Integrated decision recommendation system using iteration-enhanced collaborative filtering, golden cut bipolar for analyzing the risk-based oil market spillovers

• Published in: Computational economics Vol. 63; no. 1; pp. 305 - 338
• Main Authors: Mikhaylov, Alexey, Bhatti, Ishaq M., Dinçer, Hasan, Yüksel, Serhat
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2024; Springer; Springer Nature B.V
• Subjects: Behavioral/Experimental Economics; Collaboration; Commodity futures; Computer Appl. in Social and Behavioral Sciences; Correlation coefficient; Correlation coefficients; COVID-19; Decision analysis; Decision making; Decision making models; Econometrics; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Filtration; Financial markets; Foreign exchange; Foreign exchange markets; Foreign exchange rates; Interdependence; International trade; Iterative methods; Math Applications in Computer Science; Operations Research/Decision Theory; Pandemics; Petroleum; Prices; Recommender systems; Risk analysis; Spillover effect; Yuan (China); O13; Q4; Bipolar QROFSs; M-SWARA; ELECTRE; Golden cut; Analytics
• DOI: 10.1007/s10614-022-10341-8
• ISSN: 0927-7099; 1572-9974

This article is dedicated analyzing the interdependence of oil prices and exchange rate movements of oil exporting countries (the Russian ruble, Euro, Canadian dollar, Chinese yuan, Brazil real, Nigerian naira, Algerian dinar). The study also considers risk-based oil market spillovers in global crisis periods with integrated decision recommendation systems. For this purpose, a fuzzy decision-making model is created by considering the bipolar model and imputation of expert evaluations with collaborative filtering. The main contribution of this study is both its econometric analysis and evaluations based on expert opinions. This helps reach more crucial results. All three of the recent shocks (2008, 2012, 2020) in the oil market are transmitted to foreign exchange markets of oil-producing countries. At the same time, the last shock of 2020 caused by the COVID-19 pandemic has not yet been fully reflected on the Russian ruble exchange rate. Correlation parameters became weaker in the last year, as the Russian ruble correlation coefficient fluctuates between − 0.5 and 0.5. However, before 2020 the spillover effect had a higher significance (in the range from − 0.8 to − 0.1). Nigerian naira and Algerian dinar were showing almost the same movements, while the Russian Ruble was in a different trading range.