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We apply RMT, Network and MF-DFA methods to investigate correlation, networkand multifractal properties of 20 global financial indices. We compare resultsbefore and during the financial crisis of 2008 respectively. We find that thenetwork method gives more useful information about the formation of clusters ascompared to results obtained from eigenvectors corresponding to second largesteigenvalue and these sectors are formed on the basis of geographical locationof indices. At threshold 0.6, indices corresponding to Americas, Europe andAsia/Pacific disconnect and form different clusters before the crisis butduring the crisis, indices corresponding to Americas and Europe are combinedtogether to form a cluster while the Asia/Pacific indices forms anothercluster. By further increasing the value of threshold to 0.9, Europeancountries France, Germany and UK constitute the most tightly linked markets. Westudy multifractal properties of global financial indices and find thatfinancial indices corresponding to Americas and Europe almost lie in the samerange of degree of multifractality as compared to other indices. India, SouthKorea, Hong Kong are found to be near the degree of multifractality of indicescorresponding to Americas and Europe. A large variation in the degree ofmultifractality in Egypt, Indonesia, Malaysia, Taiwan and Singapore may be areason that when we increase the threshold in financial network these countriesfirst start getting disconnected at low threshold from the correlation networkof financial indices. We fit Binomial Multifractal Model (BMFM) to thesefinancial markets.

Correlation, Network and Multifractal Analysis of Global Financial Indices