Global macroeconomic determinants of the domestic commodity derivatives

Countries compete with products which have an absolute advantage in foreign trade operations. Also, there are derivative financial instruments derived from these products in many developing financial markets. Thus, these products provide opportunities for investors such as speculation, arbitrage, and particularly hedging with the help of trading in derivative markets. The trading of these products on derivative markets also brings about the impact of global parameters on spot markets, as well as on futures markets. Hence, it is important for both real investors and financial investors to determine and observe the major macroeconomic variables that affect these products. This chapter aims to determine macroeconomic variables which affect domestic (local) commodity derivatives such as banana (Central America and Ecuador), palm oil (Malaysia), rice (Thailand), and tea (Kenya). Thereby when the market efficiency is weak or almost absent, the ability to lower the fragility against risks faced by the investors and the other related parties by maintaining advance information is analyzed. For this purpose, K* (K Star) algorithm as a data mining method which is one of the knowledge-based analysis techniques is used in the analysis. In this chapter, four derivative products were estimated by the K* algorithm, which predicts whether their direction will decrease or increase during the next 18 months. The results show that the K* algorithm predicts an accuracy of 66.7–72.2% for three of the four domestic commodity derivatives so that this algorithm is successful in identifying similar properties between global macroeconomic variables and domestic commodity derivatives. © Springer International Publishing AG, part of Springer Nature 2018

Understanding Dynamic Conditional Correlations between Commodities Futures Markets

We estimate dynamic conditional correlations between 10 commodities futures returns in energy, metals and agriculture markets over the period 1998-2014 with a DCC-GARCH model. We look at the factors influencing those correlations, adopting a pooled mean group (PMG) estimator. Macroeconomic variables are significantly correlated with agriculture-energy and metals-energy dynamic conditional correlations; while financial variables are relevant in the agriculture-energy correlations and poorly significant in the metals-energy ones. Speculative activity is generally not statistically significant. Correlations started increasing in the years before the financial crisis and decreased at the end of our period of analysis