2024 Presidential Address “Macrofinance and Resilience”
Markus Brunnermeier
Princeton University
AFA President (2023)
AFA Business Meeting & Presidential Address
Saturday, Jan 06, 2024
5:30 pm – 7:00 pm CST
Grand Ballroom Salon E
Chapters
00:00 Introduction – Monika Piazzesi, President-Elect (2023)
06:41 Markus Brunnermeier, Princeton University
So now it’s uh my great pleasure and honor uh to introduce the president of the AFA uh Marcus runer in a moment marus will give this year’s presidential address let me introduce marus to you um marus grew up in Lans Hood a small but beautiful city in Bavaria uh along the
River Isa an hour from Munich he went to college close to home in Regensburg also in Bavaria he continued his studies in the US at Vanderbilt where he received a master in economics he then entered the European doctoral program first at Bon and then he finished his PhD at the lsse in
1999 since then he’s been at Princeton where he’s the director of the benheim center of finance and the Edwards Sanford Professor micus works on micro Finance his body of work has had a big impact on our thinking about financial crisis uh his paper is about about the 2007 financial crisis is a classic
Reference in many PhD courses it’s a great introduction into the concepts and mechanisms that were at work in 2007 it explains how an initially small decline in US house prices was able to amplify into a global financial crisis one key mechanism was a liquidity spiral
Um so even a very small decline in asset values uh can create funding problems when you have investors that are highly levered these investors have to either reduce their leverage or sell assets or both uh and so in a world without frictions there would be other investors
Who would pick up those assets uh and uh there wouldn’t be a huge asset price decline uh but in a world in which everyone May face some constraints asset values Decline and they can drop below their fundamental values and then you have a fire sale uh Marco’s earlier work
Uh which was published in 2005 so written way before the financial crisis uh had shown that it may be profitable for some investors to force others to unwind their positions at low prices and thereby engage in predatory trading his 2009 paper shows uh how fire sales can lead to higher margins and haircuts
Which further deepen funding problems in these situations so liquidity spirals involve negative externalities that investors have on each other when I sell I’m creating a problem for you for your funding uh and these externalities they’re typically not picked up by traditional measur of risk like value at
Risk um which focus on the risk of a single financial institution and so marus um came up with the idea of kovar uh a risk measure that takes into account spillovers on other financial institutions uh in describing these contributions I’m already emphasizing one big feature of marus uh which is his
Ability to come up with the best names uh you have to give it to him uh the names liquidity spiral kovar or his latest the resilience thing uh it’s just I can’t do it and I don’t know how you do it it’s amazing uh there’s genius in
These Concepts when I think about these Concepts uh I use marcos’s words I immediately adopt them and I feel like I’m mentally communicating with him about these topics um another big feature of MOS is that he’s a Craftsman uh in fact he’s a from a family with a long tradition of
Craftsmanship uh Marcus doesn’t work with wood like his father in many generations of previous Bruno Meers uh but he uh applies his craftsmanships uh to economic models uh and so if you look at his work on Financial frictions in macroeconomic models that’s just a beautiful example of craftsmanship um it
Just has a necessary amount of heterogeneity among agents uh it cuts through a whole bunch of complexity in financial markets and the real economy uh and then it just isolates the key Channel through which a negative shock affects net worth of banks uh and thereby lowers valuations and GDP uh so
It’s just a beautiful model and it just shows craftsmanship many of my students in the past were lucky to attend uh the summer school that marus teaches at Princeton and they all came back completely at awe with a frame framework with a way of thinking of uh amplification mechanisms through networks of
Banks at this stage uh you may worry that Germans are taking over the AFA uh that that’s because we all sound the same in English um uh in German we are shockingly different from each other uh Marco speaks Bavarian and very strongly so uh and he rols his r
Uh and I speak uh the dialect from Baden which is a totally different region also in the South and we we talk with Stefan Neo who is from venur or who speaks suian uh it’s very difficult to understand each other um but we’re all Southerners uh and so we uh listen with
Amazement uh to rri malander the new vice president uh and Anto chore the current editor of the JF uh who speak High German um but that’s because uh they’re from the north of Frankfurt which for us Southerners belongs to Scandinavia and so they’re not really Germans um and so we’re really from very
Different places in Europe so don’t worry about Germans taking over taking over the AFA so with these words let me pass the microphone to Marcos president of the AFA an outstanding economy whose work I deeply admire and a great friend [Applause] Marcus thank you Monica and thanks to all for making this
Possible I learned a lot from many of you directly or indirectly so I would like to talk uh about macro finance and resilience as Monica indicated already and I want to show start with a graph which I think is almost miraculous if you look at the real GDP
