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.

我们使用 2008 年至 2020 年期间的美国总体和部门数据，通过异质自回归已实现方差 (HAR-RV) 模型检验了房地产投资信托 (REIT) 对已实现市场方差的分离油价冲击的预测能力. 样本内检验表明，与供给冲击相比，需求冲击和金融市场风险冲击对模型整体拟合的贡献更大，其中冲击影响的样本内传递主要通过它们对实现的显着影响来发挥作用。向上（“好”）方差。样本外测试证实了需求和金融市场风险冲击对已实现方差及其在短期、中期和长期预测范围内的上行对应物、各种递归估计窗口、已实现波动率的显着预测值（即是，已实现方差的平方根），对于较短的子样本周期，排除了近期异常剧烈的石油市场动荡阶段，对于具有已实现高阶矩、已实现跳跃和杠杆效应的扩展基准模型，如控制变量。我们还研究了 HAR-RV 模型的基于分位数的扩展，并分析了使用冲击进行已实现方差预测的经济效益。