Volatility forecasting of strategically linked commodity ETFs: gold-silver

Štefan Lyócsa and Peter Molnár

Quantitative Finance, 2016, vol. 16, issue 12, 1809-1822

Abstract: 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.

Date: 2016
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DOI: 10.1080/14697688.2016.1211799

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