An alternative way to track the hot money in turbulent times

During recent years, networks have proven to be an efficient way to characterize and investigate a wide range of complex financial systems. In this study, we first obtain the dynamic conditional correlations between filtered exchange rates (against US dollar) of several countries and introduce a time-varying threshold correlation level to define dynamic strong correlations between these exchange rates. Then, using evolving networks obtained from strong correlations, we propose an alternative approach to track the hot money in turbulent times. The approach is demonstrated for the time period including the financial turmoil of 2008. Other applications are also discussed.