Global Macroeconomic Determinants of the Domestic Commodity Derivatives

Cagatay Basarir () and Mehmet Fatih Bayramoglu ()
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Cagatay Basarir: Bandirma Onyedi Eylul University
Mehmet Fatih Bayramoglu: Bulent Ecevit University

Chapter Chapter 16 in Global Approaches in Financial Economics, Banking, and Finance, 2018, pp 331-349 from Springer

Abstract: Abstract 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.

Keywords: Derivative markets; Macro-finance; K* (K Star) algorithm; Lazy learning; Data mining (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-78494-6_16

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