In this paper we examine how sensitive Value-at-Risk (VaR) forecasts based on simple linear quantile regressions are to the sampling frequency used to calculate realized volatility. We use sampling frequencies from one to 108 min for ICE Brent Crude Oil futures and test the out-of-sample performance of a set of quantile regression models using formal coverage tests. The results show that a one-factor model performs exceptionally well for most sampling frequencies used to calculate realized volatility. In comparison with the well-known Heterogenous Auto-regressive Model of Realized Volatility (HAR-RV) and a quantile regression version of the HAR model (HAR-QREG), we also find that the one-factor model is much less sensitive to the sampling frequency used to calculate realized volatility.

在本文中，我们研究了基于简单线性分位数回归的风险值 (VaR) 预测对用于计算已实现波动率的采样频率的敏感程度。我们对 ICE 布伦特原油期货使用 1 到 108 分钟的采样频率，并使用正式覆盖测试测试一组分位数回归模型的样本外性能。结果表明，对于用于计算已实现波动率的大多数采样频率，单因素模型表现得非常好。与著名的已实现波动率异质自回归模型 (HAR-RV) 和 HAR 模型的分位数回归版本 (HAR-QREG) 相比，我们还发现单因子模型对波动率的敏感性要低得多用于计算已实现波动率的采样频率。