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Do oil-price shocks predict the realized variance of U.S. REITs?

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  • Bonato, Matteo
  • Çepni, Oğuzhan
  • Gupta, Rangan
  • Pierdzioch, Christian

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 financial-market-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. We also study a quantiles-based extension of the HAR-RV model, and we analyze the economic benefits of using shocks for realized-variance forecasting.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:eneeco:v:104:y:2021:i:c:s0140988321005429
    DOI: 10.1016/j.eneco.2021.105689
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    Cited by:

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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. 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.
    6. 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).

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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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