Of the United States in log scale for the last 100 years you can go further It lines up nicely on a line of course there’s some recessions here and there and it goes up and down two things stand out that how nicely L lines up and then
There are two deviations one is the Great Recession which we Reon experienced after 2007 2008 and one is a great depression and we only got out of it after the second world war and let me zoom in to the most recent years when you zoom in you see
That again you know you see this nice line and then you see a big break in 2008 and uh 20 2010 the recession but we never came back we never came back in terms of levels and we never came back in terms of growth rate and if you look
At the covid recession which in its size was much bigger but we came back to the old trend line now what I want to talk about is why is it the case that financial crisis are such resilience killer resilience by resilience I mean bouncing back after you hit by a shock
Can you bounce back or not and this is big so if you just look at the gap between the the GDP we would have in the United States if we would have followed the last trend line which we followed the last 100 years it would be almost four trillion dollars more that’s not
Accumulated that’s one year every year we’re missing $4 trillion dollar and this is accumulating every year so the blue shaded area is just the accumulation of that so it’s related to some extent to Long Run risk so the non- resilience is essentially the long run risk going on and you see this huge
Break in terms of levels and growth rates of the financial crisis so I would like to study understand more what this resilience thing is and why it can’t be bounced back and so for this I would like to Define it provide a measure highlight a little bit what’s the difference between
Risk management and resilience management and what’s the difference between macro and micro resilience and then I bring this to micro Finance models and first generation second generations and apply then the resilience Concepts across micro Finance so what’s resilience resilience is associated with a stochastic process is the stochastic process bouncing back
And the stochastic process can be anything can be a cash flow process can be a return process a GDP level or GDP growth rate process it’s all about bouncing back and resilience is really about adaptability of the system is the system able after a shock to change and
Adapt such that we can bounce back of course we have a lot of risk measures like the variance or value at risk expected shortfall what is really then a good uh re resilience measure is it a mean reversion a half life of a shock or some cumulative impulse response
Function the area under an Impulse response function and I would like to you know highlight a different measure and uh propose a different measure here so the first thing to realize when you think about resilience is that it’s relative to non-adapting what happens if you the system adapts is it coming back
Because of the adaptation but what is zero resilience what’s a benchmark that’s relative if the system does not adapt so the way to think about this if you think about the boxes in the lower part of the figure here you can have an Adaptive system there’s an exogenous
Process coming in and then there are many endogenous processes GDP process cash flow processes stock return processes coming out and then you have relative to the non-adaptive system where the system is not adapting and I call something zero resilience something has no resilience if you do the same as
A non-adaptive system and if you do better better than this if you bounce B better than this non-adaptive system then it’s a positive resilience and if you do worse the adaptation might also make the situation worse than its negative resilience and one way to measure this resilience would be to take
The benefits from adaptability so the benefits you have this adaptation might you know benefit things and you value this you take the present value of that so it’s in a sense it’s the area the discounted area under the path from recovering from that so essentially any resilience measure is about a measure
You want to collapse a whole process into a single number and that’s essentially one way to measure this and a zero resilience measure essentially is one where just the system does not adapt but of course it can also happen that the adaptation makes things worse because we react to shocks and make
Things worse and then you have negative resilience and it might be that we never come back so we are trapped so you end up in a trap so trap is a very important component to it now it could also be that once you hit the Tipping Point that
You have adverse feedback loops and the situation gets worse and worse and worse and in that circumstances this area actually if you know it goes into the future further and further it becomes minus infinity okay so this measure essentially captures both the positive zero is if you it’s a nonadaptive system
Negative if the adaptation makes situation was it could be so bad with big loops and tipping points that it goes to minus infinity now thinking from this going from risk management to resilience management it’s a shift in mindset you focus much more not on the risk which
Materializes in the next uh period but uh in the long run so essentially when you do risk management it’s all about the risk return tradeoff and you do it at time T what’s the expected return and what’s uh the risk at time T you can
