These balance sheets are scaled relative to GDP at two dates, 1978 and 2011. The changes over time in the scale and pattern of intermediation are striking. In 1978, the UK banking system was dominated by foreign banks. Banks’ assets summed to a little over 100% of GDP. Non-bank intermediaries, meanwhile, accounted for little more than 50% of GDP and were dominated by traditional real-money investors, such as pension funds and insurance companies. The Bank of England’s balance sheet was a mere 6% of GDP. Fast forwarding to 2011, this flow of funds picture has been transformed. The balance sheets of ultimate asset-owners/liability-holders has risen from around 200% of GDP to around 600% of GDP – a threefold rise. The scale and pattern of intermediation between these sectors has also been transformed. The banking sector has also risen substantially over this period. UK universal banks now dominate the landscape, accounting for 80% of total banking assets. They are the new “super-spreaders” of the UK financial sector. Non-bank intermediation has also seen a dramatic scaling-up of its balance sheets, rising sixfold to 300% of GDP. Within this, growth has been particularly strong within the nontraditional sectors (such as hedge funds and unauthorised funds) and among new types of vehicle (such as ETFs and private equity funds). These are a new strain of potential “superspreader”. The Bank of England’s balance sheet has also grown rapidly, rising to over 25% of GDP, largely courtesy of QE.
For example, they suggest that viewing risk through the lens of a single layer is likely to provide a significantly distorted picture of the true risk distribution,  3  Which are based on one million replications of the data generating process. 4  Technically, this is done using the so-called Gaussian copula. 5  With zero correlation between the two distributions, Chart 1a would be a set of concentric circles. 6  The t-distribution has fatter tails than the normal. In the simulations, we assume 5 degrees of freedom. 4  BIS central bankers’ speeches  with the probability of tail events materially under-estimated. As an example of that, Chart 1f looks at the unconditional and conditional distributions of one of the sub-systems in Chart 1c. The conditional distribution is conditioned on outcomes in the second layer lying in the lower half of the distribution. A risk manager looking at the unconditional distribution (a single layer perspective) would significantly under-estimate the true tail risk they were facing. This has direct parallels with the pre-crisis situation, when the actions of individual risk managers at banks, and individual supervisors of those banks, ignored the role of systemic risk when gauging individual firm risk. This led to a material under-estimation of individual firm risk. The micro-prudential microscope was wrongly focussed. Ignoring a layer, or sub-system, is likely to be equally distorting. That can be seen by comparing the risk distributions in Chart 1d (two layers) and 1e (three layers).