil-price shocks for Real Estate Investment Trusts (REITs) realized market variance via the heterogeneous auto-regressive realized variance (HAR-RV) model. In-sample tests show that demand and financial-market risk shocks contribute to a larger extent to the overall fit of the model than supply shocks, where the in-sample transmission of the impact of the shocks mainly operates through their significant effects on realized upward (good) variance. Out-of-sample tests corroborate the significant predictive value of demand and risk shocks for realized variance and its upward counterpart at a short, medium, and long forecast horizon, for various recursive-estimation windows, for realized volatility (that is, the square root of realized variance), for a shorter sub-sample period that excludes the recent phase of exceptionally intense oil-market turbulence, and for an extended benchmark model that features realized higher-order moments, realized jumps, and a leverage effect as control variables.

Suggested Citation

  • Matteo Bonato & Rangan Gupta & Christian Pierdzioch, 2020. "Do Oil-Price Shocks Predict the Realized Variance of U.S. REITs?," Working Papers 2020100, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:2020100
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    3. Walid Mensi & Zhuhua Jiang & Xuan Vinh Vo & Seong‐Min Yoon, 2023. "Asymmetric volatility transmission and hedging strategies among REIT, stock, and oil markets," Australian Economic Papers, Wiley Blackwell, vol. 62(4), pages 597-615, December.
    4. Mensi, Walid & Nekhili, Ramzi & Kang, Sang Hoon, 2022. "Quantile connectedness and spillovers analysis between oil and international REIT markets," Finance Research Letters, Elsevier, vol. 48(C).
    5. Aharon, David Y. & Ali, Shoaib & Brahim, Mariem, 2024. "Connectedness at extremes between real estate tokens and real estate stocks," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    6. Liu, Jian & Chen, Yan & Liao, Shufei & Cheng, Cheng & Fu, Yongge, 2023. "Information spillovers in Hong Kong REITs and related asset markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 215-229.
    7. 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.
    8. Mensi, Walid & Reboredo, Juan C. & Ugolini, Andrea & Vo, Xuan Vinh, 2022. "Switching connectedness between real estate investment trusts, oil, and gold markets," Finance Research Letters, Elsevier, vol. 49(C).
    9. Popkova, Elena G. & Sergi, Bruno S., 2024. "Energy infrastructure: Investment, sustainability and AI," Resources Policy, Elsevier, vol. 91(C).
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    11. Salisu, Afees A. & Akinsomi, Omokolade & Ametefe, Frank Kwakutse & Hammed, Yinka S., 2024. "Gold market volatility and REITs' returns during tranquil and turbulent episodes," International Review of Financial Analysis, Elsevier, vol. 95(PA).

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    More about this item

    Keywords

    Oil price shocks; REITs; Realized variance; 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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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