An application of minimal spanning trees and hierarchical trees to the study of Latin American exchange rates

Erick Limas

doi:10.3934/jdg.2019010

Article Contents

2019, Volume 6, Issue 2: 131-148. Doi: 10.3934/jdg.2019010

This issue Previous Article An application of a dynamical model with ecological predator-prey approach to extensive livestock farming in uruguay: Economical assessment on forage deficiency Next Article Economic evolution and uncertainty: Transitions and structural changes

An application of minimal spanning trees and hierarchical trees to the study of Latin American exchange rates

Erick Limas

Institute for Latin American Studies, School of Business & Economics, Freie Universität Berlin, Rudesheimer Str. 54, 14197 Berlin, Germany

Received: December 2018

Revised: April 2019

Published: April 2019

Funding by the German Research Foundation (DFG) is gratefully acknowledged.

Abstract / Introduction Full Text(HTML) Figure(6) / Table(2) Related Papers Cited by

Abstract

This paper analyzes a group of nine Latin American currencies with the aim of identifying clusters of exchange rates with similar co-movements. In this work the study of currency relationships is formulated as a network problem, where each currency is represented as a node and the relationship between each pair of currencies as a link. The paper combines two methods, Symbolic Time Series Analysis (STSA) and a clustering method based on the Minimal Spanning Tree (MST), from which we obtain a Hierarchical Tree (HT). Symbolic Time Series Analysis consists in the transformation of a given time series into a symbolic sequence with the aim of identifying patterns in the set of data. The Minimal Spanning Tree condenses the core information on the global structure of the network and its main advantage is that it greatly simplifies comparisons by dramatically reducing the number of elements that must be compared. We identify two main clusters in the currency network, as well as specific currencies that function as transmission channels between clusters. Using data regarding the degree of financial liberalization, as well as the distinction between inflation targeting (IT) and non-IT countries, the analysis suggests that the obtained taxonomy is economically relevant.

Keywords:

Mathematics Subject Classification: Primary: 91C20, 91B80; Secondary: 91B55.

Citation:

Full Text(HTML)

Figure 1. Illustration of symbolic encoding: Variation of the exchange rate of the Mexican peso against the U.S. dollar. The horizontal line represents the symbol partition; data below the partition are represented by 0, and data above the partition are represented by 1. In this case, the frontier level is the trend of the series, $ \mu = 0.0025 $. Then, the original data time series is represented by the symbol sequence S = 100100101

Download: Full-size image PowerPoint slide

Figure 2. Minimal Spanning Tree (left) and Hierarchical Tree (right). Period: 2007

Download: Full-size image PowerPoint slide

Figure 3. Minimal Spanning Tree (left) and Hierarchical Tree (right). Period: 2008-2010.

Download: Full-size image PowerPoint slide

Figure 4. Minimal Spanning Tree (left) and Hierarchical Tree (right). Period: 2011-2014

Download: Full-size image PowerPoint slide

Figure 5. Minimal Spanning Tree (left) and Hierarchical Tree (right). Period: 2015-2017

Download: Full-size image PowerPoint slide

Figure 6. Financial openness: the Chinn-Ito index (2007-2016 average). Source: [14]

Download: Full-size image PowerPoint slide

Table 1. Countries, currencies and three-letter codes

| Show Table

DownLoad: CSV

Table 2. Monetary Policy Framework. Argentina maintains a de facto exchange rate anchor to the U.S. dollar. Uruguay has an inflation target regime with monetary aggregates control. Source: [28]

