 Welcome back to the second session of this conference. It's my pleasure to introduce to you Yeming Ma from the Columbia Business School. She will be presenting her paper, which will be discussed by my dear colleague on my right-hand side. And you have 30 minutes. Hi, good afternoon, everyone. Thank you so much for being here. It's a great pleasure to be sharing this work with you. This is the Reserve Supply Channel of unconventional Monetary Policy. Its joint works was Will Diamond from Wharton and Jiang, Jiang at Kellogg. So we started out with observing that there's been a very large and continued expansion of central bank reserves around the world. And in the U.S., for example, reserves outstanding before the crisis was very low at just 50 billion in 2006. And they have jumped up once during the financial crisis to 2.8 trillion in 2015, and then another time following the COVID interventions to 4.1 trillion in 2021. Now the U.S. is not alone. Also for the ECB, the balance sheet size has grown substantially, reaching above 8 trillion in 2021. So in this rise in reserves, one of the key contributors is quantitative easing or the APP program here for the ECB. And what this involves is the purchase of securities, such as government bonds, et cetera, et cetera. And importantly, though, this purchase is financed by reserves that are safe and liquid assets that can only be held by the banking sector. Now this is important to notice because the banking sector after the crisis has also been constrained by increasing amounts of regulation. For example, leverage ratios and supplementary leverage ratios essentially impose a cost on how large bank's balance sheet size can get. So in this backdrop, we wanted to really understand what is the effect of this increase in reserve supply that is now concentrated in the banking sector? In particular, how does it affect the functioning of the core activities that we know that banks do, including lending to the real economy? In particular, could there be any side effects of having such a large supply? And we think that this question is important for thinking about how we should design central bank policy going forward and in particular how large the optimal size of central bank balance sheet should be. And so just to take a step back, so banking theory has given different potential results for this question. There's been very seminal theories that say how banks are in the business of maturity transformation. So their assets are of a longer maturity than their liabilities. And so having some more liquid assets such as reserves could actually help to reduce some of the risk of these maturity mismatch and help banks lend more by having a liquid asset buffer. For this first set of theories, having a larger supply of this potentially scarce liquid asset could actually improve lending to the real economy. Now more recently, however, there's been a second set of theories pointing to balance sheet costs that constrain banks' size in total. So if you have more of one particular asset such as reserves, you could be crowding out the existence of other types of assets such as loans to the real economy. So ex ante, you know, we're not really sure which ones of these channels would dominate, which is why we want to look at the data. Now, one very simple way to look at the data would be just to look at this over time. So here in red, I have the amount of reserves held by US depository institutions. And you can see very clearly an almost flat line leading up into the financial crisis that then, you know, distinctively jumps up for the financial crisis. And again, start the doing COVID in blue. I am plotting the ratio of illiquid assets on bank balance sheets where illiquid assets are all the assets, excluding cash, reserves, fed funds, repose, treasuries and agency securities. So think of the blue line as representing essentially lending that the banking sector is extending through your economy. And what you see is almost like a mirror image. Whereas when the red line jumps up, there's almost a simultaneous drop in what the blue line shows. So the simple interpretation of this would be as reserves go up, the banking sector is lending less as a proportion of its asset size. But as we all know here in this room, quantitative easing and APP, it was not a random policy. It was implemented because there was a recession. So perhaps what we see in the contraction of lending is not so much the result of a policy, but the result of the overall recession in general. Because as we know, loan demand tends to drop in times of recessions. And so although this is preliminary evidence that there may be a crowding out effect of reserves on bank lending, it is definitely not conclusive because of the endogeneity with the business cycle. So instead of looking at the time series, we want to have a framework that we estimate using variation that does not come from QE, that comes from something not related to underlying business cycle fluctuations and demand shocks. By estimating a framework with demand and supply, we can then run a counterfactual, right, where we only move reserve supply in the system so that we can then observe how much bank lending, deposit taking and mortgage lending changes. So that's going to be the goal of this paper. And what we find as the reserve supply channel of unconventional monetary policy is that actually additional reserves or else equal crowd out bank lending. And over the period from 2008 to 2017, on average, each dollar of reserves injected crowded out 19 cents of corporate bank lending. Relatively, deposit and mortgage quantities are less affected. And that is because these markets are relatively less elastic than the corporate loan market. The underlying mechanism that we think is at play is that you have reserves that can only be held by banks that cannot be freely reallocated to non-bank intermediaries. And you have banks that are subject to regulation, which makes their balance sheet space costly and hence can lead to the crowding out of other assets if there is an additional input of reserves. Okay, I want to stress that this result is not the only effect of QE. Many of you have