Avoid risk and if you look at the figure here avoiding risk sometimes they cost you some expected return it might be like in this particular example the black line is no risk at all but you might want to go for the more resilient path which bounces back and ultimately
You might have a set back but if you can bounce back if it’s resilient it might be better off ultimately down the road so you want to exposure expose yourself to some risk but you would like to diversify risk and the diversification itself is different from a risk perspective than from a resilience
Perspective but the key difference to resilience is risk you decide at time T resilience do you decide at time t+ one how do you react to uh a shock so after you have the realization of the shock you adapt you change and that gives you resilience the adaptability gives you
This resilience so but at time T you have to do something already at time T you have to invest in your future adaptability in your ability to react in an appropriate way that the system can react in appropriate way that means you have to invest in substitute ability in
In the ability to scale up in scalability in the context of Finance you might want to invest in something which is more liquidity because it allows you to adjust and that’s really the important Insight here just to give you an example in from the real trade in
The National trade context if you have some Outsourcing going on use Source from China CH and Brazil you would say oh that’s the risk management I diversify across China and Brazil let’s say by importing from both countries half half if something happens in one country I still have the other country
Get still half of the input resilience management means I still Outsource to both countries but I actually Outsource in such a way knowing that I can scale up if something happens in one country I scale up from the other country I can easily substitute my inputs from one
Country to the other one I can easily scale up as well so this gives you this flexibility so you open many doors so they can easily switch afterwards after you see um the this resilience the other thing you want to do is you want to push away all these adaptability Inhibitors
Like the traps The Tipping points you want to build up buffers to some extent when you because if you were to hit a Tipping Point the whole system might spir out of control you get in these adverse feedback loops so that’s essentially what’s very important and there’s a connection to you know the
Mertonian hedging demand there’s a connection to Long Run risk elements to it so long-ter risk typically cannot diversify from a risk perspective you can essentially only manage long-ter Risk by improving your adaptability to changes and that’s what I mean by resilience management now resilience you might say
If I look at a system and I have subsystems I have an economy and I have you know sectors of an economy or have a portfolio big portfolio of parts of the portfolio so I’ve always subsystem if each subsystem is resilient it might make the bigger system also resilient so
How does it aggregate across units I guess and there is a a fallacy of composition in a sense having a lot of resilience at the lower level is counterproductive at the higher level and the easiest way to see it if you have an economy where each firm is very
Resilient so it can’t go bankrupt it bounces back very easily it does not mean that the macroeconomy is very resilient okay because then it’s the case that you cannot adjust across the sectors you have an apple and a banana sector both of them there’s some Shock happening which makes just bananas way
More productive than Apples you would like to adjust but if the you know the Apple sector is so resilient you can’t bring it down uh then it makes actually the whole macroeconomy less resilient so that’s uh a very important feature of resilience now having defined what resilience
What I mean by this and how it interacts I would like to put it into macro Finance models and in the first generation models and second generation models and see why we need the second generation models to understand resilience further so what I have in mind by micro Finance of course micro
Finance is a big church uh so there are a lot of things uh going many papers and you know big contributions there and you know I hope that it will flourish further and it’s a great field and I’m fascinated by it but it means it’s General equilibrium and it needs
Dynamics because in order to talk about resilience it has to be a dynamic system it needs hetrogeneous agents because they’re financial frictions and it it’s a core the financial frictions and if there’s a homogeneous agent he doesn’t lend and borrow to each other uh so you need some hetrogeneous agent and the
Frictions can come in different forms there can de issuance constraints like boring constraints or collateral constraints Equity issuance constraints or incomplete markets of uninsurable dis in radicals and the policy concerns is as I mentioned initially in this example if you look at the GDP growth of the United States or many other countries
You want to enhance growth and efficiency but you also want to have stability and resilience and you might also care about the equality in society or inequality and finally you might also care about having stable government financing okay now of course in micro Finance so it’s more than just macro and Fin here
Are two big bubbles there’s a huge intersection I that’s a quiz for tonight what this letters stand for um and it also touches upon monetary economics Public Finance but it touches on all areas within Finance intermediary Finance Corporate Finance household Finance behavioral Finance as prising all of them are part of
