charging a wholly different price. i remember talking to my friends in the insurance broking industry about it. one or two of them are here in the audience! first of all, was it true? secondly, if it was, why? and thirdly, if indeed there was an arbitrage opportunity then how could we unlock its value? i remember feelings of frustration. somehow we couldn ’ t quite see how to reconcile some of the essential differences, including regulatory and accounting distinctions. to say nothing of the language or jargon used in the two sectors. well, the rapid growth of the derivatives industry during the late 1980s changed all that! all sorts of new instruments for exploiting arbitrage opportunities like the one i described came into being. the different aspects of risk were unpicked and recombined. those developments depended partly on new information technology. and, together with securitisation the functional divide was crossed. that evolution provided colossal commercial opportunities and enhanced general economic value. i recall this because it ’ s a good example of how technical advances, some rocket science and market forces have changed the fortunes and capabilities of two great industries. and today, even though we don ’ t have full integration we certainly have greatly increased interdependence and linkages. just take the involvement of the insurance industry in credit risk transfer as an example. and just as with a lot of interdependencies you get network effects with undoubted economic benefits on the one hand, but potential vulnerabilities on the other. maybe i can start by making a few observations on the vulnerabilities - increased risk to firms or the overall financial system - that were less significant until the interconnections appeared. the starting point here will be to try to understand the nature of these vulnerabilities better, and their potential impact on the financial system. vulnerabilities : comparing banking and insurance my first observation relates to the different “ customs and practice ” of the two industries. in the banking world, obligations have historically tended to be for amounts which are clearly specified - either in money terms or according to some agreed formula - in advance. the event triggering delivery of the obligation is unambiguous. often it ’ s simply expiry of a set time period ; and the timetable for payment once the obligation becomes due is fixed ( and typically short ). in the non - life part of the insurance world, on the other hand the amount to be

past, is one striking way to get the point across. it plots a survey measure of gdp growth against the average vote for changes in bank rate, in basis points, on the mpc [ 5 ]. the pre - 2010 correlation is extremely tight and, once one ’ s allowed for it, nothing else – whether unemployment, wage growth or inflation itself – offers any further statistical help in explaining how policy rates behaved. chart 5 : monetary policy was very sensitive to output growth before the financial crisis, during the “ great stability ”, much less so after it sources : bank of england, s & p global and bank calculations. six - month rolling averages. during that pre - crisis period, when policymakers were confident about the stability of underlying productivity growth, and of the supply side more generally, this made sense. an acceleration in demand would lead to lower unemployment, albeit with some delay ; as long as the nairu is also stable, lower unemployment would necessarily, in time, indicate greater inflationary pressure, including faster wage growth ; and with inflation subject to few other disturbances, independently of demand, why wait until the end of this chain to act? you may as well tighten policy as soon as you see the economy strengthen and, symmetrically, loosen when output growth declines. in this happy environment, sometimes known as the “ divine coincidence ”, the policy that best stabilises economic growth is also the policy that offers the best chance of controlling inflation. the financial crisis marked the end of this “ great stability ” and with it this simple feedback rule for monetary policy. those days are long behind us. as chart 5 reveals, policy decisions have since been much less well correlated with gdp growth. instead – or least in addition – policy then started responding to more direct measures of the degree of slack in the economy, in particular the rate of unemployment. latterly, the mpc has also had to put weight on nominal indicators such as wage growth and other late - cycle measures of domestic inflation. shortly i ’ ll try and explain why this is. this will basically be a restatement of a point i made in a talk in the summer ( broadbent, 2023 ) but, to flesh it about a bit, i ’ ll chuck in the results of a simple model. this is designed to get across how policy should behave when there ’ s incomplete information about the various shocks hitting the economy. i ’ ll then take a closer look at the unemployment and wage data. if