probably looked at many of the other channels. For example, the effect of asset purchases, which definitely are important and should be thought of as existing in parallel to what we are looking at here. But what we wanted to highlight a little bit is that when looking at QE, it's not only important to look at what the effect of asset purchases are. The flip side of the coin is the reserves injected and it's the same amount as the assets being purchased. And so we really wanted to contribute to the literature by taking a closer look at the reserves that have been created, which so far has received much less attention. So the results we have, the one of crowding out, should be thought of as complementary results to everything we know so far about the effect on asset markets through the purchase of different securities. Now, in quantifying that effect, we also put some numbers on seminal theories in banking that have looked at how different components of banks' activities relate to each other. Theories that have said how deposit taking and lending can provide positive synergies. Theories that said how liquid assets can facilitate the holding of illiquid assets while reducing run risk, et cetera, et cetera. And the setting we use is going to be a structural model. Again, because we think that relying on the time series alone is not going to be an effective way at understanding causation. So with that, let me give you a one slide overview of the model. I promise this is by far the densest slide and after that it's going to get a lot easier. Okay, so we have a bank M facing a residual demand curve. So it knows that it's going to set its own rate RL, but that it's also going to be affected by the other rates that its competing banks are setting. Okay, the bank would then like to maximize its profits, which comprise of the revenue it's earning from lending and to corporations and mortgages. It's going to then pay its deposit costs. But importantly, and here you see there is that large C at the end of the profit function, the bank has to pay a cost. And this cost exceeds just the deposit interest rate that it has to pay its depositors. This cost, think of it as everything else that the banks would like to, would have to incur. You know, we talked about overhead costs earlier, but this could also for a bank's case involves regulatory cost, potential expected bankruptcy costs, et cetera, et cetera. And importantly, this cost, we allow it to be a function of different bank balance sheet components, including the quantity of loans, deposits, mortgages and securities. So we will allow, for example, this cost to vary as the amount of reserves, the amount of securities, liquid assets in the economy is changing. Okay, so with that cost function, very standard banks are setting their marginal return equal to the marginal costs for loans, mortgages and deposits and for liquid securities because it's a competitive market. The marginal cost is just equal to the price. Okay, so in a figure, this is going to look a lot better. We have very standard in blue, the marginal revenue from lending banks are going to set the margin revenue equal to their marginal cost. So that's the intersection of the blue and red line. Okay, and so that is an equilibrium. And now we want to understand, suppose we had more reserves in the system. If suppose we did more APPs, suppose we did more QE, what's going to happen to the marginal cost of providing loans? And suppose the estimate, and again, we're going to ask the data that, but just for example, suppose that's going to increase the marginal cost of lending. This you can see is the upward shift of the red curve to the red dotted line. All right, in that case, it would been increased in the interest rate on loans and tracing that down the demand curve in green, we would observe a contraction in the quantity of loans. And so this graphical illustration is what we want to take to the data and estimate in real quantities. Okay, so demand supply, very standard. Let me start with the demand system. So we use micro data for deposits, mortgages and loans, thinking of deposit and mortgages as a county time level market and for loans as a state level market. We have this issue that when we want to estimate the loan demand curve, deposit demand curve, we need to use supply shocks to trace out the demand curve. And here we want to use again a shock that is very unrelated to QE. We use the reallocation of bank funding after natural disasters. So natural disasters happen, think of hurricanes, you know, think of flooding. There's an increase in local loan demand and this has been shown in the reduced form literature. Now this increase in loan demand at a given branch is going to translate into a loan supply shock at other branches of the same bank, assuming that internal capital markets are efficient. And that the bank is really allocating funding. Okay, as long as the loan demand at branches that are unaffected by disasters are not correlated with loan demand at the affected branches, this should comprise a valid supply shock to trace out the demand for deposits, mortgages and loans. So this is precisely how we estimate this instrument. So we calculate how much in total is a given bank's branches exposed to natural disaster losses. And then we look at the unexposed branches, you know, how much their loan lending mortgages and deposits are going to change. Okay, so in a loan demand system, we think of how differences in the rate of providing deposits, the observable characteristics and the unobservable characteristics can explain differences in the log market shares. Okay, as standard two-stage least squares with the instrument we just mentioned gives us the following results. It shows that for a given bank, if one bank unilaterally changes its deposit rate for example, by 10 basis points, its deposit volume is going to increase by about 4.6%. Now, if you look at columns two and three, you see that the coefficients are about 10 times larger. It shows that the price elasticity in loan and mortgage markets are a lot larger. So the same rate change is going to have a much larger volume to respond. And that's perhaps not