It now let me go to the first generation macro Finance model like kotaki mua Baki and type models where it’s essentially all it’s they all linearized log linearized are on the steady state so here in particular let’s say consider the kak there’s a temporary shock and
Then you bounce back and if the system were not to adapt at all it would follow this black line but the system is adapting and makes things situation worse so syst has a shock it’s only a temporary shock it leads to fire cells it amplifies things and it leads to a
Reaction light what’s depicted here and following this resilience measure the resilience measure would be then capturing this red area under the black line and you can see you can it has an amplification component has a a propagation component to it and it makes the whole situation worse if you go to
BGG Bak gilis you have the same phenomenon going on in a DSG setting with price thickness and other elements in bu type models you have uh more you have the incomplete markets setting which leads to precautionary savings and depresses the risk for interest rate uh in these models now
What you can do and what you learn a lot already in terms of resilience you could say here the resilience is captured by mean reversion so the mean reversion would be a good measure or you can also take this this area under the black line but what you cannot analyze in this
Linearized models is something like traps or tipping points because they’re highly nonlinear and this I think this are very important to say why is the case since 2008 we’re still at a much lower growth rate of GDP why are we still at a much lower level for so long
Um and it’s the other the other thing which is case in the in this models that once there is a shock in youa downturn people anticipate perfectly well with perfect foresight how you go back to this steady state okay there’s no uncertainty anymore there’s no uncertainty about um going
Forward now if you want we want to analyze the traps and the Tipping points we have to go to Second Generation models and these are the nonlinear models uh kicking in so show here’s a small shock you see it is Epsilon shock it’s Amplified but then it bounces back
And also you know the blue line is the Tipping Point what’s on the other side on the right side of the figure is uh the drift of the system you can see there’s a state variable let’s say x and then there’s a drift on the Y AIS and if
The shock is like this Epsilon in this other figure then it is the case that the drift will be positive and you drift back to this stochastic steady state so whenever you’re shocked the X goes down but then the drift is positive and then you drift back now but if you have
Linear models then the drift would always be linear in that space like it is in this example um it is linear in in this area and it would be in the first generation model would be always linear the further you shocked away from the system the faster you drift
Back now if if you have tipping points like large shocks where there’s an Epsilon shock and then you hit the Tipping Point then instead of going back actually go further down so the situation gets much worse or in this other diagram if there’s a shock which
Pushes you all the way down here then you go beyond the Tipping Point the drift is negative then the system is after drifting away and it might be you might be absorbed in this trap here okay so that’s essentially you need is nonlinearities that’s very first or important to understand tipping points
And tipping points are also present if you have models with bubbles where you know at some point the bubble is big enough you hit the Tipping Point and then it burst and depending how large the bubble was it might drag the whole system down and that argues for your
Lean versus clean whether you want to interact already through micr potential regulation early on to slow down the build up of the bubble or prevent the bubble to build up in order to make sure you not hit the Tipping Point when the bubble really bursts now another element is this were
Tipping points another one is traps so there are two types of traps one trap is when the system the macroeconomy or the state of the firm or whatever it is um hits a trap it can’t get out of it anymore so that’s a perfect trap with no
Escape it’s an absorbing State you can’t get out of it but there also traps where you actually dra for a while for a long while and then you can still get out of it and one way to depict that is to have station distribution of the system where
It’s double hump shaped so if you look at this PDF here of of the station distribution so you most of the time you’re around here um but sometimes occasionally you dap down here and you spend a lot of this time down here so station distribution reflects how much
On average the system spends um if you let it run for a long time and it’s stuck here for a long time so that’s another way what often happens in this micro Finance model of this net worth trapped when the net worth is very low you’re trapped to get out of it
Now with these two types of the first generation models where it’s everything is linear mean reversion after there’s a shock there’s no additional shock uh the second generation essentially is the Tipping points and traps can be well analyzed um it’s also very important for the generation second generation model
Said you have some volatility Dynamic so the volatility is not constant the volatility moves around as well so why is this very important in this because you have a risky recovery you have a shock and then the recovery you don’t know when how long the recession will last will there be
