دانلود رایگان مقاله ساختار وابستگی طلا از بازار ارز خارجی: دیدگاه شبکه

abstract

Tail dependence of financial entities describes when the price of one financial asset has an extreme fluctuation (e.g., price sharply rises or falls), the degree of its effect on the price fluctuation of another asset. Under the background of the global financial crisis, tail dependence structure of financial entities plays an important role in financial risk management, portfolio selection, and asset pricing. In this paper, we propose a concept of tail dependence networks to investigate the tail dependence structure of the foreign exchange (FX) market. Lower- and upper-tail dependence networks for 42 major currencies in the FX market from 2005 to 2012 are constructed by combing the symmetrized Joe-Clayton copula model and two filtered graph algorithms, i.e., the minimum spanning tree (MST) and the planar maximally filtered graph (PMFG). We also construct the tail dependence hierarchical trees (HTs) associated with the MSTs to analyze the currency clusters. We find that (1) the two series of lower- and upper-tail dependence coefficients present different statistical properties; (2) the upper-tail dependence networks are tighter than the lower-tail dependence networks; and (3) different currency clusters, cliques and communities are respectively found in the two tail dependence networks. The key empirical results indicate that market participants should consider the different topological features at different market situations (e.g., a booming market or a recession market) to make decisions on the investing or hedging strategies. Overall, our obtained results based on the tail dependence networks are new insights in financial management and supply a novel analytical tool for market participants.

. Conclusions and future works

In this paper, we examine the tail dependence structure of a set of 42 currencies in the FX market in the range from the beginning of 2005 to the end of 2012. Based on the SJC copula model and the MST and PMFG approaches, we construct lower- and upper-tail dependence networks to analyze the tail dependence structure of the FX market. In practice, we first employ the AR(1)-GARCH(1,1)-t model to characterize marginal distributions of FX rates returns. Then, we adopt the SJC copula model to compute the lower- and upper-tail dependence coefficients between each pair of FX rates. Next, we build the lower- and upper-tail dependence matrices and transform the two tail dependence matrices to two distance matrices. Finally, we use the MST and PMFG method to construct the tail dependence networks and study their topological properties, the cluster and community structure. Meanwhile, we also investigate the lower- and uppertail dependence HTs associated with the MSTs. Some basic findings for investigating tail dependence structure of the FX market are summarized as follows.