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	R. Almeida, Financial flows and exchange rates: Challenges faced by developing countries, IPC-IG, 97 (2012), 1-20.
[2]	R. Andrade and D. Prates, Exchange rate dynamics in a peripheral monetary economy, Journal of Post Keynesian Economics, 35 (2013), 399-416. doi: 10.2753/PKE0160-3477350304.
[3]	N. Boccara, Modeling Complex Systems, Springer-Verlag, New York, 2004.
[4]	J. Brida, D. Matesanz and W. Risso, Dynamical hierarchical tree in currency markets, Fundación de las Cajas de Ahorro (FUNCAS), 332 (2007).
[5]	J. Brida, D. Gómez and W. A. Risso, Symbolic hierarchical analysis in currency markets: An application to contagion in currency crises, Expert Systems with Applications, 36 (2009), 7721-7728.
[6]	J. Brida, D. Matesanz and W. Risso, Estructura jerárquica y dinámica en los mercados cambiarios latinoamericanos, Investigación Económica, 68 (2009), 115–146.
[7]	J. Brida, D. Matesanz and M. N. Seijas, Network analysis of returns and volume trading in stock markets: The Euro Stoxx case, Physica A, 444 (2016), 751-764.
[8]	J. Brida, D. Matesanz and M. N. Seijas, Debt and growth: A non-parametric approach, Physica A, 486 (2017), 883-894. doi: 10.1016/j.physa.2017.05.060.
[9]	J. Brida, S. London, M. Rojas, Una aplicación de los árboles de expansión mínima y árboles jerárquicos al estudio de la convergencia interregional en dinámica de regímenes, Revista de Métodos Cuantitativos para la Economía y la Empresa, 15 (2013), 3–29.
[10]	J. Brida and E. Limas, A post Keynesian framework of exchange rate determination: a dynamical approach, Dynamics of Continuous, Discrete and Impulsive Systems, 25 (2018), 409-426.
[11]	G. Calvo and C. Reinhart, Fear of floating, Quarterly Journal of Economics, 117 (2002), 379-408.
[12]	E. Carsamer, Exchange Rate Co-Movement and Volatility Spill Over in Africa, Ph.D thesis, School of Development Economics, National Institute of Development Administration, 2015.
[13]	M. Chinn and H. Ito, Capital Account Liberalization, Institutions and Financial Development: Cross Country Evidence, Working Paper No. 8967. Cambridge, MA: National Bureau of Economic Research, 2002.
[14]	M. Chinn and H. Ito, KAOPEN Index, 2017. Available from: http://web.pdx.edu/~ito/Chinn-Ito_website.htm.
[15]	L. F. de Paula, B. Fritz and D. Prates, Keynes at the periphery: Currency hierarchy and challenges for economic policy in emerging economies, Journal of Post Keynesian Economics, 40 (2017), 183-202. doi: 10.1080/01603477.2016.1252267.
[16]	C. Ebeke and A. Fouejieu, Inflation Targeting and Exchange Rate Regimes in Emerging Markets, IMF Working Paper, 228 (2015).
[17]	S. Edwards, The relationship between exchange rates and inflation targeting revisited, NBER Working Paper, 12163 (2006), 1-47. doi: 10.3386/w12163.
[18]	X. Feng and X. Wang, Evolutionary topology of a currency network in Asia, International Journal of Modern Physics C, 21 (2010), 471-480. doi: 10.1142/S0129183110015269.
[19]	D. Fenn, M. Porter, P. Mucha, M. McDonald, S. Williams, N. Johnson and N. Jones, Dynamical clustering of exchange rates, Quantitative Finance, 12 (2012), 1493-1520. doi: 10.1080/14697688.2012.668288.
[20]	J. Frankel and D. Xie, Estimation of de facto flexibility parameter and basket weights in evolving exchange rate regimes, NBER Working Paper, 15620 (2009), 1-22. doi: 10.3386/w15620.
[21]	J. Frankel and S. Wei, Yen bloc or dollar bloc? exchange rate policies of the east asian economies, in Macroeconomic Linkages (eds. I. Takatoshi and A. Krueger), Chicago: University of Chicago Press, (1994), 295–329.
[22]	J. Frankel and S. Wei, Assessing Chinas Exchange Rate Regime, Economic Policy, 51 (2007), 575-614.
[23]	J. Frankel and S. Wei, Estimation of de facto exchange rate regimes: Synthesis of the techniques for inferring flexibility and basket weights, IMF Staff Papers, 55 (2008), 384-416. doi: 10.3386/w14016.
[24]	A. Grski, S. Drozdz and J. Kwapien, Scale free effects in world currency exchange network, The European Physical Journal B, 66 (2008), 91-96. doi: 10.1140/epjb/e2008-00376-5.
