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Author
Gurgul Henryk (AGH University of Science and Technology), Syrek Robert (Jagiellonian University, Poland)
Title
Mutual Information between the Main Foreign Subindices: The Application of Copula Entropy around WHO's declaration date at the time of the COVID-19 pandemic
Source
International Entrepreneurship Review, 2024, vol. 10, nr 2, s. 7-24, tab., wykr., bibliogr. 33 poz.
Keyword
Informacja, Pandemia, COVID-19, Entropia, Korelacja
Information, Pandemic, COVID-19, Entropy, Correlation
Note
JEL Classification: G15, G19
summ.
The authors acknowledge that their publication is financed by AGH University of Science and Technology in Cracow (institutional subsidy for maintaining Research Capacity Grant 11|11.200.325).
Abstract
Objective: The objective of this article is to investigate the dependencies between selected European subindices before and during the COVID-19 pandemic.
Research Design & Methods: The main analysis was quantitative. We used copula entropy and Pearson's correlation. We considered the closing prices of sectoral indices from France (CAC sectors), Germany (DAX sectors), the UK (FTSE sectors), and the US (SP sectors), along with the main indices from these countries, that is CAC40, DAX, SP500, and FTSE100 (we collected the data from the database investing.com for the period from 4 January 2017 to 30 March 2023). We performed all analyses using R along with supplementary packages.
Findings: When it comes to indications of the strength of dependence before and after the event (the outbreak of the COVID-19 pandemic) in relation to mutual information (delta) and linear correlation, we saw the biggest differences for the German market. For the DAX sectors, linear correlation underestimates post-event dependencies. The dependencies for other countries were similar on average. For half of the sectors (all markets), we recorded an increase in dependence after the event. A sector where we recorded growth in all countries was the TECH sector.
Implications & Recommendations: The dependence measurement using mutual information expressed in terms of copulas has many advantages. It is not limited to measuring linear correlations. It can also capture a nonlinear correlation. Furthermore, it not only measures the dependence degree, but also considers the dependence structure, which is more than a correlation. Moreover, there was no assumption about the ellipticity of marginal and joint distribution. This dependence measure even allows for the modelling of the dependence of variables with different cumulative distribution functions.
Contribution & Value Added: The novelty of this article is that it compares the results of dependence measurements by linear correlations and mutual information expressed in terms of copula entropy. Considering the indices and subindices of the main European stock markets, when both measures of dependence were used, we obtained significantly different results in both subperiods under investigation (i.e. before and after March 11, 2020). (original abstract)
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ISSN
2658-1841
Language
eng
URI / DOI
https://doi.org/10.15678/IER.2024.1002.01
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