Complex Networks and Minimal Spanning Trees in International Trade Network

Abstract

The wealth of a nation is changed by the internal economic growth of a nation and by the international trade among countries. Trade between countries are one of their most important interactions and thus expects to affect crucially the wealth distribution over countries. We reviewed the network properties of the international trade networks (ITN). We analyzed data sets of world trade. The data set include a total number of 190 countries from 1950 to 2000. We observed that the world trade network showed the uneven trading relationships which are measured by the disparity. The effective disparity followed a power law, < D(k) >∼ t δ , for the import and export network. We also construct the minimal spanning tree(MST) of international trade network, where each node is a country and directed links connecting them represent money flow from a source node to a target one. The topology of the MST shows the flow patterns of the international trades. From the MST we can identify the sub-economic zone if we delete the hub node. We observed that the cumulative degree distribution functions follow the power law, P>(k) ∼ k −α , with the average exponent α = 1.1(1)). We also calculated the betweenness centrality(BC) of the MST. The cumulative probability distribution of the betweenness centrality follows the power law, P>(BC) ∼ BC −β , with the average exponent β = 1.09(7).

Extracting the sovereigns’ CDS market hierarchy: A correlation-filtering approach

“…After Mantegna several authors have followed a similar approach to extract the topology of foreign exchange markets (Naylor et al [5]); (Mizuno et al [6]), interest rates (Aste and Di Matteo [7]), government bonds (Gilmore et al [8]) commodities (Gilmore et al [9]) international trade (Maeng et al [10]) and stock markets (Kullmann et al [11]; Bonanno et al [12,13]; Eryigit and Eryigit [14]; Onnela et al [15]; Mantegna and Stanley [16]; Mantegna [17]). …”

“…13 This fee 9 Such irrelevance for correlations above zero is important since MST algorithms are based on the ranking of distances between the time-series. 10 There is evidence (e.g. Melo and Rincón [19]; Fratzscher [20]) of structural changes due to the 2007-2008 crisis.…”

Extracting the sovereigns' CDS market hierarchy : a correlation-filtering approach

“…The first documented attempt to map correlation coefficients into distances pertains to Mantegna (1998), who aimed to using stock prices in order to find the kind of topological arrangement in US equity markets, and to search for the existence and nature of common economic factors that drive the evolution of stock prices. After Mantegna several authors have followed a similar approach to extract the topology of foreign exchange markets (Naylor et al, 2007;Mizuno et al, 2006), interest rates (Aste and Di Matteo, 2005), government bonds (Gilmore et al, 2010) commodities (Gilmore et al, 2012) international trade (Maeng et al, 2012) and stock markets (Kullmann et al, 2002;Bonanno et al, 2004Bonanno et al, & 2003Eryigit and Eryigit, 2009;Onnela et al, 2003;Mantegna and Stanley, 2000;Mantegna, 1999).…”

Network Analysis of Pakistan Stock Market During the Turbulence of Economic Crisis

“…Network formed through MST is useful in examining topological properties of the network, and is widely applied in numerous areas of scientific investigations, see e.g. portfolio analysis (Onnela, Chakraborti, Kaski, & Kertiész, 2002), trade network (Maeng, Choi, & Lee, 2012), systematic risk assessment (Song, Ko, & Chang, 2018), commodity market (Lee & Nobi, 2018), Foreign exchange market (Jang, Lee, & Chang, 2011), and so on. It is due to this interest of scientific community MST serve as a prime network mapping tool, and a useful method for event study .…”