[25]	A. Grski, J. Kwapien, P. Oswiecimka and S. Drozdz, Minimal spanning tree graphs and power like scaling in forex networks, Acta Physica Polonica A, 114 (2008), 531-538. doi: 10.12693/APhysPolA.114.531.
[26]	R. Henning, Choice and coercion in east asian exchange rate regimes, Working Paper Peterson Institute for International Economics, 12 (2012), 22pp. doi: 10.2139/ssrn.2151545.
[27]	R. Hill, International Comparisons Using Spanning Trees, in International and Interarea Comparisons of Income, Output, and Prices (eds.A. Heston and R. Lipsey), Chicago: University of Chicago Press, (1999), 109–120.
[28]	IMF, Annual Report on Exchange Arrangements and Exchange Restrictions, 2015.
[29]	A. Kaltenbrunner, Currency Internationalisation and Exchange Rate Dynamics in Emerging Markets: A Post Keynesian Analysis of Brazil, Ph.D thesis, University of London, England, 2011.
[30]	L. Kaufman and P. Rousseeu, Finding Groups in Data. An Introduction to Cluster Analysis, Wiley-Interscience, New York, 1990. doi: 10.1002/9780470316801.
[31]	B. Keddad, How do the renminbi and other east asian currencies co-move?, Journal of International Money and Finance, 91 (2019), 49-70. doi: 10.1016/j.jimonfin.2018.11.003.
[32]	M. Keskin, B. Deviren and Y. Kocakaplan, Topology of the correlation networks among major currencies using hierarchical structure methods, Physica A: Statistical Mechanics and its Applications, 390 (2011), 719-730. doi: 10.1016/j.physa.2010.10.041.
[33]	J. Kruskal, On the shortest spanning subtree of a graph and the traveling salesman problem, Proceedings of the American Mathematical Society, 7 (1956), 48-50. doi: 10.1090/S0002-9939-1956-0078686-7.
[34]	J. Kwapien, S. Gworek, S. Drozdz and A. Grski, Analysis of a network structure of the foreign currency exchange market, Journal of Economic Interaction and Coordination, 4 (2009), 55-72. doi: 10.1007/s11403-009-0047-9.
[35]	J. Kwapien, S. Gworek and S. Drozdz, Structure and evolution of the foreign exchange networks, Acta Physica Polonica B, 40 (2009).
[36]	X. Li, How do exchange rates co-move? A study on the currencies of five inflation-targeting countries, Journal of Banking and Finance, 35 (2011), 418-429.
[37]	Y. Mai, H. Chen and S. Li, Currency co-movement and network correlation structure of foreign exchange market, Physica A: Statistical Mechanics and its Applications, 492 (2018), 65-74. doi: 10.1016/j.physa.2017.09.068.
[38]	R. Mantegna, Hierarchical structure in financial markets, Eur. Phys. J. B, 11 (1999), 193-197. doi: 10.1007/s100510050929.
[39]	R. Mantegna and H. Stanley, An Introduction to Econophysics, Cambridge University Press: Cambridge, UK, 2000.
[40]	D. Matesanz and G. Ortega, Network analysis of exchange data: Interdependence drives crisis contagion, Quality & Quantity, 48 (2014), 1835-1851. doi: 10.1007/s11135-013-9855-z.
[41]	M. McDonald, O. Suleman, S. Williams, S. Howison and N. Johnson, Detecting a currency dominance or dependence using foreign exchange network trees, Physical Review E, 72 (2005), 46-106.
[42]	T. Mizuno, H. Takayasu and M. Takayasu, Correlation networks among currencies, Physica A: Statistical Mechanics and its Applications, 364 (2006), 336-342. doi: 10.1016/j.physa.2005.08.079.
[43]	M. Naylor, L. Rose and B. Moyle, Topology of Foreign Exchange Markets using Hierarchical Structure Methods, 2006.
[44]	M. Reovsk, D. Horvth, V. Gazda and M. Sinikov, Minimum spanning tree application in the currency market, Ronk, 21 (2013), 21-23.
[45]	A. Subramanian and M. Kessler, The Renminbi Bloc is Here: Asia Down, Rest of the World to Go?, Working Paper 12-19, Peterson Institute for International Economics, 2013.
[46]	G. Wang, C. Xie, J. Chen and S. Chen, Statistical properties of the foreign exchange network at different time scales: evidence from detrended cross-correlation coefficient and minimum spanning tree, Entropy, 15 (2013), 1643-1662. doi: 10.3390/e15051643.
[47]	I. Yu, K. Fung and C. Tam, Assessing financial market integration in Asia equity markets, Journal of Banking and Finance, 34 (2010), 2874-2885.