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

Highlights: • Study the predictive power of oil-price shocks for REITs realized market variance. • Estimate various versions of the HAR-RV model at conditional mean and quantiles. • Study in-sample and out-of-sample predictability. • Demand and financial-market-risk shocks contribute more important than supply shocks. • Controls include realized higher-order moments, realized jumps, and a leverage effect. 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.