Another shock and there’s a risky recovery and everything is time bearing not only the first moments but also the second moments the risk itself so the exogenous risk might not be time wearing but there’s endogenous risk from coming from the system because everything is Amplified and a lot of this timing risk
Comes through financial friction in particular you have higher exor risks then margins might go up or the loan to value ratio might go down leverages going down people start fire selling and then uh the risk the Indulgence risk goes up that tightens margins further loan to value ratios go down further and
You get these margin spirals you can also call it leverage Cycles or repo runs uh these are all just different names for the same thing but the question What mechanisms and the literature identified many different mechanisms um what why these debt constraints might be more binding when
Volatility goes up it could be a value at risk constraint it could be like it could be thatb becomes more informationally sensitive that’s why the market breaks down it could be because that overhang problems kick in that actually makes the whole constraint that leads to this time bearing endogenous
Risk in the system and if you have this timeing indous risk in the system it also means that people cut back on their consumption and they want to do more precautionary Savings in some safe asset and that essentially um leads to more depressed situation and also prolonged depressed situation this is like a
Resilient Inhibitors now going to the second generation models everything is uncertain so rather than having impulse response curves where say there’s a shock and then we walk back deterministically uh you want to depict things with a generalized impulse response functions like this fan charts where you depict the different
Between the distribution which would play out if um there were no shock and the distribution which plays out when there is a shock and the difference you can you depict in this uh fan charts now one important element of resilience is to understand safe assets okay and this is very important in sense
So it’s safe asset it has a different take on it it’s not about the safe return the safe asset is special because what you can do with a safe asset okay so take a model where the incubate markets there are frictions going on uninsurable distic risk so everybody of
Us faces some funding shock which you cannot ensure with a bank or anything anybody else and of course the way you handle this you save exante precautionary uh with some safe asset and so the agents cannot ensure so what they do is they save exan precautionary
And then when they phas a shock they adapt just sell the safe asset and get resources and then can readapt the system so this insuring against ID syncratic risk is essentially resilience because it’s essentially about adaptability at t+ one as I mentioned before now if you have a safe asset the
Safe asset might still gives you some cash flow some interest payment but what it really gives us is the service flow that you can adapt you can change afterwards and so I would like to you know show you the different asset pricing model so typically when we do as
The the price is just expected present value of the cash flow with respect to the indidual SDF but if you write it with respect to a repetive agent SDF it’s actually it’s written as the present value of the cash flow plus the present value of the service flow and
The present value of the service flow comes from this ret trading that we can ensure each other through retaing later on so some of you get a positive shock others get a negative shock we know we will get this in the future and then we can retrade it so even though this asset
Does not give us any cash flows it gives us some very nice service flow that we can ensure each other even though markets are exan incomplete so that’s the value of a safe asset is to a large extent driven by this present value of service flows now consider a situation that tic
Risk gets worse whenever we go in a downturn seems very reasonable now when a recession hits we have much more etic risk we more likely to go bankrupt in whatever is going on then actually you really value this service flow much more so that means in downturns this component of the value of
Or the price component of the safe ad is expanding that means it gives you more in downturns the value goes up it gives you a negative cap M beta so tically it gives you a good insurance resilience instrument from a hedging perspective from an aggregate risk perspective it e
Risk goes up it is also a good hatching instrument so a safe asset gives you individual resilience and an aggregate hatching tool and for both this comes ully out of the system now the other thing is what’s special about safe assets so it’s it’s a very good tool for individual risk to
Uninsurable risk and it’s also a tool to have a negative beta and it’s also touched safe assets are often associated with bubbles okay why is that there’s a natural complementarity between Bubbles and safe ass assets so what a bubbly in order for a bubble to sustain in the long run it
Has to the return on the on this bubbly asset R has to be smaller than the growth rate of the economy and if the bub is attached to the safe asset the expected return on the safe asset has to be smaller than the growth rate of the
Economy so it has to and it is depressed it is depressed for several reasons so one if there’s a lot of uninsurable e gratic risk people want to save more and by Saving more you drive down the risk- fore interest rate that makes it easier to SA if is R smaller than G
