The global financial system is composed of many different financial markets on which a diverse set of assets are traded. Because there are so many assets traded on some markets it can sometimes be convenient to think about groupings of them. For example, shares are assigned to industry sectors based on the business activities of their companies. These sectors provide a useful tool for sorting and comparing different companies, and it can be insightful to compare the performance of stocks within the same sector to identify any that are under- (or over-) performing. For some markets, however, an external classification like this is not possible. An alternative approach is to group assets based on the behaviour of their prices. Two assets which have strongly correlated price changes (they increase and decrease in value at similar times) would belong to the same group and two assets which are weakly correlated belong to different groups. Groups identified in this way can be useful for several reasons but a familiar one is in the construction of diversified portfolios to minimize investment risk – see the “Can we spread the risk?” post.
Much prior work along these lines focused on equity markets, so we set out to investigate the group structure of the foreign exchange (FX) market. To do this, we represented the FX market as a network in which each node represented an exchange rate (such as the EURUSD rate which gives the number of US dollars that one receives in exchange for 1 euro) and each edge connecting pairs of exchange rates represented the strength of the correlations between those rates. A network like this is similar to a social network (such as a facebook network) in which each node represents a person and two people are linked if they are friends. A group in the exchange rate network (known as a community) then corresponded to a set of nodes that had stronger links to each other than they did to the rest of the network.
Importantly, the exchange-rate groups that we found changed through time depending on market conditions, so we introduced several techniques to track the changing relationships between the rates. Using this approach, we were able to uncover major trading changes that occurred in the FX market during the 2007-2008 credit crisis and to identify the relative importance of the different rates.
You can find out more about this work in the paper, “Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007–2008 credit crisis” Chaos 19, 1 (2009). Dan and Nick