Using Mutual Information to Analyse Serial Dependence: The Effects of COVID-19

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Author(s): Andreia Dionísio (CEFAGE, IIFA, Universidade de Évora, Portugal)and Paulo Ferreira (Polytechnic Institute of Portalegre, Portugal)
Copyright: 2021
Pages: 17
Source title: Handbook of Research on Financial Management During Economic Downturn and Recovery
Source Author(s)/Editor(s): Nuno Miguel Teixeira (Center for Research in Business and Administration, School of Business Sciences, Polytechnic Institute of Setúbal, Portugal)and Inês Lisboa (CARME, School of Management and Technology, Polytechnic of Leiria, Portugal)
DOI: 10.4018/978-1-7998-6643-5.ch023

Keywords: Accounting and Finance / Business & Management / Business Science Reference / Economics & Economic Theory

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Abstract

The main objective of this research is to analyse the serial dependence of high frequency data for G7 stock indices. The authors use two different periodicities, and with linear and nonlinear approaches, they evaluate the stock markets' behaviour and conclude about the higher or lower dependence levels of the stock markets in the periods before and after the COVID-19 pandemic declaration. They use mutual information and the global correlation coefficient based on that measure, comparing results with the linear coefficient. The results are clear, showing that nonlinear dependence exists and could be an important factor in terms of historical information, especially for very high frequency data. Results are mixed in regard to the effect of the pandemic declaration in the dependence of stock markets.

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Using Mutual Information to Analyse Serial Dependence: The Effects of COVID-19

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