Forecasting Commodity Markets Volatility: HAR or Rough?

Mesias Alfeus and Christina Nikitopoulos-Sklibosios ()

No 415, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

Abstract: Commodity is one of the most volatile markets and forecasting its volatility is an issue of paramount importance. We study the dynamics of the commodity markets volatility by employing fractional stochastic volatility and heterogeneous autoregressive (HAR) models. Based on a high-frequency futures price dataset of 22 commodities, we confirm that the volatility of commodity markets is rough and volatility components over different horizons are economically and statistically significant. Long memory with anti-persistence is evident across all commodities, with weekly volatility dominating in most commodity markets and daily volatility for oil and gold markets. HAR models display a clear advantage in forecasting performance compared to fractional volatility models.

Keywords: commodity markets; realized volatility; fractional Brownian motion; HAR; volatility forecast (search for similar items in EconPapers)
JEL-codes: C20 C53 C58 G13 Q02 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2020-12-01
New Economics Papers: this item is included in nep-cwa, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:uts:rpaper:415

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