Community Analysis of Global Financial Markets

Vodenska, Irena; Becker, Alexander P.; Zhou, Di; Kenett, Dror Y.; Stanley, H. Eugene; Havlin, Shlomo

doi:10.3390/risks4020013

Cited by 22 publications

(9 citation statements)

References 80 publications

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“…There is a large strand of research that explores the comovements of stock markets and foreign exchange markets, with a focus on exploring either the volatility spillovers from one market to another or the degree of their correlation [62][63][64][65][66][67][68][69][70][71][72].…”

Section: Literature Reviewmentioning

confidence: 99%

The Comovement of Exchange Rates and Stock Markets in Central and Eastern Europe

2019

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This paper analyses the link between exchange rates and stock markets in four Central and Eastern European countries. We simultaneously explore the comovements of foreign exchange markets and stock markets at the cross-country level and the link between these two markets within each country while employing a Dynamic Conditional Correlation Mixed Data Sampling (DCC-MIDAS) model. Such an approach to financial markets conveys a much more visible picture of the existing patterns of financial integration between these markets that would otherwise be neglected. The estimates reveal significant differences between the patterns of correlation in our sample countries. First, the paper finds a quite low degree of convergence between foreign exchange markets, with rising correlations during some of the crisis episodes. Second, both the 2004 European Union enlargement and the European sovereign debt crisis underpin the stock market comovements in the Central and Eastern European countries. Third, the correlations between the exchange rate returns and stock markets rise mostly during the European sovereign debt crisis and to a lesser extent during the global financial crisis, revealing signs of contagion and lower portfolio diversification opportunities. These results are of utmost relevance for the process of financial integration and they also have important implications for policy makers, risk management, and investors.

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Section: Literature Reviewmentioning

confidence: 99%

The Comovement of Exchange Rates and Stock Markets in Central and Eastern Europe

2019

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“…Then, the initial and final wealth of the investor are correspondingly 0 and X u (f ) − U . The variance of the portfolio is given by equation (9).…”

Section: Variance Of Portfoliomentioning

confidence: 99%

“…A particularly interesting feature of financial markets data is the, not yet fully explored, complex interdependent dynamics of the prices between various assets, which is most often modeled through the empirical correlations. This issue has been of specific value to the econophysics community and its members have provided numerous contributions addressing the relationships between stocks, market indexes and currencies [2,3,4,5,6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Correlation patterns in foreign exchange markets

Basnarkov

Stojkoski

Utkovski

et al. 2019

Physica A: Statistical Mechanics and its Applications

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The value of an asset in a financial market is given in terms of another asset known as numeraire. The dynamics of the value is non-stationary and hence, to quantify the relationships between different assets, one requires convenient measures such as the means and covariances of the respective log returns. Here, we develop transformation equations for these means and covariances when one changes the numeraire. The results are verified by a thorough empirical analysis capturing the dynamics of numerous assets in a foreign exchange market. We show that the partial correlations between pairs of assets are invariant under the change of the numeraire. This observable quantifies the relationship between two assets, while the influence of the rest is removed. As such the partial correlations uncover intriguing observations which may not be easily noticed in the ordinary correlation analysis.

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“…Many approaches have been attempted to map the financial system as a network in which nodes stand for different financial agents and edges between the nodes for their interactions. Some of the methods used to investigate interactions across different financial agents include correlation-based networks, such as the minimal spanning tree approach introduced in [ 2 ]; the extension of the above approach proposed in [ 3 ]; the planar maximally filtered graph, which is also used in a recent study in [ 4 ]; and the average linkage minimum spanning tree proposed in [ 5 ]. Other approaches are based on the correlation threshold network [ 6 ] and partial correlation threshold network methods [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Inference of financial networks using the normalised mutual information rate

2018

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In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics.

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Community Analysis of Global Financial Markets

Cited by 22 publications

References 80 publications

The Comovement of Exchange Rates and Stock Markets in Central and Eastern Europe

The Comovement of Exchange Rates and Stock Markets in Central and Eastern Europe

Correlation patterns in foreign exchange markets

Inference of financial networks using the normalised mutual information rate

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