 Okay, so welcome back to the session. We're moving then to the second paper of it on climate stress testing, bank lending, and the transition to the carbon neutral economy. This will be presented by Hynn-Guen from the Halle Institute for Economic Research. And the discussant will be Zaharias Zautner from University of Zurich. So without further ado, I give the floor to Hynn. Hello, everyone. Thank you so much for having me here. And this is my great pleasure to present our paper, which is still really young on climate stress, bank lending, and the transition to the carbon neutral economy. Actually, the first time we presented this paper was also in this room a few months ago. So today you see an updated version of the paper that we presented three months ago. So this is a joint work with Larissa Fuchs from University of Cologne. I'm from Iveha Halle and University of Vienna. Chiang Nguyen and Klaus Scheck from University of Bristol. So if you browse through, like, newspapers, now you will see a lot of discussion about how banks price climate and recent risk, whether they are responsible for their borrowers who are polluting the environment. So there's also a lot of discussion in the academic literature that show bank did do so. So they do so by, for example, pricing the brownness in the loan contract. Or they could also do it by, for example, reducing loans to those brown borrowers. However, the results really mix because not all banks have the same preferences. Also, not all banks are well-prepared for the green transition. So we also have this, like, kind of the keynote speech also by Maria Suntagianetti a few months ago that she actually showed that a lot of banks just appear to be green, but they do not really support the grand transition. So essentially what they do is that they claim that they take into account pricing mechanism for the brown loans, but in fact they keep lending to the brown borrowers. And essentially in this paper, we will show that whether the bank supervisors could influence the bank decision to support the green transition. And we also look at whether reducing lending is really an optimal choice or actually increasing lending to those brown borrowers with the purpose that those brown borrowers will then chance it to be greener would be a better option. So when we look at the initiative from bank supervisors between 2007 until 2019, we see that there's a huge increase in the awareness of central banks. So the blue line there that you see are the central banks that register to be green network membership. So as of 2019, we have 50 central banks around the world register for that and showing also a skip in essentially after the Paris Agreement. So in this paper, we have really two simple research questions. We use the French climate threat test of 2020 as a natural experiment. We then combine that information with the carbon emission of borrowers and the borrower environmental performance from refinery. And also we use syndicated loans from deal scan. So the first question is that we ask, how do climate threat test affect credit supply and the cost of credit for brown borrowers? We will show you that first of all, without climate threat test, there's really limited evidence what the bank do. We find that they could reduce credit and ask for higher interest rate, but the results are quite fragile. However, after they participated in the climate threat test, they then learn about the information, how to evaluate their brown borrowers, and then they essentially also learn about the model, what could happen when the central bank do some action that link their loan portfolios to the essentially cut bonds in recent weeks. So there we find that threat tested bank actually are better informed and they instead of reducing the loan volumes, they actually increase the loan volume for those brown borrowers. And we also find that they at the same time charge higher interest rate because of the risk involved. And later on, we then link this result to the borrower level to ask, so after the borrower received this additional lending from those threat tested bank, what do they do? We see that brown borrowers do show some evidence that they improve, first of all, they're more likely to commit to carbon emission reduction targets. They have products that are classified as environmental friendly products. So you can think of like go to the real market and you see some product with like the sticker. This is produced from for example, recycle materials. Those are the kind of products that the borrowers reported that they have an offer. But when we look at the longer term kind of like behaviors, for example, whether there are some evidence on the direct reduction in carbon emission or whether they try to green their supply chain network, for example, by terminating relationship with brown suppliers and instead switch to the more responsible one or if they actually take into account the criteria related to the environment when they saw their materials. However, there we did not find so much evidence. So just some institutional background, I see that the ECB also did a bit of climate threat test with the kind of top down approach. So from last year, the ECB did this exercise and showed that we have 65% of borrowers that generate the net interest income mainly for the banks are from actually carbon intensive industries. For the French climate threat test here, this is more of a kind of bottom up approach. So essentially the central banks show their scenarios for how much carbon price could increase over time depending on different scenarios. And then all the banks that participated in the climate threat test, they could use their own data and calculate and calibrate how much losses would that be depending on each of the scenarios that got carbon prices raising. So if you see the figure here with three scenarios between 2020, 2050, so they take quite a long term approach and see the carbon price it evolved in 30 years. So the black line show very general increase, so it's not so sharp increase in carbon prices, but the kickoff date is quite earlier compared to the other scenarios. In the delay transition, which is shown in the yellow line, this means that we did not increase carbon prices so early, but we started with some small step at some point because those climate change issues get bigger. So for example, because we have this higher temperature resulted from the negative externality of carbon emission and we then at that point of 2030 realize that we need to do something else. So then by that time the carbon prices need to increase even higher compared to the black scenarios of the baseline and we see a big increase there. And finally in the accelerated transition, we essentially expect the worst to happen and the carbon prices increase already around 2025 and reach around 1000 USD over a ton of carbon. So with these three scenarios, there was essentially nine banking groups in France. They voluntarily participated in this threat test and then they estimate the losses from those scenarios. So just to be make, I just wanted to make sure that the threat test actually they do consider physical risk from natural disaster as well, but for banking data, they did not have so much data to really estimate that. So the physical risk was estimated more for the insurance group of institution whereas those climate transition risks are mainly done for the banking groups that participated in this. So here this is the timeline that how this climate threat test works. They did it in between June 2020 until April 2021 and then there's two approach with the dynamic balance sheet. There's a long-term approach and then they also use the static balance sheet which is just look at the loan portfolio of banks as of 2020 and then estimate the losses when carbon prices increasing. So this climate threat test actually do not really identify violators like the general climate threat test. So if you think of the basic, the most traditional climate, the threat test that the ECB do is for example, when the interest rate increase or decline by let's say 250 basis point, what happened to the bank? But in this threat test, they just look at the carbon price and they just serve as the kind of information, production products. So essentially the banks that participated in this threat test, they learn what would be the kind of model that the central bank look for when they estimate the losses for the bank and also the information on what kind of detail on carbon emission borrowers that the bank need to collect so that they can better estimate the expected loss. In the data, we have all the EU denominated syndicated loans from French borrowers between 2016 quarter one until 2023 quarter two from deal scan. And these loans are provided to French borrowers but both by French and foreign banks. We exclude all the financial firms in the data and we care mainly about the lending decision of lead arrangers in the syndicated lending market. So essentially for each syndicate, we have like one usually one to two lead arranger and then we have a lot of participant banks and here because the decision mainly from the lead arranger so we look through the decision of them. So for the bank data, we then use data from bank compute start and bank focus. We use borrower characteristics from compute start global and for carbon emission and environmental performance we then use refinative data between 2016 and 2022. Later on we replace our measurement of brownness borrowers