surprising if you think of firms and borrowers as being much more sensitive relative to your sleepy depositors. Now, this again is about how one bank changes its rate. What if every bank in a given economy changes its deposit or loan rates, right? That should not only help them to steal business from each other, that also changes the size of the entire market. So to understand how the outside option size or the size of the market is changing, we then consolidate our instrument to the market level. So basically asking how much do branches of banks in a given market in a given county, how much are they exposed to natural disaster losses? We use that as an instrument to understand the aggregate effect of a demand shock. And we find that if all banks in a given market change their rates by 10 basis points, then for deposits, that's going to be a 1.3% change in total volumes. Mortgages have a 4.0% change and loans have by far the largest change again at 16.1%. For the same rate pass-through, loans and mortgages are more responsive, once more. So we know how much quantities would change given a rate pass-through, and now we need to understand what is that rate pass-through. So once again, we have observations of marginal revenue from our demand estimation that in equilibrium it's going to be equal to realizations of marginal costs, but now we want to understand how this marginal cost is going to change if the central bank is injecting more reserves, if we have more QE. Now, it's a complex function, the one in the first line, that is the functional form of the cost function we allow, and importantly, we want to have these interactions between different balance sheet quantities. So we want to allow how, when there's different amount of reserves, how the cost of bank lending, how the cost of taking out mortgages is changing, for example. And so this interaction effect in the cost function makes our supply system a bit more difficult to estimate than the standard demand and supply system in which if you have a demand shock for the deposits, for example, you're only changing the deposits costs. So what we would then have though is that if there's a deposit demand shock, it's not just the deposit quantities and costs that are changing, it's also the loans, the mortgages that are going to adjust at the same time because of this interrelated cost function that we set up in the first place. So instead of having just one dimensional supply system, we will have a multi-dimensional one, and for that we would need multiple instruments to really pin down all these cost function parameters. And again, we want to find some shocks that are completely unrelated to QE to get rid of the endogeneity problem. The first one is we essentially reuse these natural disasters, but instead of looking at how a bank transfers the initial demand shock, we just use the initial demand shock to begin this because now we need a demand shock to trace out the supply curve. The second one is a very standard bardic instrument where we look at how the deposit growth of different counties change over time and we think of banks as being exposed to that deposit growth and that not coming something from the bank supply side. So with these instruments, we can run the bank's marginal costs as well as their quantities of loans, deposit and mortgages against each of these instruments. And we show that using these regression coefficients, we can jointly pin down the coefficients of the cost function. Remember, that's the big function I showed you over here, all the case with different parameters here that can help us understand how does bank's marginal cost relate to their balance sheet composition. So what we find is the following. Here we are running in the panel A, the different costs and volumes on the natural disaster shock and you can see that the volumes are all going up, but for the costs, the mortgage and loan costs are higher. So when you have more demand, it's going to be more costly to give out lending and deposits are more valuable so their costs are relatively lower. As seen by the negative sign, the first column. In the second panel, this is a bardic deposit shock. So you're exposed to a lot of deposit growth and here you see that the mortgages and loans, they're cheaper to lend out. So this seems all to be consistent with what we would think of these shocks as doing. And then the second step again is to use these coefficients to pin down the cost function. And here is what we have and probably the most interesting result of the paper is in this matrix. This matrix shows you for a given change in the quantity of deposits, mortgages, loans and securities. There I'm going row by row. How much does the marginal cost of deposits, mortgages, loans and securities change and there I'm going column by column. If you look at the diagonal of this matrix, you see that the coefficients are positive. This means that if you have more deposits, then the additional unit of deposit is going to be more costly and similarly for mortgages and loans as we would expect. Now what's really interesting is if you zoom in onto the last row, again that is how the effect of a unit of securities changes the marginal cost of different banking activities. And the middle two columns shows a positive coefficient. So you see 0.317 and 0.264. That shows that having more reserves, having more liquid securities on bank balance sheets is increasing rather than decreasing the marginal cost of lending to firms and the marginal cost of giving out mortgages. That shows that it's not the case, at least in the sample period we have, that having more reserves is making lending cheaper. In contrast, it's actually making it more expensive. And so it seems that relative to the two sets of theories, it is the set of balance sheet cost theories that is dominating the overall results. Now, quantitatively what these coefficients mean is that if you have 100 million increase in reserves for the average bank branch, the marginal cost of mortgages increases by 31.7 basis points and the marginal cost of loans increases by 26.4 basis points. Now, you may think these are basis points, so probably not something we want