Modeling and forecasting commodity market volatility with long‐term economic and financial variables

Abstract

This paper investigates the time-varying volatility patterns of some major commodities as well as the potential factors that drive their long-term volatility component. For this purpose, we make use of a recently proposed GARCH-MIDAS approach which typically allows us to examine the role of economic and financial variables of different frequencies. Using commodity futures for crude oil (WTI and Brent), gold, silver and platinum, our results show the necessity of disentangling the short-and long-term components in modeling and forecasting commodity volatility. They also indicate that the long-term volatility of most commodity futures is significantly driven by the level of the general real economic activity as well as the changes in consumer sentiment, industrial production, and economic policy uncertainty. However, the forecasting results are not alike across commodity futures as no single model fits all commodities.

Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting

“…The authors employ the Baltic Dry index which is somewhat similar to Kilian's (2009) GREA. Interestingly, GREA also turns out to be of explanatory value for other commodities (Nguyen & Walther, 2019). However, we cannot support the findings that the VIX is important for the volatility of Bitcoin.…”

“…However, as outlined in Nguyen & Walther (2019), the long-term component of GARCH-MIDAS affects the overall volatility including forecast of longer horizons.…”

Forecasting realized volatility of agricultural commodities

“…Hence, we maintain that the search for improving the forecasting accuracy of the U.S. agricultural commodities volatility should not be located at the development of extended HAR models that take into account properties such as jump component, the continuous component, the signed jumps and the volatility or return leverage, but rather on other direction, such as the inclusion of exogenous predictors. , Degiannakis & Filis (2017), and Nguyen & Walther (2019) have already shown that the incorporation of different asset classes volatilities helps improving commodities prices and volatilities (oil prices and volatility in particular) and hence, further study should assess whether such asset classes could also help improve forecasts for agricultural commodities. Even more, future research should consider how extreme weather events, food stocks, biofuels production or even market speculative activity could improve further the agricultural commodity volatility forecasts.…”

Uncertainty, Spillovers, and Forecasts of the Realized Variance of Gold Returns

“…The Lasso estimator is a well-established regression technique that aims at both predictor selection and model regularization (that is, the process of limiting the dimension of a forecasting model so as to prevent overfitting) in order to enhance the prediction accuracy and interpretability of the resulting forecasting model. Our paper, thus, adds to the already existing large literature on the predictability of gold-returns volatility based on a wide array of models and macroeconomic, financial, and behavioral predictors (see, for example, Pierdzioch et al [21], Fang et al [22], Nguyen and Walther [23], Salisu et al [24], Bonato et al [25]) by considering the role of uncertainties of major economies of the world and the associated international spillovers. We also would like to emphasize that our aim was not to introduce new econometric or forecasting methods in this paper but to provide a novel and important application of forecasting RV of the gold market, based on the informational content of uncertainty of major economies, and also the corresponding international spillovers to the rest of the world.…”