instead of using carbon emission. We then also look at the reference environmental risk index that also widely used in the literature. The screen based on pollution news borrowers. So for example, if total energy would have a pollution scandal then the risk index that reference provided was then going up. And then also we look at the climate risk exposure by actually my discussion, start now at all 2022 and then we find the same result there. So the idea of the paper is just we have two group of banks, the treatment group. So here's just summary statistic in our data. On average we have one syndicated loan has a side of one million euros. So this is really large loan provided to really large borrowers and each loan has the maturity of around five years and the loan spreads of around 225 basis point. In the sample because we only have like really large syndicated loans and in France between the periods that we can also observe the loan spread we only have 993 loans given to 46 really large French borrowers by 60 EU banks. So 44% of our loans are given by stratested banks and the rest given by foreign non stratested banks. So when we move to the borrower level analysis we observe that essentially almost 40% of our firms getting loans from stratested banks and during our sample periods they have a carbon emission growth really vary a lot is ranging between minus 47% to 112%. So this is our identification strategy. The first step is as we just really do a simple check on how banks price carbon emission risk and whether they change credit supply based on carbon emission of borrowers. So we just regress the loan volume or loan spread of loan L provided bank B to firm F at time T as a function of carbon emission of firm F at the time T minus one. We then include bank fixed effects. We also have borrower fixed effects industry fixed effects and we cluster our error term at the bank level. In the second step where we have this ambition to really evaluate the effect of climate stress we then do this triple interaction in a quite standard difference in different framework. We then regress the loan outcome the same as loan volume and spread based on the interaction whether the borrower F is a high emitter based on their total carbon emission prior to the climate stress which is above the median of all firms in our sample interacted with the post treatment period T and treated B as the indicator for whether bank B participated in the climate stress. So again we have borrower characteristic in we also have essentially bank fixed effects loan type fixed effects and industry fixed effects included in the model. So after we look at the bank level result we then aggregate to the borrower environmental performance and here instead of looking at loan volume or spread we then look at short versus long-term adjustment in the environmental performance of borrower at the time T. So the short-term adjustment we will have dependent variable as whether borrower has environmental improvement tools whether they reported that they have products with environmental responsible uses and then we see whether they have initiative to restore environment whether they committed to emission reduction and whether they pinned down essentially one concrete number of how much of the reduction in carbon emission that they would have in the next 20 years. And then we also look at whether they have this environmental evaluation criteria when they do their investment. For the long-term adjustment this is something that not just they can say that they did but it's more like concrete number of their environmental performance that we also could see in real time. So we have emission score, total emission growth, direct emission growth whether they have supply change environmental policies meaning that for example they will source their materials with like environmental criteria or whether they have the termination with environmentally unfriendly suppliers. So again here we have the triple interaction high emitter F gonna be dummy that take value of one if the average carbon emission of borrower F before 2020 is above median and zero otherwise. The treated gonna be that it's not at the bank level this is at the borrower level so we look at whether borrower F would receive any loan from the stratested banks the year before. And then here we have borrower fixed effects and time fixed effects in the model. So in order to do our standard difference and different approach we have to ask how similar are stratested and non-stratested bank before 2020. So here we have this comparison based on bank characteristic also borrower characteristic essentially the borrowers that link to treated and control banks how different they are in terms of carbon emission and how different they are in terms of side leverage and profitability. So we do not really care about the level differences but here we care about the evolution. This means that whether banks and firms evolve in similar way before the climate threat test. So we take the difference and then we have this first two columns are the mean and standard deviation of the treated loans. The third and the fourth are the control sample and finally we have the normalized difference in difference which is the difference in mean of treated and control divided by the square roof of the sum of variance for treated and control variables. So here we see that with the approach of impents at all 2009 the rule of thumb is that if the normalized differences the absolute value of that is above 0.25, 0.25 then we need to worry about the violation of parallel trend test whereas any number that is below the 0.25 threshold we can be confident that they involve in a similar way before the shock. And we see here that we did not see any violation of parallel trend test. So now I will jump to the first result which is on the bank lending and borrow a carbon emission so there's no effect of climate threat test yet. So here we have the loan level information of all loans that we have observed information on maturities, loan amount, spreads before 2022 quarter two. And the first two columns you see are how carbon emission relate to loan amount and spread on loan amount and the third and the fourth are for loan spreads. So we see that for the first column we have some evidence that banks reduce loan amount and reduce by 7 percentage point. However, when we control for borrow a characteristic the result goes away. The same goes with the spread without the controlling for borrow a characteristic we will see that banks increase spreads for those brown loans but after controlling for borrow a characteristic we also did not, we could not see the effect there. Now this is the effect of climate threat test and I show you the dynamic effects. So essentially we plot the coefficient on the triple interaction term three years before the climate threat test and also three years after the climate threat test you see that before that all the coefficient are insignificant also confirm the result of parallel trend do appear in our data and after that the banks seem to adjust slowly. So the first year we could not see any effect but from the second and the third year we see that they increase loan amount but at the same time asking for higher interest rate when the loans are for brown borrowers. Now this is just the result in payable format instead of the dynamic result and here we see the same way so the first column we do not control for borrow a characteristic the second columns we do control for those borrow a characteristic such as leverage and size. The third and the fourth also have the same structure and we see here is that treated bank meaning the bank that participated in climate threat test they increase loan amount to those brown borrowers by 15 basis point and at the same time they ask for 8% percentage point higher in loan spreads. So this means that instead of reducing exposure to the brown industry with the long-term approach what they do is that they know that if 60% of their portfolios gonna rely on those brown borrowers what they need to do is to change this so brown borrowers to a greener one rather than just completely reducing the exposure. So this is what contrasts from other result on how bank react to climate threat test and essentially here we see that banks did seem to aid borrowers in the transition to what greener activities but also adjust risk pricing accordingly and also just for example my discussion, Jack has a paper that look at actually a lot of brown borrowers they do those green patterning, right? So it's not that the brown borrowers do not do much but they needed financing to really green their production so that they could for example instead of relying on the brown energy they could invest in technology and later on also getting more ready for the green transition. So this is our heterogeneous adjustment and we look at so which bank actually do that? So we split between banks that have long-term relationship and banks that did not have the long-term relationship would borrowers and we see that the banks that give more credit and also charge higher interest rate usually banks that have relationship with the brown borrowers and finally I show you just some short result on how this then additional lending lead to changes in firms environmental performance. The first thing is the short-term effect we have essentially where the borrowers have improvement tool related to environment they have product with environmental responsible use also for example