to worry about. But I would argue that it depends first on how many of those millions of reserves we are injecting. It would also depend on how the change in marginal costs is translating into changes in interest rates and how eventually that change in interest rates is translating into changes in quantities. Now, for that second part, remember that's what we did in the demand system where we estimated how a given change in interest rates translated into quantity changes depending on the elasticity of demand. So in the last section, what we do is to put everything we have so far together. We have a demand system that tells us how quantities change given interest rate changes and we have a cost function, a supply side, that tells us how when reserve quantities change how the marginal costs of lending, of taking out mortgages, of issuing deposits, how that changes. So we run a counterfactual analysis in which we inject the amount of reserves into our system as we observe in practice from quantitative easing over the years of 2008 to 2017. We inject the reserves, we observe, we let the system equilibrate what is the increase in marginal cost of everything. We see what's the increase in markup given bank's market power and then we see how the eventual changes in interest rates translate into quantity changes. So in the new equilibrium, what we find first is that if you have more reserves, you need to reward the holding of reserves more in a closed system. So relative to the market-wide risk-free rate, the interest on excess reserves or the reserve spread increases by an average of 16 basis points. So if you compare that to something like the interest on excess reserves minus the Fed funds rate spread, which in the data over this time period is 11.6 basis points, then I would say it's not exactly the same, but it's quite surprising that it's in the same ballpark because we have not used any related data in that sort. The correlation of these two data series is also very high over time. So we really seem to observe that as you have more reserves, you're forced to reward the holding of these reserves more and that in the market because the Fed is setting this interest on excess reserves, it's the Fed funds rate that is changing to change the spread. Now this initial change in the spread on reserves is passing through to different markets that banks operate in for deposits. The deposit spread increases by 12.7 basis points and for mortgages and loans, it's at 18.8 and 15.6 basis points respectively. So in terms of just the quantity, the price response, excuse me, we don't see such a big difference. So approximately deposits is the lowest and then mortgages is the highest, but approximately we're in the 12 to 18 basis point range. Now what is really different, however, is how much these rate changes pass through to the quantities. And here we find that it's really bank loans that are seeing the largest effect. Okay, for a dollar injected, we find a 19 cent drop in the amount of loans extended to firms, versus that for mortgages and deposits, those quantities are much less affected. And here the reason again goes back to the demand systems. We estimated we've found that loan demand is much more elastic than deposit and mortgage demand so that the similar amounts of rate pass throughs are going to have a much larger effect in the loan market. And once again, I stress that this is not the only channel of quantitative easing or of APP. Again, this is result of injecting reserves, right? In total, we would also expect some effects from the assets being purchased, expect effects from changes in the long-term interest rate and in the yield curve, and those are all important. But here we highlight one channel that was probably not really paid attention to before, which is that the addition of reserves, which have to happen if you, the central bank is purchasing anything, and if reserves are confined within the banking sector, that that has the side effect of not helping bank lending, but crowding out bank lending. And the effect is at 19 cents per dollar, and over time you see in blue and red we compare our projections and the data they align so that it really seems that over time we are seeing evidence of increases in reserves leading to declines in loan amounts. Okay, so again, this is the reserve supply channel of unconventional monetary policy. We think it's an additional factor that really should be considered. Then we think about how much QE should we be doing, what should the optimal size of bank balance sheet be, because bank regulation is in place and that's unlikely something we can change. We came to this conclusion not by looking at QE because we were worried about the endogeneity. We set up a demand and supply system that we estimated using exogenous variation, and we ran a counterfactual that showed us a dollar in reserves crowds at 19 cents of loans from bank balance sheets. I think implications of this real doubt going forward could be that the amount of reserves, if you have larger reserves, one of the costs is potential crowding out, but if you want to alleviate some of the negative effects, one thing that in the U.S. has been done during COVID is, for example, to exempt reserves from the supplementary leverage ratio regulation, and that is something one might want to think about. You want to keep having large central bank balance sheet sites going forward. The other possibility is to no longer restrict the access to reserves to the banking system. All of the problems that arise here are because you have a lot of reserves that cannot leave the banking system, and the banking system has a lot of costs in holding these reserves. Suppose you were able to have other type of intermediaries, say money market funds, have access to central bank reserves. Then the injection of reserves no longer have to be fully absorbed by bank balance sheets, and at least you can allow the market to tell who is in a better position to absorb these reserves and perhaps banks can then have a greater capacity of extending lending through your economy. With that, thank you very much. I really look forward to the discussion and all of your