Screening for light crude oil and market comovements

Abstract

This research is structured as follows: the introduction in the first section presents an overview of the method and outlines the study's objective. The literature review provides related work referring to the crude oil market and the application of hierarchical correlation clustering to characterize current developments in both fields. It is followed by the research and methodology, characterizing the implemented method and algorithm and providing information about the data preprocessing steps. The publication is completed by the results and discussion section, presenting the visualized cluster mapping results. The project's outcome is summarized in the concluding section, reflecting current and possible future implementations of the screening. Areas for future investigations are mentioned. All tables are provided in the appendix to facilitate the reading flow. Acronyms and abbreviations are indicated in the appendix.This study aimed to perform a screening for economic interrelationships among market participants from the stock market, global stock indices, and commodities from fossil energy, agricultural, and the metals sector. Particular focus was put on the comovements of the light crude oil benchmarks West Texas Intermediate (WTI) and Brent crude oil. In finance research and the crude oil markets, identifying novel groupings and interactions is a fundamental requirement due to the extended impact of crude oil price fluctuations on economic growth and inflation. Thus, it is of high interest for investors to identify market players and interactions that appear sensitive to crude oil price volatility triggers. The price development of 14 stocks, 25 leading global indices, and 13 commodity prices, including WTI and Brent, were analyzed via data mining applying the hierarchical correlation cluster mapping technique. All price data comprised the period from January 2012 – December 2018 and were based on daily returns. The technique identifies and visualizes existing hierarchical clusters and correlation patterns emphasizing comovements that indicate positively correlated processes. The method successfully identified clustering patterns and a series of relevant and partly unexpected novel comovements in all investigated economic sectors. Although additional research is required to reveal the causative factors, the study offers an insight into in-depth market interrelationships.123129