Economic and political effects on currency clustering dynamics

The cross-correlations between the exchange rate fluctuations of 74 currencies over the period 1995-2012 are analyzed in this paper. The eigenvalue distribution of the cross-correlation matrix exhibits a bulk which approximately matches the bounds predicted from random matrices constructed using mutually uncorrelated time-series. However, a few large eigenvalues deviating from the bulk contain important information about the global market mode as well as important clusters of strongly interacting currencies. We reconstruct the network structure of the world currency market by using two different graph representation techniques, after filtering out the effects of global or market-wide signals on the one hand and random effects on the other. The two networks reveal complementary insights about the major motive forces of the global economy, including the identification of a group of potentially fast growing economies whose development trajectory may affect the global economy in the future as profoundly as the rise of India and China has affected it in the past decades.

Journal of Physics: Conference Series, 2017

A currency exchange rate is the price of one country's currency in terms of another country's currency. There are four different prices; opening, closing, highest, and lowest can be achieved from daily trading activities. In the past, a lot of studies have been carried out by using closing price only. However, those four prices are interrelated to each other. Thus, the multivariate time series can provide more information than univariate time series. Therefore, the enthusiasm of this paper is to compare the results of two different approaches, which are mean vector and Escoufier's RV coefficient in constructing similarity matrices of 20 world currencies. Consequently, both matrices are used to substitute the correlation matrix required by network topology. With the help of degree centrality measure, we can detect the currency's dominance for both networks. The pros and cons for both approaches will be presented at the end of this paper.

Physica A: Statistical Mechanics and its Applications, 2011

We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 periods. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.

Physical Review E, 2005

In a system containing a large number of interacting stochastic processes, there will typically be many non-zero correlation coefficients. This makes it difficult to either visualize the system's inter-dependencies, or identify its dominant elements. Such a situation arises in Foreign Exchange (FX) which is the world's biggest market. Here we develop a network analysis of these correlations using Minimum Spanning Trees (MSTs). We show that not only do the MSTs provide a meaningful representation of the global FX dynamics, but they also enable one to determine momentarily dominant and dependent currencies. We find that information about a country's geographical ties emerges from the raw exchange-rate data. Most importantly from a trading perspective, we discuss how to infer which currencies are `in play' during a particular period of time.

arXiv (Cornell University), 2016

There are many studies dealing with the analysis of similarity among currencies in foreign exchange market by using network analysis approach. In those studies, each currency is represented by a univariate time series of exchange rate return. This is the standard practice to analyze the underlying information in the foreign exchange market. In this paper, Escoufier's coefficient is applied to measure the similarity among currencies where each of them is represented by bivariate time series. Based on that coefficient, we analyze the topological structure of the currencies. An example of FOREX analysis will be presented and discussed to illustrate the advantages of coefficient.

In this paper, we propose a method for investigating the dynamics of markets. The goal is to segment/classify the time into intervals, where the market characteristics are homogeneous and at the same time to provide a description of each segment of the signal. We have combined a linear model and Fourier series to describe the market changes in long and short term, respectively. The method solves the time segmentation problem based on the Iso Level Algorithm (ILA), solving the EquiPartition problem (EP). Several market characteristics related to the used modelling efficiently summarize the behavior of the market in each segment. The whole methodology provides the critical times where the market behavior changes and a description of each estimated segment. We have successfully applied the proposed method on EURO-USD exchange rate of last ten years.

2011

We propose the average of weights of all links adjacent to each currency as a centrality measure. This measure, together with the existing centrality measures will be helpful in interpreting the network topology of currencies. A case study will be presented and discussed to illustrate the advantage of the proposed measure.

Physica A: Statistical Mechanics and its Applications, 2006

By analyzing the foreign exchange market data of various currencies, we derive a hierarchical taxonomy of currencies constructing minimal-spanning trees. Clustered structure of the currencies and the key currency in each cluster are found. The clusters match nicely with the geographical regions of corresponding countries in the world such as Asia or East Europe, the key currencies are generally given by major economic countries as expected. r

Mpra Paper, 2008

Previous studies have investigated the comovements of international equity markets by using correlation, cointegration, common factor analysis, and other approaches. In this paper, we investigate the stochastic structure of major euro and non-euro area stock market series from 1994 to 2006, by using cluster analysis techniques for time series. We use an interpolated-periodogram based metric for level and squared returns in order to compute distances between the stock markets. This method captures the stochastic dependence structure of the time series and solves the shortcoming of unequal sample sizes found for di¤erent countries. The clusters of countries are formed by the dendrogram and the principal coordinates associated with the sample spectrum for both the series of returns and volatilities. The empirical results suggest that the cross-country groups have become considerably more homogeneous with the introduction of the euro as an electronic currency. For reference, we also explore the pairwise correlations among the series.

2005

There is intense interest in understanding the stochastic and dynamical properties of the global Foreign Exchange (FX) market, whose daily transactions exceed one trillion US dollars. This is a formidable task since the FX market is characterized by a web of fluctuating exchange rates, with subtle inter-dependencies which may change in time. In practice, traders talk of particular currencies being 'in play' during a particular period of time -- yet there is no established machinery for detecting such important information. Here we apply the construction of Minimum Spanning Trees (MSTs) to the FX market, and show that the MST can capture important features of the global FX dynamics. Moreover, we show that the MST can help identify momentarily dominant and dependent currencies.