they develop reduction in carbon emission target and we see all the results they are actually politically significant. However, now if we move to the long-term adjustment we do not see any improvement in emission score we have not yet observed any decline or increase in total emission growth and direct emission growth there's not yet any supply chain policies in place and if anything we see actually a bit of evidence that they less likely to use material sourcing environmental criteria. So here we have not yet made a conclusion whether this is greenwashing or whether this is just like something that takes longer time to adjust so essentially we need to observe a little bit more on this additional lending whether it's really that the firms only talk the talk rather than walk the walk. So I want, I'm gonna skip all the rebuttals checks that we did there so essentially we use alternative measurement for carbon transition rates and do a fancification test to confirm that our results intact and this is our next step to really explore for example what would be the difference across industry and also for example different across bank characteristic and I'm gonna conclude here so essentially this is our paper we try to be the first to examine the effect of bank climate stress and we actually did see that bank climate stress did make a difference instead of reducing loan to those brown borrowers the banks that participate in those climate stress they increase lending with the hope to aid the borrowers for the green transition so at the moment we see some good news which is borrowers try to commit more into these green initiatives but at the same time it's take time for us to then observe whether those at least know funding lead to more investment could really be the good source for essentially reducing direct upon emission and also other environmental performance and I look forward to my discussion, thank you. Thank you very much for the opportunity to discuss this paper. I'm at the University of Zurich Swiss Finance Institute and have a small ECB head on because I'm a regular research visitor so if you're at ECB or around I'll be back and be happy to talk to you if we didn't have time to talk here. I'm very happy to start with a positive assessment very positive assessment of the paper because if you think about it it has all these ingredients that we try to put into a paper when we try to make it a hit so it's an important topic. I think it's fair to say that climate stress test will become a standard tool in bank supervision moving forward and rightly so because it is probably the big elephant out there if we think about the risks that are there and we don't know yet how they will affect banks it's carefully executed the authors are aware of the identification challenges many issues that I had when starting to go through the paper paragraph paragraph kind of were addressed as I went along what I also very much like is that there is a surprising result you all know this, you submit a paper to the journal and then it comes back and the editor of the referee says sorry it didn't change my prior, why do I care? I knew this already so here there's really a surprising result because I was surprised to read that actually after the stress tending they increased rather than decreased lending to the high carbon emitters and increased the rates so it does make sense so there's a plausible channel last point it's an awareness channel, an information collection channel that's through these stress testing they're able to kind of understand the importance of climate risks, these banks and how to address them in the lending process I've just 10 minutes so I'll make three comments two big picture comments on the interpretation of the results and one kind of more technical comment on the parallel trans assumption big picture comment so there's a key policy issue that is addressed in this paper and the key policy issue and I should say I work a lot on climate finance not, you know, macro potential things I work on sustainable finance more broadly so a key issue that we face in sustainable finance and sustainable investing is that we cannot simply say oh, let's all stop giving money to the dirty firms because these dirty firms need money to get cleaner as a matter of fact, I don't know if you know the statistics if you look at the 176 biggest carbon emitters in the world they're responsible for 80% of industrial carbon emissions so it's extremely concentrated and if we just go out there as equity or debt providers and say, let's stop giving them money well, how can they make the transformation that they have to do because they're responsible for so much dirt in the atmosphere that it's really those companies that we have to support in the transition now, this is understood by most banks if you talk to them, they say we actually have to increase transition risks because we need to support our clients in the journey to become cleaner now, how does that square with all these public demands towards net zero targets and show me that you reduce emissions it's not so obvious as it's not so obvious that the best investment product is an investment product that has only clean freedoms maybe we should label the high ESG products those that have a lot of dirty firms in there and where the investors are actually engaging and getting them change business practices so it's similar here and so the paper touches upon this important issue so it's critical to identify the right prone firms those that are willing to do the transition that have credible plans to decarbonize and continue providing them funds and reduce the funds from those firms that are not willing to do this transformation now, what the paper shows is that the climate stress tests in France and just as a side comment, I'm surprised it's again the French because in a lot of other settings that I've been studying like disclosure and so on it was also the French that were the first and it has to do with the Paris Agreement that I think they wanted to kind of signal to the world we're really doing something before the Paris Agreement started or the summit so the French climate stress test helps achieve identifying the right firms so this awareness information collection process now, here's a question so these are syndicates banks know each other, they know the borrowers so if the stress test at banks are better able to identify the right borrowers why are the others not just following? So how does it hold in equilibrium? So if you're together with other banks especially stressed at the French banks in the stress testing and you see, apparently they've learned something they change or do not change landing patterns not away from the dirty firms why are the other firms not just imitating this? There seems to be value in this information that they learn, that's a message from the paper so why are the others not imitating this? So here's my suggestion simply provide some more discussion you don't have to put down a model to explain this that helps us understand what the frictions are that make the control groups in a sense not imitate the treatment group Comments too Again, big picture comment and I was very pleased to see on the last slide that you also start thinking in this direction so what are the implications if you think about banks and bank characteristics? So the focus was on the borrowers but I would like to learn more also from a financial stability perspective about the banks so far as I said, mostly at the bank loan level but important questions are being raised by the paper at the bank level so do these loan effects translate into crater or lower climate risk stability of the banks? Treated banks versus controlled banks? Do markets award the changes or non-changes in the lending behavior? Why do we actually in that comes back to an earlier comment assess banks on whether they give net zero targets rather than actually assessing them on the actual changes of the borrowers they continue to provide funding to if it's dirty borrowers I think it will also provide some nice suggestions for regulators if we are able to go this extra mile and understand the implications of these stress trends at the bank level so here are two suggestions first, I would like to see some more tests at the bank level for measures of climate risk systemic climate risks their measures, the climate war is an example so show us how before or after the stress test these measures of climate risk at the bank level have changed maybe they haven't and then second comment it would be nice to look a little bit at the sensitivity of stock returns and I know there are also some non-listed French banks in there but of stock returns and how they react to realizations of these transition risks so if you believe the narrative that they lend to the better firms with the carbon emissions but the commitment to reduce them then you should expect also that if there are realizations of transition risks and there are measures for those right aggregate shocks news coverage of transition risks for example and other things that if there are these realizations of transition risks that those banks that were stress tested and kind of adjusted the pricing and the lending should suffer less compared to those banks that didn't do this okay so I have some work with co-authors where we look at this a little bit where we create a measure of the climate risk exposure of banks and so there we actually do show it does matter how your carbon intensity looks