Treffer: Replication Data for: A Comparison of Price Fluctuations Between Brent Crude Oil and Retail Fuel Prices in Stavanger - An Algorithmic Model for Refueling

Title:
Replication Data for: A Comparison of Price Fluctuations Between Brent Crude Oil and Retail Fuel Prices in Stavanger - An Algorithmic Model for Refueling
Authors:
Nes, Ola
Contributors:
Nes, Ola, Nielsen, Alex B.
Source:
USD-NOK exchange rates were downloaded from ExcelRates.com. This data has one price for the exchange rate between USD-NOK per trading day, which is recorded at 04:30 PM Central European Time and comes from the official exchange rates from the European Central Bank. For weekends and holidays, the last trading day exchange rate is used. Data from Excelrates.com are based on the official exchange rates from the European Central Bank (ECB). Historical daily Brent crude oil prices were downloaded from Macrotrends.net. Macrotrends has one price for Brent crude oil per trading day, which is the closing price for that day.
Publisher Information:
DataverseNO
Publication Year:
2021
Subject Terms:
Mathematical Sciences, Gasoline and diesel prices, Brent crude oil, Efficient market hypothesis, Statistics, GARCH(1,1), Algorithmic trading models, Pearson correlation, Volatility, Economical markets, Price spikes, Edgeworth cycles
Document Type:
dataset
Language:
English
Relation:
https://doi.org/10.18710/RPTX0D
DOI:
10.18710/RPTX0D
Availability:
https://doi.org/10.18710/RPTX0D
Accession Number:
edsbas.AAB2A944
Database:
BASE

Weitere Informationen

This data set is used in the Master's thesis: "A Comparison of Price Fluctuations Between Brent Crude Oil and Retail Fuel Prices in Stavanger - An Algorithmic Model for Refueling" by Ola Nes (2021) The data set contains the fuel prices collected (Excel and CSV files), and the Python code which contains all functions used in the thesis. Abstract for thesis: "This thesis investigates and compares the volatility in the retail fuel market in Stavanger and Brent crude oil. Gasoline and diesel prices have been collected from gas stations in Stavanger in 2020 and 2021, and are used for the thesis’ main goal of developing an algorithmic mathematical model for refueling vehicles at optimal times for consumers that could be used in practice. The collected data suggests that there is higher volatility in the retail fuel market in Stavanger compared to the Brent crude oil market. Gas stations follow a characteristic Edgeworth cycle pattern that have price spikes occur when restarting their price cycles. These occur for the most part at the same time across all gas stations monitored in Stavanger. This pattern can be difficult for consumers to predict. Therefore, a practical refueling algorithm could be useful. There are many factors that go in to such a model to make it efficient such as price spike analysis from the Edgeworth cycle pattern found in retail fuel markets and estimating volatility using GARCH(1,1) method."