This paper empirically investigates the cointegrated relationship between oil price and nominal exchange rate of US Dollar/ Norwegian Krone (USD/NOK) which is covering a time period from 2001 to 2011. The Augmented Dickey-Fuller test, Engle-Granger test and Error Correction Mechanism are employed for this research. This paper concludes that there is a cointegrated relationship between oil price and nominal exchange rate of USD/NOK in the long term.

This study has conducted an OLS-regression to examine the relationship between gasoline and crude oil prices in the Swedish petroleum market, focusing on potential asymmetry, where gasoline prices respond more quickly to increases in crude oil prices compared to decreases. Additionally, we examine the impact of individual petroleum stations' competitiveness on this asymmetry, applying the distance to the nearest station as a measure of competitiveness. To explore this relationship, we utilise two datasets: one comprises unique user-generated data for individual gas stations spanning the period from 2019 to 2022, while the other includes recommended prices covering the period from 2001 to 2020. The findings provide some evidence supporting the existence of asymmetry, indicating the presence of inefficiencies within the market. However, no evidence suggesting larger asymmetry concerning individual competitiveness was discovered. Overall, this research offers novel insights into the dynamics of the Swedish fuel market in recent years.

Cette thèse analyse comment les fluctuations du prix du pétrole affectent les économies des pays exportateurs de produits de base. Plus précisément, l'objectif de cette thèse est d'étudier les impacts macroéconomiques, financiers et environnementaux des fluctuations des prix du pétrole, en accordant une attention particulière à l'hypothèse de la monnaie du pétrole. À cette fin, cette thèse se compose de trois chapitres. Les premier et deuxième chapitres portent sur le taux de change réel des devises de plusieurs pays exportateurs de pétrole. Le troisième chapitre explore les liens entre le prix des biocarburants et le compte courant des pays émergents et en développement exportant ou important des matières premières agricoles contrôlant l'effet non linéaire potentiel exercé par le prix du pétrole sur cette relation. Ces chapitres montrent que si le prix du pétrole a un effet macroéconomique sur les économies exportatrices de pétrole et les pays exportateurs de produits agricoles, son impact varie d'un pays à l'autre et il n'y a pas de règle unique pour décrire le fonctionnement de ces économies. / This thesis analyzes how fluctuations in the price of oil affect the economies of commodity exporting countries. More specifically, the aim of this thesis is to investigate the macroeconomic, financial and environmental impacts of oil price fluctuations, by paying particular attention to the oil currency hypothesis. To this end, this thesis is composed of three chapters. The first and second chapters deal with the real exchange rate of the currencies of several oil exporting countries. The third chapter explores the links between the price of biofuels and the current account of emerging and developing countries exporting or importing agricultural raw materials controlling for the potential nonlinear effect exerted by the price of oil on this relationship. Altogether these chapters show that while the price of oil has a macroeconomic effect on oil exporting and agricultural commodities exporting countries, its impact varies across countries and there is no one fits all rule.

Crude oil prices impact the world economy and are thus of interest to economic experts and politicians. Oil price’s volatile behaviour, which has moulded today’s world economy, society and politics, has motivated and continues to excite researchers for further study. This volatile behaviour is predicted to prompt more new and interesting research challenges. In the present research, machine learning and computational intelligence utilising historical quantitative data, with the linguistic element of online news services, are used to predict crude oil prices via five different models: (1) the Hierarchical Conceptual (HC) model; (2) the Artificial Neural Network-Quantitative (ANN-Q) model; (3) the Linguistic model; (4) the Rule-based Expert model; and, finally, (5) the Hybridisation of Linguistic and Quantitative (LQ) model. First, to understand the behaviour of the crude oil price market, the HC model functions as a platform to retrieve information that explains the behaviour of the market. This is retrieved from Google News articles using the keyword “Crude oil price”. Through a systematic approach, price data are classified into categories that explain the crude oil price’s level of impact on the market. The price data classification distinguishes crucial behaviour information contained in the articles. These distinguished data features ranked hierarchically according to the level of impact and used as reference to discover the numeric data implemented in model (2). Model (2) is developed to validate the features retrieved in model (1). It introduces the Back Propagation Neural Network (BPNN) technique as an alternative to conventional techniques used for forecasting the crude oil market. The BPNN technique is proven in model (2) to have produced more accurate and competitive results. Likewise, the features retrieved from model (1) are also validated and proven to cause market volatility. In model (3), a more systematic approach is introduced to extract the features from the news corpus. This approach applies a content utilisation technique to news articles and mines news sentiments by applying a fuzzy grammar fragment extraction. To extract the features from the news articles systematically, a domain-customised ‘dictionary’ containing grammar definitions is built beforehand. These retrieved features are used as the linguistic data to predict the market’s behaviour with crude oil price. A decision tree is also produced from this model which hierarchically delineates the events (i.e., the market’s rules) that made the market volatile, and later resulted in the production of model (4). Then, model (5) is built to complement the linguistic character performed in model (3) from the numeric prediction model made in model (2). To conclude, the hybridisation of these two models and the integration of models (1) to (5) in this research imitates the execution of crude oil market’s regulators in calculating their risk of actions before executing a price hedge in the market, wherein risk calculation is based on the ‘facts’ (quantitative data) and ‘rumours’ (linguistic data) collected. The hybridisation of quantitative and linguistic data in this study has shown promising accuracy outcomes, evidenced by the optimum value of directional accuracy and the minimum value of errors obtained.