comments. Thank you. Thank you, Yeming. Your paper will be discussed by my colleague Anjesa Deonalo. Good afternoon, everybody. It's a great pleasure having the chance to discuss this super interesting paper. I think you got it already that from for this audience, for this place, this is really a topical paper, something that everybody should read in this audience. So this paper is about QE, or APP, any asset purchase program by central bank and zoom in on a specific characteristic of asset purchase, so the creation of reserves and the accumulation of central bank reserves on banks balance sheet. Banks balance sheet only because, as Yeming mentioned at the very end, these reserves, no matter who is the final counterpart in the asset purchases by the central bank, have to stay and have to be held on banks balance sheet. And under some circumstances, this means that QE translates to an expansion of the balance sheet of banks. So the question that Yeming and I called or asked in this paper is whether this increase in the supply of reserve is actually having an effect on other banks' decision, in particular about the decision of banks of providing loans, mortgages, and raising deposit. And they have a very top-provoking funding that actually increased in the reserve supply during the 2008-2007 period, crowded out bank lending. So what they show is that for every dollar of reserve that was injected, there was a 90-19 cent reduction in bank loans. And they call this the reserve supply channel, as Yeming mentioned several times. This is not capturing the overall effect of QE, likely for us, but it's rather showing an effect or a force that is counterweighting the stimulative impact of QE. And if you think about the overall amount of reserves that were injected, this is not a small number, but it has a very sizable effect. So what's the idea? What's the mechanism behind the emergence of this reserve supply channel? So the idea is that the injection of reserves and the expansion of balance sheet that comes with it is actually changing the cost of providing other banking services, loans, mortgages, and also deposit. And in principle, this effect will go either way. Reserves are a very safe and liquid asset, so banks with more reserves are banks that are better prepared to face, for example, liquidity shock, and so this should give them the ability to lend more, the ability and willingness to lend more. But the effect that Yeming and Erko Auto show is that it goes the other way round. So you have more reserves, and somehow this increased the cost of providing other services, so in particularly loans. And so the overall effect is the one that I mentioned at the beginning, is that they're crowding out of bank loans due to the increase in reserves. So I know that this doesn't really do justice to the paper and the authors and the great work that they put into their estimation, and I think Yeming did a great job summarizing the estimation strategy of what they do. So they build a structural model of the amount and supply of loans and other banking services, and they estimate this model through instrumental variables. And it's really a challenging task, and I think they tackle very, very well with a very sophisticated estimation strategy, which she went through in her presentation. So, and as I said already at the beginning, this is a very interesting and policy-relevant paper. So, and potentially present a controversial result, because as I said, this is something that I think no one in the central bank was expecting or paid too much attention to, and there's some fed colleague who actually in a very different setting prove a sort of opposite result so that actually this accumulation of reserve led to an increase in bank loans. So what I will try to do in my discussion is not to go through the estimation strategy, but rather try to focus on the mechanism behind the result and try to understand better why an increase in reserve on banks balance sheet is leading to a reduction in bank loans. So try to understand under which circumstances we should be worried about this reserve supply channel of QE, and one is that we can dream and sleep better. So let me sort of do a sort of list of basic ingredient that you need to get this reserve supply channel to emerge. So first of all, you need that the banks are not the ultimate seller of asset that are purchased by the central bank. So this is, for example, it's very different across the US and Europe where, for example, for treasury sovereign bonds, those in Europe were mostly held on banks balance sheet and they were the ultimate seller. And because if banks are the ultimate seller, essentially there's only a swap between treasuries and reserves which are very similar assets so there's no expansion in banks balance sheet. And this is an ingredient that is absolutely needed to have this reserve channel at play. And the second ingredient that you need is that there's some sort of constraint, as Heming pointed out, that the bank are facing and this constraint must be a constraint that really binds for a bank and also you need the reserves and loans enters into this constraint as a substitute. So if you increase the amount of reserves, you make the banks to be closer to the point in which the constraint binds or is no longer satisfied and then you have to reduce something else on the balance sheet or changing something else on the balance sheet for the constraint to be satisfied again. So this is what captured this substitution between reserves and loans and the existence of this constraint is what actually is the driving force behind the fact that holding more reserves actually lending more costly. So what this constraint could be? So Heming mentioned at the very beginning of our presentation a leverage constraint and in the paper they also mentioned they have a policy implications section at the very end of the paper in which they discuss what could be the source of this cost. And so let's think together about what the constraint that banks might be facing. So hardly this is a risk weighted or a liquidity constraint because reserves and loans do not enter as a substitute in this constraint. Reserves