Topological Properties of Network of Global Currency Exchange Rates
Sushil Kumar1, Sunil Kumar2, Imran Khan3, Pawan Kumar4
1Sushil Kumar*, School of Basic & Applied Sciences, K R Mangalam University, Gurugram, Haryana, India-122103. Department. of Physics, Hansraj College, University of Delhi, Delhi-7, India.
2Sunil kumar,Ramjas College, University of Delhi, Delhi-7, India.
3Imran Khan,Ramjas College, University of Delhi, Delhi-7, India.
4Pawan Kumar, School of Basic & Applied Sciences, K R Mangalam University, Gurugram, Haryana, India-122103.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 5476-5479 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6940018520/2020©BEIESP | DOI: 10.35940/ijrte.E6940.018520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We study time series of exchange rates of 37 global currencies expressed in terms of US dollars. We take US dollar is as the base currency because it is one of dominant currencies, which is used almost in 66 countries as their currency. The average volatility is computed from returns using overlapped rolling window technique. To study the effects of crisis on the structure and dynamics, we consider three sub periods; before crisis, during crisis and after crisis. Different statistical properties and network properties in three sub periods. From analysis of currency network at different thresholds, we find change in the structure of network in the period of crisis. We find the highly correlated and weakly correlated currencies in the calm and crisis period using threshold networks, which can help the investors in portfolio management. The group of most correlated currencies in the crisis period is different from that in before and after crisis period. Different centrality measures can differentiate the currencies according to their geographical location.
Keywords: Econophysics, correlation, random matrix theory, threshold networks, centralities measures
Scope of the Article: Threshold networks