like in the portfolio so if there are realizations of climate risk those banks with a big exposure to carbon emissions suffer more and it's like a conditional effect compared to those firms that have less risk and it would be very interesting to see whether the climate stresses are having kind of a moderating role in reducing the sensitivity last comment you've seen this this table tool which is a critical table on whether the parallel trends assumption holds when I saw the when I saw the table I thought yeah it's nice but it's not how I would you know usually and I know there's a great paper that you cited us that think of you know you showing me whether the parallel trends assumption holds it's obvious that there's some selection going on right it's french banks it's french boroughs it's voluntary to participate in the stresses of course there's some selection so rather than you know coming up and say look I do this test with the standardized differences even so you know means are very different and so on that there's no parallel trend just go there and say yes there's some selection going on and describe it and tell us how it may bias your your kind of different if estimates as a simple difference in mean test simple t tests simple crafts and then see you know where differences and you know how how does this affect the behavior so let me conclude important topic some more work on the interpretation and consequences of the results that's the kind of stuff that at the end we determine the impact of the paper if you can say more about this but you also need to do some more work on the identification issues and actually that's the important stuff to make the hurdle at the journals it's a number one comment to reject the papers or I don't believe you know there's a random assignment so I don't believe the results which I don't think is a useful comment here given that it's such a nice setting so therefore you know try to do a little bit more of the upfront that maybe you know there are some differences and we know how they affect the results and then I'm very pleased thank you for the opportunity thanks so much and let's collect some questions from the audience I already see one hand here up front hello thanks a lot for the nice talk and the nice discussion I just wondered if you know they extend the loans and and they somehow charge high interest rates with this really the risk I wonder if also the risk rates are the the probability of default that they so if they have internal models it should also be reflected so I wonder if you have you have that data as well and you could explore this channel maybe a bit thanks and really nice paper and great discussion as well I really enjoyed both and my question was I think you said something about the stress test itself allows for dynamic balance sheets after a certain number of years could you just say a little bit more about how they do that and how are they modeling this adjustment and balance sheets because that always strikes me as like one of the key things in terms of whether this is really a risk to banks or not thank you for the paper and the discussion and I was wondering so these are syndicated loans so to at least my understanding they sold usually after origination so I was wondering whether this selling could contribute to yeah the brown loans actually going up so thank you so much Jack for the great discussion so I just had a coffee with beyond and we actually said that I couldn't ask for a better discussion for the climate generation risk paper so thank you so much for that and also thank you for the great question so I think I would just go to the first comment that you say about essentially maybe I could do something else on also like telling the bigger pictures what would be the real implication so that's on our to-do list for their essentially like the bank level analysis this is something that I think this really knew that we did not think about like but this is really something we could do so essentially what we could do is like look at the bank balance sheet and estimate the aggregate level of climate and this is something that really nice addition to the result as well for the battle of trend I think that yes we could do like heterogeneous checks on for example maybe there are some selection and it will also include the other French banks that naturally they did not choose to participate but they could learn from the other French bank and see how different they are and why didn't they for example have the same result there so thanks for that and then we have the one the question on the credit rigs of borrower I think so at the moment we have not had the best yet but we do have the measurement for for example financial constraint firms and we are in the process of collecting the credit rigs score of firms and then hopefully if we control for so the interaction of those borrower credit risk characteristic with the stratested dummy and then if we still see the effect maybe then this is really from the brownness rather than the other credit rigs I think David raised really excellent question on how do banks then estimate this risk based on dynamic balance sheet so the idea is that the French central bank they did not really spell out like what would your loan portfolio look like in 30 years what they spelled out is essentially this is the coupon prices so essentially you as a bank have to come up with also different pathway for the for the balance sheet so maybe for example if they have the internal model that say they would reduce exposure to those borrowers then the balance sheet would look very different from for example they try to increase the exposure and then provide more funding so this is up to the internal model of the bank and then there are then analysis on peer so essentially if you are borrowers and you come up if you are the bank and you come up with like kind of too good to be true dynamics balance sheet then there's always this ally or comparing to the other peers that maybe that doesn't make sense and then the bank have to adjust based on that and then finally on the syndicated loans I love the question because I have the other paper that talking about how banks tackle the carbon transition risk by selling off those brown loans in their secondary markets and we use this US data so the difference is that I mean this French banks of course syndicated loans mean that they can sell in their secondary market but for European loans it's much less likely compared to the US we could actually control for that because we do observe for example institutional participant in the syndicate and then we can see exactly which loan got sold later on so on the other paper that I work with Chang-Win and Isabella Miller we observe that depending on the level of uncertainty in climate change recent risk the banks when the change recent risk is high they're gonna take into account that in the secondary market investors they kind of like because of risk sharing they do not price the climate change recent risk that much so the bank could really offload those risk so it is like similar to the kind of divestment that you see in the pollutive plants but here's the bank because they have this brown assets so what they do is that they just sell off those dirty assets to the secondary market and if that's the case we do observe that the chance that they price that brown frixes less likely thank you okay thanks again very interesting results and an interesting discussion Hün and Saharias we're going to have to move on to the final paper on market potential policy leakage through firms this will be presented by Björn in Birowicz who's at the Deutsche Bundesbank and our discussion is going to be Francesco Lopez from the University of Roma 3 okay so Björn the floor is yours thank you very much and I know I'm facing a huge challenge I know everybody of you is thinking about should I pay attention should I sneak out as usually people do when they have to run for a train or plane it's all good and or should I just silently fall asleep and I will hope or try my best that you all will stay awake but in case you want to fall asleep at some point in time I have a I have a wrap up after five minutes and then you can slowly well let's see so the paper I'm going to present is on market potential policy leakage and you might have realized over last years there was some there's some literature coming up how can you how can macro potential policy leak and was just a nice paper published on jie where it's about the banks and the non banks and how you can avoid macro potential policy if you will there's currently ongoing projects with respect to where there might be leakage between different kinds of industries and in this paper we actually look if there might be leakage coming from the firms so the boroughs the banks lend to and that paper is joint work with Axel from bonus bank Ozzie from the ECB and Steven on Jenna also from the University of Zurich and so let me talk a little bit about the background and the contribution of our paper so we all know that after especially in that room I don't have to explain too much and you'll see I keep it very high level policy wise you won't see any equations because I'm aware of the time of the day so we know after the crisis there was many things regarding counter-secret capital buffer we want to reduce prosyclicality and increase bank resilience and then the idea of this ECB is we want to contain excessive credit growth but also help during downturns we