carry no risk weight and reserves are actually there to improve your liquidity position so they are not treated the same way as loans in those constraints. So as Heming mentioned, what a potential candidate or a suspect could be the leverage requirement or the supplementary leverage requirement, which as she mentioned was reserved at a different rhythm during COVID both in the US and in Europe and the Bank of England already in 2016 excluded reserves from the computation of the supplementary leverage requirement. So my point about this constraint is that it's there but entering to force in the US only in 2018 as a requirement. Of course banks knew already about this constraint. It was a rule already in 2014 and they probably are most likely front loaded. So this constraint might be a candidate. And what I think is that the sample period that they have that goes from 2001 to 2017 is really an exciting time for someone that is interested in bank regulation because this is the time in which we had the crisis, then we had basal tree regulation and then the proposal and then the implementation and entering to force of the bargeous requirement. So they have a very long sample time. And then I was wondering whether it would be possible to sort of zoom in or separate or zoom in on some shorter time period where regulation entering to force or also to try to understand which one of these regulatory constraints might be the one that is actually driving the result. Also because if you think of banks before the crisis and right after the crisis and nowadays or so closer to the end of your sample period the ability of banks to satisfy constraint were very different. So if I was checking for with European data so now the average European bank has an SRS LR of 5.2%. The requirement is only three. So they are way above the constraint which means that this is no longer binding. And I wonder when this happened so I think zooming in in shorter time period could give a better idea of which one of these constraints if it's really a regulatory constraint that is binding or is something else. I think this is the other point that I wanted to make is that it's about a balance sheet expansion. So in the asset purchases described in the US banks were not the ultimate counter party in the purchase of assets. They were acting as intermediaries with non-banks and I think you have very convincing data on this and you mentioned this at the very beginning of the paper and also in the policy discussion. So what happens essentially if we look at the bank's balance sheet was on the asset side we saw an increase of reserves and we saw something else also moving on the liability side of the bank's balance sheet which was an increase in deposit. And I didn't put the wonderful Essian metrics that you have but deposit and reserves actually have not changed the marginal cost of lending in an opposite direction. So if you have one euro more of deposit you actually reduce the marginal cost of lending. If you have one euro more of reserves you actually increase the marginal cost of lending. Of course the magnitude of the two effect is not the same but it's quite close. So I was wondering whether the exercise in the counterfactual which simulates an injection of reserves only but without thinking much about how this injection or expansion of reserve on the asset side is funded on the liability side whether it is saying anything to us in terms of the magnitude of the effect. I don't think it will change the direction but you might say something about the magnitude. And also the other point that I wanted to make is that looking at the liability side I know this is my bias, personal bias of always looking at the liability side but there are very different type of deposit and the one that banks were, they were backing up the increase in reserves were mostly wholesale funding. So something that is extremely roundable, unstable which is not really something that goes in the direction of pushing up your incentive to lend. So this might also be a force that is at play that is a consequence of the increase in reserves and of all the points that you are making about banks not being intermediary to these QE asset purchases and that could be an interesting angle to explore. Let me skip the smaller comments because I think we can discuss separately among the two of us so let me just summarize. I really enjoyed reading this paper and I think I was in the office reading it in the last few days and I think all my comments were, oh my god, really? Oh really, I really want to know more. So and you start really from the abstract where you want to, you think that is super, something that is super interesting and I don't think it's just because I'm in a policy institution who did QE but it's really because it's a very well written paper very interesting, very careful analysis and I think there are some avenues in which maybe not even for this paper but for some future work in which one can deepen the discussion about the underlying mechanism behind the result and for example explore this interaction in more details between QE, so monetary policy, and regulation. I think that will be really interesting to do and will also give the possibility to derive more to strengthen the policy implication of the paper altogether. So with this I stop here. Thank you. Thank you, Agnes. So for those of you who are listening online submit your questions via Slido and before I return back to Ye-Ming those of you who are on site here just raise your hand if you want to ask a question. So Ye-Ming, do you want to first reply to Agnes? Yes, thank you so much. I think that's the most important thing for such a thoughtful and kind discussion. I just want to quickly touch base on two things. I think we need to discuss much more on some of the deeper comments. Maybe let me start from the end and think a bit about the accounting. So I think as you correctly point out at least at the very beginning when there is an injection in reserves through the purge of securities and if these securities are originally held in the non-bank sector, the expansion on the bank's asset side in terms of reserves, the