See Minoiu and Reyes (2013) for more information  BIS central bankers’ speeches  9  correlations is, in their own way, a rather imperfect metric of cross-system interaction. Nonetheless, they suggest these interactions are, in general, both important and strong. A final, indirect and reduced-form, piece of evidence on the complexity of the macro-financial system comes from looking at the statistical distribution of behaviour in these systems. For other complex systems, physical, natural, biological and social systems, this statistical distribution is known to be highly non-normal (Newman (2005)). Indeed, many of these systems are found to be Power-law distributed, with much fatter tails than would be expected from a normal distribution. To what extent are these statistical properties evident in macro-economic and financial timeseries data? Chart 14 looks at the statistical distribution of GDP growth, credit growth, equity prices and rice prices, using a long time-series covering several hundreds of year. These densities have been transformed such that, if the underlying distribution is normal, the dots trace a straight line. Any deviation at the extremities is evidence of a fat-tailed distribution. It is clear from Chart 14 that all four series exhibit a significant degree of fat-tailedness, considerably more so than a normal distribution. This is consistent with economic and financial systems, locally and globally, exhibiting complex system of systems properties. For example, a four-standard deviation event – a true catastrophe – would under a normal distribution be expected to occur roughly every 15,000 years.
Under the estimated distributions for economic and financial systems, such an event would occur every 10 to 15 years. Implications for policy design So what might this mean for the design of the policy architecture? Let me mention three areas where clear progress has been made since the crisis, but where more may be needed in the period ahead if we are to manage effectively this complex, macro-financial system of systems: data, modelling and policy design. (a)  Data  Monitoring risk in a system of systems requires data on each of the sub-systems, often at a high level of granularity. Often in response to crisis, efforts have been made over time to improve data on economic and financial systems. For example, there have been successive waves of improvement to statistics on international banking flows, starting in the 1970s. In response to the latest crisis, renewed efforts have been made to fill data gaps and to improve the degree of disclosure by banks of their balance sheet positions (FSB (2012)). As one example, data on banks’ cross-border assets and liabilities only distinguish between banks and non-banks. In practice, a richer sectoral decomposition of these data is often desirable to understand interactions between different parts of the financial sector and between it and the wider economy (Hills and Hoggarth (2013), Lane (2014)). For example, the dollar funding crisis which faced European banks in 2011/12 involved a complex string of cross-border financing.