want to support credit growth then again so and that what what that's requires is that banks build a capital in normal times which they then can use in crisis times to absorb that the important thing we will be which is important to understand our paper better is the feature of automatic reciprocity a very interesting technical word so it means we want to avoid regulatory arbitrage and so what we could have we could have cross-border lending of banks so you are sitting in a country which introduces the CCYB while you have to adhere to the higher capital requirement but all the foreign banks are coming in and lend more to your customers so not facing a well the regulators thought about that so they introduced a feature of automatic reciprocity meaning everybody lending to borrowers in this rest your jurisdiction face the same capital requirement in that in this jurisdiction so we ask the question our paper how effective is macro-predential policy which is by definition national when we have a globalized world and firms are not just working in a very small single region but in a larger one and we know there's a huge literature on the very negative and transitory effects when we look at changes on capital requirements when we look at bank lending it's very often the case we look at all kinds of experiments and see if capital requirements micro-predential go up lending goes down but then we also know like where do firms do they get now get the money from while there are some research showing that there is some substitution going on by banks with lower capital requirements and especially if you look at the cross-border context you see you have one there are higher capital requirements they so if banks in a foreign country have higher capital requirements the foreign banks lend more because they have lower ones but if there is there's an increase in the CCYB there have been some papers coming out over the last years showing that actually everybody reduces lending so now let's see how do actually so now we live in a globalized world as before so how do internationally operating firms now react to this national macro-predential policy and if you look to the literature of internal capital markets of international firms you see the firms operate globally and they want to exploit every loophole or use financing advantages as much as possible and you might all be aware of the discussions like where do actually firms generate profits where they should pay their taxes and there's a lot of discussion going on on on an international level and there's some literature showing that firms go to the lowest tax rate countries so we all know financing of firms happens a lot in the Netherlands it's a nice country but the main reason is obviously taxes or when you go to Ireland it's the same case there's also one the institutional quality differs or when you have different levels different levels of financial development so what we do in our study is how do the funding structures of multinational firms so internationally operating firms actually change when external borrowing constraints increase so the CCYB changes um so and this is the summary slide i was alluding to so when after i'm done with that slide then you can for so just lay back and relax um so what we find in paper the question we ask is what's the effect of a larger CCYB on landing in general and on risk and we look at the landing of banks and we look so we have a setup we are coming from a german perspective coming from the Deutsche Bonus Bank so we have the data on that which is the most granular i guess and we look at cross-border landing of german banks and we see if a country introduces or increases a CCYB the cross-border landing decreases by 8.6 percent we also can see what happens to non-banks while there's nothing going on makes a lot of sense given that the CCYB doesn't apply to non-banks so non-banks do not change their landing to firms in countries where there's a positive CCYB we also look at the risk of the portfolio so what is the change in PD probability of default and we see this is kind of important to distinguish between the separate firms because you see it's heterogeneous on the one hand the probability of default to the country where the CCYB is in place goes down so the subsidiary firms abroad the PD is reduced however on the other hand and this is the important thing which we will delve in much deeper in the data parts of the paper is the PD of parents is going up so something is going on here internationally and that's the main question of the paper so we look at do the affected subsidiaries then in the CCYB countries substitute the bank credit by just asking their parent companies can you maybe lend us something and this is exactly what we find we see that they increase their landing by roughly one third compared to the period before and we also check is a credit substitution complete and we find yes so eventually there is an introduction or an increase of a CCYB and an internationally operating firm has the same leverage so there's no change just the money comes from some worlds in the case what we show it's coming from the parent company abroad which is unaffected uh now the big question is the parent company has to lend the funds so it has to get the funds somewhere from where do they get it from well they simply go to the domestic banks and increase their borrowings and they go to their domestic non-banks and increase their borrowings so there you see here there's a shift going on which is an international shift so obviously now banks lend more in countries which had nothing to do with the change in CCYB and the other countries now the question and we also ask is there's general risk shifting going on and this is the thing we cannot really confirm because we observe that banks and non-banks so the on the domestic side pay attention to the risk of the parent so do not just simply replace but actually account for the risk and this then also confirms our mechanism because the risky appearance lend less to the substitutes which are affected okay that's already everything in a nutshell so the contribution of our paper is um we what we observe from the paper is if and there's a change in CCYB in one country it might also have an impact on other countries and especially and this is important for policymakers when the macroprudential policy stands is heterogeneous across countries so I can already like look to the conclusion the conclusion will be if we had the same CCYB everywhere we presumably would not observe the effects we were observing the paper but that's not the case um so we see that there is a decrease in cross-border bank lending and a decrease in risk to the affected countries so the countries increasing the CCYB however there's also an increase in domestic bank and uh bank lending and also an increase in cross-border firm lending to the subsidiaries and also an increase in risk of the banks lending domestically so macroprudential policy might leak through the international structures of firms and in that little picture here you see uh so uh at the bottom you see this right a little problem at the bottom you see banks are lending less cross-border but then you also see that we have an increase of this 30 of one-third that the firms lend to their subsidiaries okay let me briefly uh talk about the data we use so we use internal bundlesbank data uh which has the limitation everything is from a german perspective and ideally of course we want to have a global sample and know everything well we don't so we have to go what what we have but we have very nice data so we have something like a credit register and I already talked to my discussant um the credit register comes with two caveats one is we don't have individual loans we have credit exposures so in each quarter we know how much a bank lends to a specific part of a firm but we don't know the so the the accept new loans or repayments of loans and we don't have prices also in them uh but the the nice thing is we have the probability of default of each firm uh included so it's the estimates and then the even nicer part well I personally think what makes it really unique we have very granular data on german foreign direct investments so as soon as you borrow from a bank located in Germany well they have to report the entire structure of the cooperation and this is a mess of data but it's very nice because you because you see internal capital structures you see how much one subsidiary lends to another subsidiary or to the parent company which helps us here to figure out the effects we observe so we use the setup that we have a parent company in Germany which land and and which has subsidiaries abroad so outside Germany and have detailed information on the libid liability structure so we just data from 2013 until 2019 overall include 30 countries where of course not everybody had a ccyb and you see here we have quite a nice fraction also a number of lenders and a number of borrowers so there's a lot of heterogeneity in the data so this is what happened until 2019 when you look at Europe with respect to the ccyb so i i received once received a question from somebody outside so isn't it all homogeneous well we as most policymakers here in the room know definitely not and the beauty for us to some extent here using this data is Germany didn't have a ccyb until the end of 2019 so we were unaffected it was always discussions going on i know like from internal discussions but we we just didn't make it until and