commercial bank's asset side in terms of reserves is automatically matched by an expansion in its liabilities, most likely deposits. It has to be just for things to be balanced that something's got a balance on the liability side and that should be one-to-one in its immediate mechanical effect. However, that does not mean that that amount of expanded deposits is there to stay. After the fact has occurred, the bank should equilibrate. It's going to look at, okay, this is how large my balance sheet now is, this is how much I want it to be or how much I can afford it to be. How do I adjust both on my asset and liability side to, let's say, a new equilibrium in which I no longer am obliged to hold on to all the deposits that initially expanded and I think what we want to understand is the world that has equilibrated rather than the very initial world right after the injection where I fully agree that it's like a one-to-one expansion in deposits. Now, as that said, it should still be that in the equilibrium-adjusted world, that could be a simultaneous expansion in deposits and that's something we definitely could think about running where we basically run the counterfactual with a simultaneous increase in reserves and an increase in deposits. And I fully agree that the liability side matters and actually, I think you pointed it out, but Viral and Raghu, actually, they agreed with your point so they wrote a very recent paper. You should all take a look at it if you haven't, but they basically look at the liability side. Potential side effects on the liability side where they argue that larger reserves could actually increase, let's say, instability through exacerbating the possibility front. So definitely, in the liability side, side effects may also be very important and so far we focused mostly on the asset side. Yes, also in terms of regulation, I agree, I think if you're a little bound by how many parameters we can estimate, but we can definitely try to have, let's say, time-specific coefficients that then can better understand over what time period, what regulations were at play and how large were these constraints at affecting lending. But we will need to try a bit to see how much flexibility the data gives us and how much power we have is the estimation. But once again, thank you so much. Is there's one question here in the back, Morten? Yeah, I was wondering about them. So the result you find that the elasticity is much higher for the loans. One interpretation of that would be that this is very costly because then the corporate sector, they can invest less. But I guess the other one is that large firms, they have very close substitutes to issue equity or corporate bonds and so on. So therefore the cost actually may be very small. So it will seem to be important to figure out what happens on the corporate side. That's very much true. So I think what we can say is that there is less, fewer loans that the banks are able to finance. But it would also be important to understand is that really a drop in the total amount of borrowing or is it substituting a way to cap to markets? Perhaps one way to understand the current results is to say that there could be some substitution and especially for the largest firms, I think probably the substitution will be very good. But that also means that if you do not have the option to substitute, if you're a smaller firm or if you're a lower-rated firm, you don't have a credit rating. If you're more credit constrained, that means you're going to be especially affected if banks are constrained from injection reserves. But I fully agree. Ideally, we have a bigger model that also has a capital market involved. Next question here in the middle. On your left. Great paper. So I don't know if you can look at this in the data, but you can possibly argue that reserve requirements force banks to drop the riskier loans, which are actually bad. They shouldn't have been made in the first place, but banks were risked doing risk-taking behavior, at least I guess during the financial crisis you can maybe argue for that. And that was the loans that dropped and that was actually the purpose of QE. So of course I'm playing the devil here. So is there any way for you to look at something about the quality or risk of the loans that were dropped? That's a very good point as well. I guess there's a deeper philosophical point as in do we want a very safe banking sector, which would be probably like a narrow bank, or do we care about like liquidity transformation and lending that the banking sector performs? I guess there's a sweet spot in between that banks should take let's say positive MPV projects and rather than finance a lot of zombies. The counterfactual currently cannot do this because we would have to model quality inside the supply side, but we should be able to look at the data around that time and just to see corresponding to the same time period because we do have granular loan-level data. What types of firms are likely to lose their financing and probably that can get to that. Thank you. Next question, Peter here in the middle. Left. So yeah, I liked a lot. So the paper, one question is, have you clarified what kind of assumptions you need to go from kind of analysis coming from local shocks to kind of general equilibrium effects that you kind of get to. So these elasticity's potentially are different. Yes, absolutely. So the assumption would be that the regions of the demand curves we have estimated and the regions of the cost functions we have estimated in response to local disaster shocks and in response to deposit growth from a bar tick shock are representative of the ones with a large injection in reserves. And of course that is not exactly true. Like we have a much larger magnitude, let's say for response that we have in the counterfactual than we have for an average deposit or loan market with the average shock that we experience. But with these shocks, we do have quite some variation. There are some very small shocks. Like you have a lot of rainfall locally and you do have a very small loss. But it is true that this data said it also has things like Hurricane Katrina, like very large scale