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Forecasting Realized Volatility of Agricultural Commodities.

Degiannakis, S., Filis, G, Klein, T. and Walther, T., 2022. Forecasting Realized Volatility of Agricultural Commodities. International Journal of Forecasting, 38 (1), 51-73.

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GFilis_IJoF_post-script.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1016/j.ijforecast.2019.08.007


We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous AutoRegressive (HAR) model. We obtain tick-by-tick data for five widely traded agricultural commodities (Corn, Rough Rice, Soybeans, Sugar, and Wheat) from the CME/ICE. Real out-of-sample forecasts are produced for 1- up to 66-days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we convincingly demonstrate that such HAR extensions do not offer any superior predictive ability in the out-of-sample results, since none of these extensions produce significantly better forecasts compared to the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit by adding more complexity, related to volatility decomposition or relative transformations of volatility, in the forecasting models.

Item Type:Article
Uncontrolled Keywords:Agricultural Commodities; Realized Volatility; Median Realized Volatility; Heterogeneous Autoregressive model; Forecast
Group:Bournemouth University Business School
ID Code:32687
Deposited By: Symplectic RT2
Deposited On:30 Aug 2019 13:31
Last Modified:14 Mar 2022 14:17


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