Condition but it also means and as I mentioned the safe AET has a negative beta that also makes the expected Return of the safe asset go down so all of that makes it much easier for the safe asset to be bubbly than other assets okay and of course there might be an additional
Convenience yield on that that also makes it easier to have a bubble on that now say from an macro perspective safe AET also have some problems with resilience because you might lose the safe asset status when you hit a Tipping Point point and you lose the safe asset
Status your country’s Government Bond is suddenly not a safe asset anymore people run away from that when is that that’s for example When government debt becomes informationally sensitive and when that happens suddenly aomc information kicks in and then you don’t the B spread widen and you can’t
Retrade it so fast anymore and I told you the key as the key benefit of safe asset you can retrade it very easily at the low bit mask spread and so if you can’t rate it anymore then actually the safety is lost you lose safe asset set
And the service flow goes away okay it could also be that the bubble component of save asset collapses all of this gives you less resilience on a macro level another problem with safe asset is the asymmetric Supply there’s a l lot literature saying we don’t have enough
Safe assets there a shortage of safe assets particular after the global financial crisis and but the US Treasury has issued trillions of extra debt so I would argue it’s more the asymmetric Supply on that uh so it says and U so the way to see it there are certain
Countries when is a downturn people value the service flow the safety flow much more and then they’re flying to Safe asset there’s flight to safety that benefits these countries but it hurts the other countries where the money is Flowing out okay so this Astic supply of
Safe asset is very important this so let me summarize so the rring gives the fundamental resilience that would make safe asset the resilience instrument from a micro perspective but from a macro perspective there are also some dangers because you might lose the safe asset status that’s a danger and this
Asymmetric Supply makes it which entity can provide the safe asset if it’s a government for one country it’s symmetrically distributed across the country but in an international perspective it’s only for certain countries which can provide the safe asset and downturns now so far I haven’t really talked much about the financial sector
And I think the financial sector is very important to take into account essentially the financial sector is creating a lot of the safe assets is creating liquidity creates money the liability side focusing on the liability side of the financial sector and the big debate is of course who should create
Money should it be the public sector central banks governments or should it be private Banks and which system is more resilient provided to save asset by the public or the private sector or money by the and of course it relates to literature on narrow banking uh as opposed to fractional Reserve banking
Where essentially only the public sector can provide money it relates to all the new thing on cbdc Central Bank digital currency which is extra money created by uh the public sector as opposed to bank deposits created by the private sector so all of these are very important
Issues in terms of resilience so let me this interaction between price and financial stability and the private money creation and how this might hurt resilience let we show how this uh plays out so when there’s a shock the banks might shrink the balance sheets they have to shrink the balance sheet and Del
Lever of course on the asset side of the bank’s balance sheet s there will shrink uh the balance sheet of course and this will lead what we talked about uh amplification in levels but also in volatility and this will create a rich volatility Dynamics and the banks will
Actually be not able to absorb all Le inic risk from the in Diversified away that’s one role of the banks is to diversify some of the graic risk away they might not be able to do so but what’s also very important is when Banks shrink the balance sheet or the
Financial sector more generally they ALS Al shrink the liability side of their balance sheets so they create less money so money supply declines in the economy on top of it because the financial sector takes on less UC in C risk from the firms and the households these firms
And households that would like to shift their balance their portfolio more towards a safe asset to more towards U money so there’s increase in demand of money so you have a decline in the supply of money and an increase in demand of money that means that leads to disinflationary pressure and this
Disinflation hurts the banks again because the banks have a lot of nominal liabilities so they have some claims nominal and real claims on the asset side and the liability side of mostly nominal claims so that hurts them again and there’s this Paradox of prudence the
Banks each Bank might say oh I have to shrink my balance sheet and that’s actually prudent from an ilal Bank perspective but it makes it at the macro level imprudent that’s this you know this Paradox of Bren things of course if you add runs to it and other
Nonlinearities it adds to um more complexity now what the macro Finance literature has is going to essentially uh it has to split up to the the financial sector in many sub sectors so of course the banks within Banks there’s a lot ofinity the traditional and Shadow Banks pension and life insurance asset
