论文标题
混合频率的分位数回归，以预测价值 - 风险和预期不足
Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall
论文作者
论文摘要
尽管计算风险度量的分位数回归已在金融文献中广泛确定，但是在考虑在混合频率下观察到的数据时，需要扩展。在本文中，建议建立在混合 - 频率分位回归的基础上，以直接估计值 - 风险（VAR）和预期的不足（ES）度量。特别是，低频组件包含来自通常，每月或较低频率的变量的信息，而高频组件可以包括各种每日变量，例如市场指数或实现的波动性度量。每日返回过程的平稳性较弱的条件是得出的，并在广泛的蒙特卡洛演习中研究了有限的样本特性。然后，使用两个能源商品，即原油和汽油期货，通过真实的数据应用程序探索所提出模型的有效性。结果表明，根据一些流行的VAR和ES测试测试程序，我们的模型优于其他竞争规格。
Although quantile regression to calculate risk measures has been widely established in the financial literature, when considering data observed at mixed--frequency, an extension is needed. In this paper, a model is suggested built on a mixed--frequency quantile regression to directly estimate the Value--at--Risk (VaR) and the Expected Shortfall (ES) measures. In particular, the low--frequency component incorporates information coming from variables observed at, typically, monthly or lower frequencies, while the high--frequency component can include a variety of daily variables, like market indices or realized volatility measures. The conditions for the weak stationarity of the daily return process are derived and the finite sample properties are investigated in an extensive Monte Carlo exercise. The validity of the proposed model is then explored through a real data application using two energy commodities, namely, Crude Oil and Gasoline futures. Results show that our model outperforms other competing specifications, on the basis of some popular VaR and ES backtesting test procedures.