events that led to very large losses in the local economy. And there for those types of shocks, we do see a much larger region of the parameter space being used to estimate the coefficients. But it is the assumption that yes, the response to the shocks that we use are able to predict, are able to represent what we would observe following a system-wide reserve injection. Next question on the way in the back. You mean, thanks for the great presentation. I was wondering about how you interpret the securities market in the context of your model. The reason is that it seems to me there is a marginal cost for banks of intermediating basically between the non-banks and the central bank in terms of balance sheet cost. But the bank seems to pass through this balance sheet cost to borrow us instead of the non-banks. So you could think of a market where because the banks have the power basically to do QE, they pass through the balance sheet cost to the non-banks instead of the borrow us as in your setting. That's a very interesting way to think about it. I think perhaps by review preference, the fact that we observe what we observe meant that they were unable to pass through everything to the non-banks. From some other work, my personal take is that they do have quite considerable amounts of market power over non-banks, but perhaps it's present but incomplete. Next question. Let me look at the slide. So there's one question on the way you separate out the amount in supply. How do you use the natural disasters to capture local demand? So also for sort of a European audience, could you elaborate a little bit more to what extent banking in the US is truly local? Because here we think of often you have these large banks that are basically active in different markets. That's a great question. I think it's not purely local for sure. I think what probably is more local if a disaster hits a region, it's the housing, it's the infrastructure in that place, it's the residential homes, all those things that need to be rebuilt. So I think that definitely is very local. Now, the assumption would have to be that it's those borrowers and those depositors that they go primarily to local banks in that county for which we would have the disproportionate effect. If they do also go to other banks in other counties, that is something we would have to net out. So I think what we use here, you can think of this as the relative increase in exposure of the local bank branch due to local disasters. And this is going to be a larger effect. The more locally oriented the banking sector is. So I guess if you're taking another economy or banks are even more local, probably this kind of shock is going to be even a better instrument at generating the variation. It would not be useful in a world in which there are no local markets at all and that everyone, no matter where they are, can go to any bank branch and has a similar propensity to go to any bank branch equally. Thank you. Next question is, so you abstract from loan maturity in your setup. So a very long contract is the same maturity. And so one question would be if one were to model some sort of term premium, at least in this house we've been saying one of the effects through which QE reaches different parts of the economy is also by extracting duration and it should in principle compress this term spread. So if we think of your model you would have two different loans with different maturity. Would that be another channel that would be worth looking at? It would probably go in the opposite direction. Right, I think it's an interesting heterogeneity because I guess there should be two differences depending on the maturity of the loan. One exactly is the one you said. If QE's goal is to flatten the yield curve then longer maturity loans may be benefiting more on the asset side let's say. But it should also be the case I guess longer maturity loans, they have a different duration risk, they have different considerations from the bank supply side and that also could affect how the cost function differs. The cost function parameters differs for these two types of loans. So there will likely be very interesting interactions there. We can try to separate those out. So we'll observe the maturities at issuance at least that were extended for these loans. Thank you for the suggestion. Okay, my last question is stealing from Agnes' discussion. If I sort of push the rationality provided in terms of comparing the US and Europe so to the extent that these banks are also sellers of securities Agnes was stipulating if I could be smaller so is that something you would agree with that if you were to run this type of estimates for Europe would you expect because of this to find smaller facts? I think literally or let's say first the mechanical effect, yes, right? So if you think x anti or else equal there are some banks who hold more of the securities that the central bank is buying relative to a banking sector that holds less. Yes, there's definitely the second one would respond by more given the channel I'm proposing but I think I guess it's important also to ask why a certain banking system is holding more of a certain security in the first place and if there's any anticipation effect that perhaps these are the things that the central bank going forward is more likely to buy that contributed to a larger holding of certain type of securities then I would just argue maybe that's just the intermediate step of the channel we're proposing remember the intermediate step has to be that the banks I guess are first buying it from their clients as a managers and then they're offloading it to the central bank so there's exist a very short period in time at least in the US most often the banks are holding these things on balance sheets and then perhaps that is a different time frame a different type of equilibrium that we see in Europe but I think yes and no depending on what the x anti expectations and incentives were for holding securities in the first place good well when I hear yes and no I'm thinking I think more research is needed so well thank you so much and of course for making this an interesting session thank you so much