then there was covid and we just introduced it and immediately released it and now we we are not even at one percent so and then you look at other countries where you see like Norway we're at the forefront of introducing ccybs and then also Sweden where among the first sort of Scandinavian countries and other followed in but there's lots of heterogeneity between the different countries and overall as i said we have overall 30 countries included so the first question we ask is what's the effect of largest ccyb on the borrowing from banks and from non banks and also on the probability of default of borrowers and so this is basically all the same but just with varying degrees of fixed effects so if you have questions look at the fixed effects we saturate in a lot to get rid of many potential problems we might face and we see this year is at the bank country level so we aggregate all lending for the bank at each to each country it lands to at each quarter in time and then just said look at if there is a ccyb in the country what's the change in lending and you see here on the even on the aggregate level the lending decreases when you increase the ccyb technically it's a diff in diff with varying treatment timing and variant treatment intensity for those technically interested um what we then do we look at the bank firm level and the firm level here is the subsidiary so this is just cross-border lending so outside Germany and it's a single bank to a single subsidiary at a certain quarter time level and you see here if you increase the ccyb also here it's confirmed if the ccyb increases it reduces the cross-border lending of banks to countries which increase the ccyb so an increase here of one percentage points and the ccyb means a decrease of 13.6 percent in bank lending to a customer in this country now we also look at non-banks because it's included also in our credit register and the big question is now what do non-banks do and many people claim well they take over and so well what we observe having a very clean setup they don't change their lending at all and yeah why should they they are not subject to the ccyb so you might think that they might be taking over it's not what we observe but a word of caution and I've some people would throw everything in one pot and of course what happens if I use a dummy here on non-bank the relative effect would be positive people doing this in a different diff and yes I've seen papers like that would conclude oh the non-banks are doing much more lending after ccyb is introduced no it's a relative effect there's just that if you do a fair comparison at least from our perspective in that special setup if you will we don't see any change in lending on the non-banks we then also look at the risk of of lending so we know we know from the literature so from the microphone literature that there is actually the capital requirements are going to some extent with towards lending but very often they go towards risk-weighted assets so the risk is a very important thing banks might not really change the lending they might just change the risk structure of borrowers so what we also check is what's the change in probability of default and lack of risk-weighted assets so what we see here we aggregate the data to the bank country level so take the average pd in the country a bank lands to oh it's each specific point in time and then check like what's the reaction when you increase the ccyb and we see at the aggregate level the probability of default to lending to of borrowers where you land to this country decreases we then of course also do that at the bank firm level and also here when we throw everything on one pot on the first four columns you see it's a negative effect so i increased the ccyb in the country and the average pd of borrowers goes down towards this country now we do a little split we we just look at the subsidiaries and the important thing here technically it's within mnc so multinational cooperation it's like this one international firm like Siemens and now we compare all the subsidiaries within Siemens with the same lender so mnc times lender fixed effects and here we ensure that both subsidiaries and parents receive loans from the same lender at the same point in time to really make a comparison possible what you see here the subsidiaries actually have lower risk well we now also split it with the lenders so now the mnc fixed effect here becomes a firm fixed effect because obviously there's just one firm in an mnc and i just kept that for ease of illustration but when you compare the parents and it's not the exact same comparison we cannot we cannot use the firm times bank times time fixed effect because that would be fully saturated but if you use a little bit more less granular comparison you see if i just compare the parents if i have an effect of subsidiary have a higher probability of default when there is a ccyb introduced for one of the subsidiaries so the following questions we are then about where does that come from and this is also one of the reasons why we dig deeper into that so the first question we asked is what's the effect if you have a ccyb on the international funding structures of multinational operating firms and well mncs have the very easy thing with what they could do they could always circumvent when they find answer for favorable sign of financing conditions they can always circumvent these or try to circumvent these they can shift their boring to unaffected firms in the multinational cooperation so if their ccyb is going up in sweden you could always say well why don't you go to Germany and borrow there maybe funding costs have changed to some extent because of the change in capital requirements and then you use your internal capital markets and transfer the money from Germany to sweden just internally where no ccyb is in place so within the firm so what we do in our analysis we look at if unaffected parents lend more to affected subsidiaries and if this substitution is then also complete and we indeed find that no matter how you slice it and dice it it's like the internal depth from a parents to total assets increases by roughly one percent or by total liabilities by 2.4 percent which means overall an increase by one third meaning that the internal depth from parents to affected subsidiaries increases when the ccyb increases now we also check what does it mean for the capital structure of the subsidiary so is there any change well if you're an international firm what you see here's there's no change with respect to total liabilities and of course we also look at other funding sources like for example are there other unaffected subsidiaries lending we don't find that confirmed and we also look at not perfect but a little gross measures on capital markets but we have more information available and we also don't see any change over there and therefore we conclude that the substitution is complete deriving from the substitution of credit away from banks towards the parent companies well now the next the next question is if the parents companies provide the funds the big question is though where do they get the funds from and they are now located in our case in an unaffected country which is Germany now well they just approach their banks and when they have an affected subsidiary well they increase their lending by five percent from banks and 13 percent from non-banks so they use all the funding sources they have available if you will so the ones they used before and just increase their lending and then now think back think back of the result we had when we compared parents with affected subsidiaries to those which do not have affected subsidiaries there was an increase in risk well there you go this is where it's coming from the increase in risk is simply because the parent companies seem to assume more debt from banks and from non-banks and this is then this then having effect on their probability of default now we also look a little bit deeper if there is risk shifting going on this is especially to also verify if that's really the mechanism what we have in mind if that's also confirmed in another setup and what we look at is is there actually just a lending by banks and non-banks where it's just like the parents saying well I have a I have an affected subsidiary the CCYB has increased lending has less advantages can I get more money from you or is the banks are the banks and the non-banks differentiating by the risk of the parent or not and what we find is that actually yeah so the domestic banks so here's like okay that's not too much presumably a problem in terms of risk because you see if you are a riskier parent company you also receive less from the banks and you receive less from the non-banks however as you know you have to put these two together now let's have a look at the distribution of probability of default and these are usually pretty small so when you look at the distribution of PD is what you would need here is a PD of two percent to just have a zero effect and here a PD of almost four percent well the average PD of a parent is 0.5 percent or the median is 0.25 percent and there is a quite large standard deviation so very few parent firms maybe do not increase lending from banks and non-banks but most do this is at least what is statistically shown here but nevertheless banks and non-banks still pay attention to the risk of the parent and yeah well that's good so we also would like to ensure is this can we confirm our mechanism so is it really