Structural breaks and volatility forecasting in the copper futures market

Abstract

This paper examines whether structural breaks contain incremental information for forecasting the volatility of copper futures. Considering structural breaks in volatility, we develop four heterogeneous autoregressive (HAR) models based on classical or latest HAR‐type models. Subsequently, we apply these models to forecast volatility in the copper futures market. The empirical results reveal that our models exhibit better in‐sample and out‐of‐sample performances than classical or latest HAR‐type models. This suggests that structural breaks contain incremental prediction information for the volatility of copper futures. More importantly, we argue that considering structural breaks can improve the performances of most of existing HAR‐type models. Highlights There are many structural break points in return volatility of the copper futures. We propose 12 new heterogeneous autoregressive models. Our models outperform the existing heterogeneous autoregressive models. Structural breaks contain additional ex ante information for volatility forecasting. The ex ante information is obvious in forecasting mid‐ and long‐term volatilities.

VIX term structure and VIX futures pricing with realized volatility

“…See Corsi and Audrino () for a review of this model. Some latest developments of HAR‐type models are discussed by Gong and Lin ().…”

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

“…Many studies (e.g., Becker & Clements, 2008;Chen et al, 2013;Gong & Lin, 2018;Khalifa, Miao, & Ramchander, 2011;Koopman et al, 2005;Kristjanpoller & Minutolo, 2016;Ma et al, 2017;Patton, 2011;Yang, Chen, & Tian, 2015) use these loss functions to evaluate the statistical difference of the forecasting performance. Many studies (e.g., Becker & Clements, 2008;Chen et al, 2013;Gong & Lin, 2018;Khalifa, Miao, & Ramchander, 2011;Koopman et al, 2005;Kristjanpoller & Minutolo, 2016;Ma et al, 2017;Patton, 2011;Yang, Chen, & Tian, 2015) use these loss functions to evaluate the statistical difference of the forecasting performance.…”

“…Many studies on forecasting stock volatility consider information from the stock market alone-for example, Corsi, Pirino, and Reno (2010), Bekaert and Hoerova (2014), Duong and Swanson (2015), Wang, Ma, Wei, and Wu (2016), Ma, Wahab, Huang, and Xu (2017), and Gong and Lin (2018). In this research, instead of treating the stock market as a stand-alone source, we consider cojumps within the agricultural futures market, based on four different commodities (i.e., corn, wheat, cotton, and soybean), and cojumps between the agricultural futures market and the stock market.…”

Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets

“…Moreover, structural changes are likely to be responsible for most major forecast failures of time-invariant series models. Recently, Kumar [2] has found that volatility transmission from crude oil to equity sectors is structurally unstable and exhibits structural breaks; Gong and Lin [3] have examined whether structural breaks contain incremental information for forecasting the volatility of copper futures, and they have argued that considering structural breaks can improve the performance of most of the existing heterogeneous autoregressive-type models; Ma et al [4] have introduced Markov regime switching to forecast the realized volatility of the crude oil futures market; in the same context, Wang et al [5] have found that time-varying parameter models can significantly outperform their constant-coefficient counterparts for longer forecasting horizons.…”