بحران ارز و تکامل بازار ارز: شواهدی از حداقل درخت پوشا

We examined the time series properties of the foreign exchange market for 1990–2008 in relation to the history of the currency crises using the minimum spanning tree (MST) approach and made several meaningful observations about the MST of currencies. First, around currency crises, the mean correlation coefficient between currencies decreased whereas the normalized tree length increased. The mean correlation coefficient dropped dramatically passing through the Asian crisis and remained at the lowered level after that. Second, the Euro and the US dollar showed a strong negative correlation after 1997, implying that the prices of the two currencies moved in opposite directions. Third, we observed that Asian countries and Latin American countries moved away from the cluster center (USA) passing through the Asian crisis and Argentine crisis, respectively.

During the past decade, the application of statistical physics and complexity to financial market data has attracted much interest. Network analysis, in particular, has been playing a leading role among the related techniques because the representation of a financial market as a network topology provides efficient ways of understanding its structural properties [1], [2], [3] and [4]. In the stock exchange market, stock networks generated by trading activity represent the similarities between stocks and have significant implications for portfolio optimization [5] and [6]. Several attempts have also been made to apply network theory to the analysis of the foreign exchange market. Mizuno et al. [7] analyzed foreign exchange market data and derived a hierarchical taxonomy of currencies constructing a minimum spanning tree (MST). The identified currency clusters matched nicely with the corresponding countries’ geographical regions around the world. McDonald et al. [8] developed a network analysis of currency correlations in the foreign exchange market using the MST approach, and showed that global foreign exchange dynamics such as dominant and dependent currency structures can be found in MSTs. However, previous research into the foreign exchange market has shed little light on the time series properties of currency networks for currency crises. It is important to examine the time series properties of currency networks because network shapes vary as time goes on. Whether the time series have typical properties in response to currency crises is also a serious question. In this paper, our particular interest lies in the application of network theory to the analysis of the foreign exchange market. We examine the network properties of the market and interpret the network topology in financial terms. Analysis of the time series properties of currency networks in relation to the history of currency crises is the goal of this paper. The remainder of the paper is organized as follows. In Section 2, we present our data set and discuss some of its specific aspects. In Section 3 we describe the methods that we use to construct the MST derived from the entire sample of data. In Section 4 we present the results obtained from analysis of the data set, and in Section 5 we draw our conclusions.

Currency crises became common during the 1990s when globalized financial capitalism flourished. We have observed that the currencies of nations with heavy foreign debts in emerging markets severely depreciated against major currencies such as the US dollar when the global credit market tightened up. This paper has focused on a network-based analysis of currency crises in foreign exchange market. We have examined the time series properties of foreign exchange market and analyzed them in relation to currency crises, extracting some meaningful observations. First, the mean values of the correlation coefficients decreased around most currency crises whereas the normalized tree length increased. These patterns appear repeatedly in the ERM crisis (Table 3), the Asian crisis (Table 4), the Argentine crisis (Table 5), and the US sub-prime crisis (Table 6). In particular, the mean correlation coefficient dropped dramatically passing through the Asian crisis, and remained at the lowered value. The Asian crisis occurred as the financial market experienced rapid globalization and liberalization in the 1990s. However, the mean correlation drop pattern for currency crisis is ambiguous. The steep drop of correlation coefficient is found before the date Asian crisis begins (Fig. 5), which is distinct from the ERM crisis where the drop is found after the crisis (Fig. 3) starts. As most currency crises are the outcomes of various continuing economic events, it is difficult to pinpoint the beginning and ending dates of crises. In this paper, we use the starting dates of crises in Refs. [9] and [10], and the multiple issues of Wall Street Journal. Second, the correlation between the Euro and the US dollar dramatically turned negative from 1997, which implies that the prices of the two currencies moved in opposite directions. Finally, we observed that Asian countries and Latin American countries moved away from the cluster center (USA) of MSTs going through the Asian crisis and Argentine crisis, respectively. This implies that the Asian (Latin American) countries, belong to a cluster of MST before the Asian (Argentine) crisis, tend to move farther away from the cluster center of MST after the crisis. North American countries such as Canada and Mexico also moved away from USA through the US subprime crisis. However, these patterns are not shown in the ERM crisis. These tentative results await refinement and correction in the light of further research. It would be dangerous to generalize that the above-mentioned phenomena will be observable during all currency crises. As we have seen, the Mexican crisis and the Russian crisis, which were well known for their contagious nature on the global financial market, did not show obvious trends. The methods used in this paper have the following limitations. For the validity of correlation analysis between currencies, we need the assumption that the data from currency markets are mutually independent normal random variables. The Pearson coefficient of linear correlation in Eq. (2) may not work well for non-random, strongly correlated data. The financial data in practice, including ours, are likely to be correlated and show non-Gaussian behavior. In addition, although MST analysis is beneficial in capturing the general views and changes of international currency market, the relatively simple tree diagram structure in MST is definitely limited in not being able to represent the detailed characteristics of international currency markets, which are complex and dynamic. For more accurate analysis, we should pay attention to these limitations and improve the standard techniques. Our work can suggest elementary ideas about the relationship between currency crises and the properties of the currency network. The results of this study could be developed into the research on an early warning system (EWS) for financial crises. With the growing frequency of financial crises, many researchers and practitioners have tried to develop a better EWS. It remains for future work to investigate whether the performance of EWS can be improved by considering currency network properties in addition to standard macroeconomic factors.