that parents lend to subsidiaries so if risky appearance obtain less additional funds from banks and non-banks we would assume that they also provide less funds to their affected subsidiaries and that's exactly what we find confirmed so you see here so they provide larger funds to their affected subsidiaries however this decreases in their PD so if the risk is going up they obtain less bank and non-bank credit less additional bank and non-bank credit but also provide less bank credit internally to their affected subsidiaries and this has nothing to do with the probability of default of the subsidiary but it's really related to the probability of default of the parent company lending to that subsidiary so yeah so we do not really find substantial risk shifting going on they still pay attention so let me conclude we have reciprocity rules and these seem to limit leakages by containing excessive bank credit growth through a CCYB country so especially in the cross-border context however we have multinational corporations which have the ability and also do so that they circumvent the CCYB requirement by using internal capital market and this then again increases credit growth again for the cross-border lending bank through more credit to firms and countries which have either no CCYB or especially a lower CCYB so we can conclude from the findings in our paper that macro-pudonism policy might leak through the international capital structures of firms and I think the important policy conclusion of our paper is well we need less heterogeneity and CCYB or put it in positive terms we need more harmonization of counter-circular capital buffers among all the countries and we have basically almost all european countries in here so i think that's a very important takeaway for policymakers harmonize the CCYB and you do not presumably observe kind of these leakages we observe here in the paper but of course and look ahead to the to the discussion some words of caution we have a special setup here right we have internationally operating firms we have a perspective just from germany that might be substitution of bank lending from somewhere else so especially the effect of the CCYB on standard loan firms might be different and we have very important firms i think by internationally operating firms and these days almost every firm is internationally operating but of course firms just being located in one country might have might experience very different effects and second second is we are currently very silent on potentially longer term treatment effects so what we observe is you have more credit growth in low CCYB countries when there is a change in CCYB in other countries and this of course might change everything again because this increase in credit growth might then the domestic part in our setup induce an increase in the CCYB which might lead to a reallocation would be very interesting for future research but we don't go that far so thanks a lot for your attention i'm looking forward to discussion thank you very much very clear Francesco the the floor is yours yeah so it's definitely a very well executed paper yes this is the one thank you very much so i i think you did a great job in discuss in explaining these results and it was very detailed and i know i'm the only one standing between you and the exit door so i will not spend much time explaining their results i just want to i mean so i have three sets of comments it seems long but it's not so the first one is about Contasigua capital buffer i think i mean of course with this audience you don't need to explain much about how they are constructed how they're built but i think the paper needs a little bit more discussion on the details in about how this should affect lending in the sense that what we have with what we have with Contasigua capital buffer rate is that some additional capital requirements that goes through the risk weighted asset and we know that the risk weighted asset basically can be different between products between companies so i think i mean explain this heterogeneity within bank i know you don't have you don't have data about specific products and i discovered this at the coffee break but maybe you can like try to leverage some different kind of exposures i don't know to which granularity your data goes but i mean for instance you i mean my idea the idea was to basically look at the different kind of absorption the different capital different kind of weights that each exposure might have based on the lgd or this or the like collateral the company i mean pledge on the on the loan these sort of things that basically would change the capital the risk weight and then could basically make bank tilt the lending towards specific products when the when the Contasigua capital buffer of specific country goes up and yeah exactly so this so a bit more discussion about the Contasigua capital buffer would be would be great then the second the second point is about the credit register so again i didn't and initially i didn't know why you aggregate a loan at the borrower level but then i discovered that that's because the only thing you are really really have uh as so it's i i i thought it was possible to have like individual loans this would basically allow you i think you can still do that is looking at the changes i mean i initially i thought about the date of the regeneration of the single loan but if that's not possible maybe you can see when the when the lending to a specific company changed quite substantially and try to see and try to basically rebuild a more standard like difference in difference setup in which you have the change in the lending to support the word a specific company or subsets subsidiaries in countries where there's a change in the capital buffer you know because you have two levels in the in the Contasigua capital buffer you have the levels and you have the change the changes within the country right so you could potentially focus only on the changes so this would allow you to put like country fix effects because i mean you have saturated your model with many many fix effects but there is no country fix effects and basically so that means that you're also looking at the like heterogeneity across across countries that would be nice to also focus on the like changes in the in the rate we think within each country and also i mean if you have data about off-balance sheet you know that of course off-balance sheets tend to have lower absorption compared i mean in terms of risk weights compared to standard loans or standard lending so this might also way might also lead banks to tilt basically the their exposure to off-balance sheet which might have some kind of some kind of interesting i mean implication for stability and the final one so identification again i i think i've just already mentioned the first one so that the adding country fix effects would basically help would be helpful to understand whether the identification comes from the cross-sectional difference between countries or the difference within countries so they increase in the rates and in the second part when you show that basically there is increase in an intra-group which is i think the substitution effect is a very sent is the very center of the paper i think it's very interesting i mean and it's something that is really new so i was wondering if you can say something more about the kind of products that i mean the kind of lending at this that they had i mean the this head gives to the subsidiaries like is this commercial credit is which kind of which kind of lending we're talking about is it would be very interesting once again to understand better we this this the product that basically are transferring within the same group in order to understand if there are some implications for financial stability and um yeah once again again i already touched on this i think it's it's you use the probability of default because probably is the is the only data you really i mean it's useful i mean you have data for a big chunk of your sample i don't know this but the point is that i mean the the it is important but also looking at the gd also looking at the collateral probably you have some information whether the exposure is collateralized or not and this would affect of course again the risk weight so it would be important to try to to work a little bit more on the kind of credit so in my view to conclude in my view the center of the paper is really substitution effect so this is what is really new we know that's a counter significant buffer work work and we know that there is this kind of substitution effect from the bank side but we didn't know about the fact that that basically companies can use this internal credit to stop to to substitute bank credit and this is the very new part i think on your paper and it would be interesting if you can build up a little bit more on the products that used to do the substitution the kind of and the implication for financial stability because i think this is this is something that is a bit missing in the paper i mean which it was a great paper but i think it's this this saying something more about financial stability implication for financial stability would basically increase the contribution but thank you very much for this opportunity thank you very much francesco um so let's again see whether we have questions from the floor i see three four hands shooting up already perhaps starting on the left here denise it sounded like you were hoping there will be