Managers and so forth and to put this in the macro model is very challenging because essentially you need for each sub sector you need a a state variable and you know it’s very hard to handle macro models with with so many state variables but you know newest technology
Technology with a deep learning neural networks allows now to handle this uh multiple State variable micro models and this this systemic risk on spillovers from One bank to another bank or from one sub sector to another sub sector depends very much on the network structure and the design of the
Financial sector so here just very stylized there on the left side I have a centralized network and on the right side I have a distributed Network and of course the centralized network is way less resilient if you take the center point out the whole network is going
Down if you have reduced the Abed Network it’s much more resilient so that’s uh the network structure matters a lot for resilience as well so finally uh and I indicated already this was about the financial sector what’s about the public sector and the policy design what’s about the governments and central
Banks and of course one big issue is government debt how to connect that and of course it’s connecting to the Public Finance literature as well I mentioned already the exorbitant privilege if you able to be able to issue a safe asset you have this exorbitant privilege possibly a bubbly asset that solves this
Um recent debt valuation puzzle but it also the resilience is also what the that maturity um you have to issue there’s a lot of open question what’s the optimal debt maturity from a resilience perspective how does the debt maturity and also the debt structure more generally have spill over to the
Financial sector then spill back to the official sector that’s connected to this diabolic D Doom Loop sov NEX Bank Nexus literature and of course key on that is micr potential policy if you tighten micr potential policy too much you get into Financial repression of course you favor than much more debt financing but
This might actually trigger some other problems and lend of Last Resort uh design bailout policy that essentially will then play out will affect how the financial sector will respond to that and how the indulgy networks will form the financial sector networks will form it has also huge implications how to
Conduct monetary policy traditionally monetary policy is very much focused on managing the risk-free rate and but to some extent through QE the term premium but you know the emphasis much more focusing on risk Premier as well because by for managing the risk Premier you manage also the
Ulous risk and also the the price of risk so you would like to do occasionally redistributive Monet policy which lowers the risk premium in order to give more resilience to the financial sector and essentially you have to focus a bottleneck up approach you have to analyze where is the bottleneck which
Sectors in the economy or sub sectors in the economy are balance sheet impaired and you have to fix that otherwise you drag the other parts down with it QE and QT uh is a classic example of the global financial crisis the housing sector was balance imp paired so you want to
Actually buy mortgage back Securities to prop up and help the housing sector uh or the households who own and have uh too highly in in level AED housing if uh you don’t want to necessarily buy uh treasuries because it doesn’t really help anybody’s balance sheet and it also
Has huge implications on the size and the equity of the center bank’s balance sheet so let me conclude so I try to convince you that resilience in particular Financial resilience is first order important for macro resilience can be applied to any stochastic process and how to think about resilience I think I
Would like to invite you to think more about it in the context of many changes we go through many big transition phases now B the green transition artificial intelligence we need systems which are resilient and I think there’s a lot of things to be done there’s only the first
Steps on this we should actually not only do risk management we should also do some resilience management think about how we can make the system more adaptable invest in adaptability and flexibility liquidity and this like that Point issues like traps tipping points and other resilience Killers that’s the
Focus point it’s not about how risky something is if you can come back from that that’s a fine if you can adapt to it take the risk if you can’t if you’re trapped if you hit a Tipping Point and the whole system Smiles out of control
That’s I think what you have to focus on and the safe assets with its negative beta they individual resilience and on the macro level they help resilience tool because they give this exorbitant privilege if you have this exorbitant privilege in times of recessions it’s very easy for the Finance Minister or
The US Treasury sec to run a stimulus program and that enhances resilience again if you from a country which does not have this privilege you have to run austerity programs and that pushes the whole thing down it gives you anti- resilience and you might even lose a
Safe asset status finally there’s a lot of resilience belows within the financial sector and also then to the macroeconomy as I mentioned various policies monetary fiscal regulation Financial regulation fiscal policy they all feed into resilience thanks for your [Applause] attention I’m handing maros and I plag
For the service for the AFA thank you very much for your presidency and also the AFA fellow award which turns out to be heavier so it’s right here so thank you very much uh thanks for coming there’s a reception outside there’s a reception outside see you there thanks