Estimating the term structure of commodity market preferences

Abstract

The commodity futures curve is viewed as a market-based path forecast, a term structure, optimizing multivariate loss preferences. Based on the forecast decision setting, we apply estimation of flexible multivariate loss functions, which reveal the preference term structure along the futures curve, which can be flat, smoothly sloping or oscillating, rotating among optimism, pessimism and symmetry. Evidence from the thirty main world commodities around the global crisis period, accommodates the futures curve forecast rationality questioned in the literature, suggesting the presence of joint preference asymmetries for longer maturities and symmetries for shorter ones. This reveals joint optimistic preferences for most commodities until 2004, evolving into oscillating preferences rotating within the term structure from symmetry to pessimism and optimism in 2005-2008 and finally back to weaker optimism until 2013.

The Rationality of USDA Forecasts under Multivariate Asymmetric Loss

“…To test for the stability of the estimated loss function parameters, we use an out‐of‐sample technique of detecting forecast breakdowns proposed by Giacomini and Rossi (2009) that has been used in similar econometric settings (Mamatzakis and Koutsomanoli‐Filippaki 2014; Mamatzakis and Tsionas 2015; Christodoulakis 2020). Following Isengildina‐Massa, Karali, and Irwin (2013), we specifically examine the potential for forecast breakdowns during the 2007–2008 commodity price boom.…”

“…Krüger and LeCrone (2019) show that this method has a high power and is robust to fat tails, serial correlation, and outliers. The method has been used to evaluate forecasts of a number of economic variables by professional forecasters (Aretz, Bartram, and Pope 2011;Pierdzioch, Rülke, and Stadtmann 2013; Mamatzakis and Koutsomanoli‐Filippaki 2014; Fritsche et al 2015; Pierdzioch, Reid, and Gupta 2016; Tsuchiya 2016a, 2016b; Christodoulakis 2020), government agencies (Auffhammer 2007; Krol 2013; Tsuchiya 2016a; Giovannelli and Pericoli 2020), international organizations (Christodoulakis and Mamatzakis 2008; Tsuchiya 2016a; Giovannelli and Pericoli 2020), and central banks (Capistrán 2008; Baghestani 2013; Pierdzioch, Rülke, and Stadtmann 2015; Ahn and Tsuchiya 2019; Caunedo et al 2020). These studies overwhelmingly suggest that forecasts that are biased or inefficient under MSE loss are rational under asymmetric loss.…”

The network of commodity risk

“…A rich literature has then focused on the spillover effects between oil price and financial markets [ 72 ], and oil price and other commodities [ 10 , 39 ] such as precious metals [ 47 , 93 ], agricultural [ 43 , 84 , 85 ], energy [ 49 , 92 , 102 ], and, more recently, the impact of climate related variables on the co-movements of commodity prices that affect the stability of the financial system [ 50 ]. Besides, commodity price behavior shows small trends and big variability that affects market preferences also in the long-run [ 23 , 30 , 103 ].…”