analysis can provide an important contribution to this work. although we now have better information about household debt, important pieces of the puzzle are still missing. the riksbank ’ s data collection was a first measure and there is no older, historical information on debts at the household level. and, as many others have said before me, we do not have any information on household wealth either. today, i intend to speak above all about the new results from the analysis of the riksbank ’ s data on the debts of individual ’ s and households and explain how these relate to other studies of household indebtedness. i also want to explain why the results should be viewed in the light of the vulnerabilities in the swedish financial system. let me begin, however, by saying a few words about why household debt is a cause of concern to the riksbank at all. international stakeholders, for example the imf, the oecd and the eu, have also been concerned. the risks associated with high household indebtedness have also been discussed at other central banks. the riksbank warned about these risks already prior to the financial crisis ( see, for example the speech made by stefan ingves on 4 october 2013 ). bis central bankers ’ speeches household debt can threaten sustainable growth the riksbank has long pointed out the risks associated with the development of household debt. when household debt is analysed, the debts are usually placed in relation to something else. as most households pay their borrowing costs using their current incomes rather than their assets, the debt ratio, that is debts in relation to disposable incomes, is often used as an illustrative measure of risk. 2 since 1995, the aggregate household debt ratio, that is total household debt as a percentage of disposable income, has increased from 90 to 174 per cent, and it is expected to continue to increase in the years immediately ahead. although the aggregate debt ratio has grown somewhat more slowly in recent years, it is still very high in an historical perspective ( see chart 1 ). chart 1. aggregate debt ratio per cent sources : statistics sweden and the riksbank this has led to the households becoming increasingly sensitive to shocks such as a loss of income, falling asset prices or rising interest rates. our primary concern is not that the households will cause the banks any major loan losses. as we have pointed out for some time, our assessment is that this risk is limited. however,

journal of central banking, 9 ( 1 ), pp. 3 - 39 and an isin - by - isin regression framework akin to d ’ amico, s. and king, t. b. ( 2013 ), “ flow and stock effects of large - scale treasury purchases : evidence on the importance of local supply ”, journal of financial economics, 108 ( 2 ), pp. 425 – 448. 8 to this end, for instance, an estimated term structure model is used following li, c. and wei, m. ( 2013 ), “ term structure modeling with supply factors and the federal reserve ’ s large - scale asset purchase programs ”, international journal of central banking, 9 ( 1 ), pp. 3 - 39. in this framework, yields are driven by three factors : the level and slope of the yield curve ( “ yield factors ” ) as well as a factor related to the central bank ’ s bond holdings ( “ quantity factor ” ). in quantifying the impact on bond yields, the model thus takes into account the effect of the central bank ’ s current and future expected bond holdings, including the impact of reinvestment. 9 euro area yields are proxied by the gdp - weighted long - term yield of the four largest euro area jurisdictions. the 10 - year yield impacts are obtained from a version of the li - wei ( 2013 ) model used at the federal reserve to 21 / 22 bis central bankers'speeches convert the soma portfolio of securities into yield impacts. impacts are shown for different app reinvestment scenarios, defined by the horizon indicated in the legend. the marginal impact of each additional year of reinvestment is given by the distance between the scenario curves. latest observation : february 2018. 10 praet, p. ( 2017 ), “ maintaining price stability with unconventional monetary policy measures ”, speech at the mmf monetary and financial policy conference, london, 2 october. 11 see the euro area bank lending survey, october 2017. 12 this chart shows the risk - neutral probability distribution function implied by two - year zero - coupon inflation options. these risk - neutral probabilities may differ significantly from physical, or true, probabilities. they are estimated on the basis of call ( “ caplets ” ) and put ( “ floorlets ” ) options with different strike rates on the ( threemonth lagged ) euro area hicp index excluding energy, food and tobacco, assuming black - scholes option