in the united states before the war. however, it has become considerably more so as a result of the reduction in the supply of russian gas, which already began in 2021. gas is likewise expected to be more expensive in europe than in the united states in the medium term. this applies to end customers, too, and the same is true for electricity. for the time being, the impact on profitability can only be estimated in a cursory manner or using simulations. by contrast, the impact on price competitiveness can already be quantified ( see chart 8 ). in 2022, the relative rise in energy prices, up to its peak in september, had a marked impact on germany ’ s price competitiveness, reducing it by around 0. 9 %. [ 10 ] sectors with an above - average share of energy costs in production, such as the chemical and paper industries, were, of course, affected to a greater extent – and still are. however, in the same period, exchange rate developments benefitted german businesses, with the euro depreciating by 12. 5 % against the us ( united states ) dollar. this, in turn, improved germany ’ s price competitiveness by 1. 9 %. this temporary depreciation of the euro against the us ( united states ) dollar is likely to be due to the strong policy rate hikes by the federal reserve, which started earlier than in the euro area. the euro has since regained most of the lost ground. on average, the price competitiveness of the german corporate sector appears robust overall. however, energy - intensive industries have been disproportionately affected by the rise in energy prices, becoming less competitive. thanks to good earnings and financing conditions, enterprises should generally be in a good position to absorb the rise in energy prices. [ 11 ] however, all other things being equal, there is likely to be an incentive for energy - intensive industrial plants to tend to invest in countries where energy costs are comparatively low and will remain so in future. of course, simply looking at current energy costs doesn ’ t go far enough here. as decarbonisation is a global necessity, it is imperative to look at the costs of future energy. this means risks and opportunities for germany as an investment location. ladies and gentlemen, let us now turn to the third challenge facing the german economy, namely demographic developments. one concern is that demographic change limits our ability to create prosperity – as does the at least

. derivatives positions and net foreign currency positions are also imperfectly captured in global flows of funds data, particularly for companies. for example, over recent years many emerging market companies have borrowed cheaply in dollar - denominated instruments. but gauging the scale of that borrowing, and whether it is hedged either with derivatives or dollar income receipts, is a real data challenge. this is a significant gap because unhedged dollar borrowing is one potential fault - line in the international financial system at present ( bis ( 2014 ) ). ( b ) modelling once the necessary data is in place, a natural next question is how best to use this to understand the dynamics of the macro - financial system of systems. there are a number of potentially fruitful, if fledgling, avenues of research currently being pursued on that front. in making sense of the macro - financial system, it would be desirable to have a quantitative framework for understanding and evaluating interactions both within the financial system and between it and the wider economy. pre - crisis, such models were close to non - existent. indeed, many mainstream macro - economic models did not even contain a well - defined financial sector, much less interactions within it ( roger and vleck ( 2011 ) ). the bank of england was an early pioneer of an approach which placed the financial sector centre - stage, with its so - called ramsi ( risk assessment model of systemic institutions ) model ( burrows et al ( 2012 ) ). a stylised overview of ramsi is given in chart 16. ramsi embodies interactions between the elements of individual banks ’ balance sheets ( the microprudential layer ), between individual banks ( the macro - prudential layer ) and between the financial system and the economy ( the macro - economic layer ). as an example, ramsi embodies an interbank network which allows counterparty network contagion to propagate. it also contains a behavioural model of funding markets and banks ’ liquidity positions which can generate liquidity crisis externalities ( aikman et al ( 2009 ) ). there are also explicit behavioural links between macro - economic variables and bank balance sheets ( through arrears, defaults, losses given default etc ). these complex, non - linear bis central bankers ’ speeches channels of risk propagation mean the probability distributions generated from ramsi are fat - tailed ( chart 17 ). these ingredients, with rich interactions both within and across banks and between banks and the