Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach

Abstract

Based on a time-varying copula approach and the minimum spanning tree (MST) method, we propose a time-varying correlation network-based approach to investigate dynamics of foreign exchange (FX) networks. In piratical terms, we choose the daily FX rates of 42 major currencies in the international FX market during the period of 2005–2012 as the empirical data. The empirical results show that (i) the distributions of cross-correlation coefficients (distances) in the international FX market (network) are fat-tailed and negatively skewed; (ii) financial crises during the analyzed period have a great effect on the FX network’s topology structure and lead to the US dollar becoming more centered in the MST; (iii) the topological measures of the FX network show a large fluctuation and display long-range correlations; (iv) the FX network has a long-term memory effect and presents a scale-free behavior in the most of time; and (v) a great majority of links between currencies in the international FX market survive from one time to the next, and multistep survive rates of FX networks drop sharply as the time increases.

Correlation structure and dynamics of international real estate securities markets: A network perspective

“…Wang et al [9] and Keskin et al [37], we employ Kruskal's [38] algorithm to construct the MST network. The construction procedure of the MST is presented as follows.…”

“…It is crucial information not only for understanding and describing the interaction behavior of complex financial systems but also for financial activities ranging from asset allocation and investment portfolio to risk management [2]. There is a huge body of literature on investigating the correlation structure of assets in financial markets, such as stock markets [3][4][5][6], foreign exchange markets [7][8][9], and international equity markets [10,11]. Furthermore, a high percentage of the literature concentrates on the study of correlation structure of international real estate securities markets (see, e.g., a review by Worzala and Sirmans [12] and references therein).…”

A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

“…Source: Own study 4.5. Average Path Lentgth (APL), (Wang et al, 2014) This indicator is defined as the average number of steps along the shortest paths for all possible pairs of network nodes. It measures the effectiveness of information flow or mass transport in a given network.…”

Tail dependence networks of global stock markets

“…First, financial time series is assumed to follow normal distribution when the correlation is calculated by the Pearson correlation coefficient, whereas numerous studies have shown that the return on financial markets is not subject to normal distribution (Bae, Karolyi, & Stulz, 2003;Durante, Foscolo, Jaworski, & Wang, 2014;Wang, Xie, Zhang, Han, & Chen, 2014). However, this method has some shortcomings.…”

“…However, this method has some shortcomings. First, financial time series is assumed to follow normal distribution when the correlation is calculated by the Pearson correlation coefficient, whereas numerous studies have shown that the return on financial markets is not subject to normal distribution (Bae, Karolyi, & Stulz, 2003;Durante, Foscolo, Jaworski, & Wang, 2014;Wang, Xie, Zhang, Han, & Chen, 2014). Second, the Pearson correlation coefficient only reveals linear correlations, and it ignores the heterogeneity of financial data at different times; therefore, it is difficult to accurately measure the tail correlation in a booming market and a recession market .…”