Volatility forecasting of strategically linked commodity ETFs: gold-silver
使用九种单变量、两种多变量和三种组合HAR模型，基于高频数据预测黄金和白银的一日向前波动率，发现组合预测优于单变量模型，且未发现波动溢出效应。
We apply heterogeneous autoregressive (HAR) models—including nine univariate, two multivariate and three combination models—to high-frequency data to predict the one-day forward volatilities of two strategically linked commodities, gold and silver. We provide evidence that it is difficult to beat the benchmark HAR model using univariate models and that, a much better strategy is to average the forecasts from many models. In addition, the forecasts are not improved by using volatilities from strategically linked commodities; thus, no volatility spillovers are detected. Interestingly, when the two strategically linked commodities are modelled together using the generalized HAR model, the forecasts are comparable to those of combination forecast models.