less questions okay so let me let me be quick very very interesting work it's reminded me a little bit of this discussion in hong kong when there was you know housing booms house price booms and when it was being financed by banks in hong kong it was a concern when it was driven by cash buyers from mainland china hong kong supervisor could just say well it's not my banks on the hook so i don't really care right there's affordability question but whatever that's a different question so it just reminded me of a little bit i'm i'm applying the ccyb and i'm still getting the credit the subsidiaries in my country are still getting the credit so activity is remaining fine but the risk is elsewhere i don't care so it would be nice to hear what you're talking about that complementary question so first the fact checking uh it might be my fault but in the end i didn't really understand do you look only at lending from german banks and non banks okay so that was this credit registry so that is like the two two questions like which are more conceptual so the first question would be does it work in reverse so can subsidiaries of companies serve as conduits for for let's say avoiding ccyb in a domestic country and i say like a why i kind of uh being on those in those terms because it's like when you run some sort of uh regressions also for euro area banks what you find there is a substantial home bias so essentially the sensitivity to credit like a two capital requirements seems to be higher once those capital requirements affect subsidiaries or something abroad lending abroad so it might be some asymmetry if you increase ccyb in germany i know that you have only one observation in this case so does the increase in germany can trigger the reverse process of getting credit via subsidiaries of companies so that would be the the first question it's not clear yeah okay they just they just wasn't the ccyb increase in the time period no they didn't have the zero twenty twenty five no they want to do it they announced it early in nineteen but they there wasn't i didn't do it in the okay so go and then the other question will be about srb so you have srb in a few countries uh in the same period in some of the countries in which the the credit was given and this yeah i know that this is not necessarily on the domestic credit but maybe some mapping to which degree you have subsidiaries of german come like a german banks there and to which degree branches or direct cross-border funding to those german branches in different locations and maybe something on srb and reciprocity of this capital it could be a super extension of your paper um i had the question whether you check whether there are different effects whether that subsidiary is in a euro area country or in a non euro area country because i could imagine that it's much easier to substitute sort of the the lending if your uh the firm subsidiary is in a euro area country because there might not be any currency mismatch you know so revenues are in in euro if the german bank then or the german bank lent to the parent and they pass on the euros no no additional risk whereas let's say i don't know if it's in the us or some other country that might be much more difficult to to substitute because you have additional risk and if you find that different differential impact would even increase the argument that within maybe the euro area it's even more important because potentially the yeah this multinational substitution effect might be more relevant i was just wondering how you can explain the fact that there is no substitution of banks to non banks in a domestic country while you see that non banks are extending more loans in the foreign country how you can explain that or what would be your explanation to that so you mean why they do not increase the non banks yeah why you found a zero percent increase for domestic non banks and i think a 30 percent increase for non banks in the country of the parent i know the 30 percent increase is the multinational firm the parent lends to the subsidiary just industrial firms no financial intermediation whatsoever so it is the banks reduce lending the non banks don't change anything and the firm inside in the capital markets they lent instead of the banks so the parent firm siemens germany lends to siemens suite that's what we find thank you very much this is a very eye revealing and fascinating i never knew of such effect it would be interesting to know what is the scale of this in terms of the total portfolio and what is the motive for banks to engage in that activity if they are not capital constrained so first i would like to thank you especially francesco for for very interesting and definitely helpful discussion also for all the questions so it's the first time we're presenting the paper and i have to say there's still many things we have to do in the paper and we wrote it to put it together in the very last minute and we're so happy that we are allowed to present it here and i agree with with many of the of the points so why would banks do it well it's the parent asking i would need more money for my subsidiary so you just replace it but we also show you do not just do it because all of a sudden there's a there's more risk going on just like because the parent asks for it but then you look at general credit risk and see like you are in need of further funds so i lend more but i pay attention to the risk if you are riskier i give you less i think this is just general loan demand by the firms in response to a ccyb abroad there were sr srb's in in in place in in certain parts of the countries that that's true but we try to abstract from these kind of things by using bank times time fixed effects and almost all regressions i mean it doesn't fully address it and it's a very interesting interesting point and we might look into that that would be fascinating to even find further currently we we saturated away to be honest looking at the same currencies absolutely it's it's top of my to-do list we currently have just european firms to keep it as comparable but then of course one of the first countries is norway and norwegian krona has changed substantially like oil prices and all the like over the last decade so we definitely have that on the list on the top and in hong kong the the argument well now the problem is somewhere else that seems to be kind of the case yes on the other hand we are showing that it's not necessarily a risk shifting so there is more lending in an unaffected country but you lend less if the risk is larger so it's not just blindly increasing in an unaffected country we find a full substitution but so far it it's not just the risk shifting going on cost by ccwb in another country so here i can caution down um yeah talk talking more about the implications of financial stability absolutely running through open doors as we say in germany absolutely we have to discuss much more what does it actually mean also in the aggregate level there was a comment on what does it mean on the total level of banks portfolio well it's cross-border lending which is usually some fraction uh in germany it's okay issue regarding size of course it's tiny regarding domestic lending and we we have to still put numbers on the on the overall economic effects and of the overall international implications i i fully agree to that and we'll look into that regarding um we cannot control for country well we have firm fixed effects that controls each firm in each country so that that shouldn't be a problem um but we cannot look at individual loans or the products of the loans and i would love to look at loan commitments uh i fully give you that uh that's a very nice comment we just don't have these data okay so this is the caveat but yeah we definitely will look more into that and uh thanks so much again it was very very helpful okay uh thanks so much Björn and uh Francesco uh once again um before uh we uh close the proceedings here uh let me um just make a you know say a couple of uh very brief um words first of all you know thanks to the audience for for sticking around and and and for being here all throughout i think we had a great interaction uh there's also been um audience online i'm told uh up to 400 participants have been able to join the various sessions online um we are also uh planning to put the video recordings of the whole two days on the website so that everyone who might have missed the session can can go back to the recording and and and catch up on those on those sessions and those recordings will join the uh powerpoints and also the papers that are already populating the website right uh second uh remark uh uh is i want to uh very much uh extend my my thanks to uh the co-organizers here at the uh ecb uh angela all of them uh here in the room angela mataloni uh mako spain uh stefan fa and uh liviostraca uh for organizing this and for the excellent uh collaboration uh throughout uh the settlements i think sorry it took us to put this uh put this on um and um uh a big thank you also um to all of the organizational support here at the ecb uh i want to mention uh christine wehrheim nikolina mehilli stefan seitz ania sinch franciska falkenstein and vera rosenthal uh and all of the technical and organizational support staff uh that have made it so that the conference really went so smoothly uh throughout so a big hand i think uh to uh release the co-organizers and that's uh yeah that's that's it i uh thank you all for for coming and uh uh wish you a safe trip home