Axiomatic equilibration, performed axiomatically

"the axiomatic imposition of equilibrium," over at Medium.com.

Can Google Trends be used to predict stock movements?

Probably not.

Macro muddles

Noah Smith has a new post which displays his great flexibility as a blogger. He's a fairly stern critic of modern macroeconomics, usually. This post is a more or less conscious effort to do mental gymnastics by suspending relevant sense data and seeing just how far it is possible, if one tries really hard, to offer a coherent defense of current practices in mainstream macroeconomics (read: DSGE modelling). One big positive: there was a financial crisis, and DSGE modellers deserve great credit as they have now noticed the importance of the financial system and made strides in including it (in some form at least) in their models. Another: macroeconomists, apparently, are trying to make their models simpler so they can be more useful to policy makers. I say apparently.

Because that's not what I heard at a recent conference from policy makers themselves. My latest Bloomberg column explores a little of what I heard from some of them about what they would like to see more of from macroeconomists.  More realistic finance and more simplicity were certainly on their list, alongside a lot more humility, more acceptance of uncertainty, and more models based on realistic human behavior. They said they haven't seen much of this yet:

... are the top journals in macroeconomics publishing useful work? Judging from a recent conference that included regulators from the U.S. Federal Reserve, the Bank of England, the International Monetary Fund and the Organization for Economic Cooperation and Development, the answer is not encouraging: Six years after a financial crisis that exposed fundamental flaws in the dominant economic theories and models, the profession has made little to no progress in correcting itself.

The policy makers said that top economics journals still aren't publishing research or providing tools that they can apply in their work. The dominant culture favors mathematically complex, intellectually stimulating theories over simpler, more useful ones. "We're in for a long siege,” as one of them put it, “during which useful economics that really works will remain out of the top theoretical journals.” [Read more]

That last quote really struck me. Useful work will stay out of the top journals. How then can they be considered top journals? What am I missing?

At that conference, I gave a short presentation during a panel session. I (cautiously) presented my impression that one reason economists don't want to move away from their models based on rationality and equilibrium is that, in doing so, they will inevitably have to give up some of their favorite theorems and hence be unable to make statements about the possible welfare implications of policies. Taking steps toward realism will invalidate the mathematical basis of their thinking. I see that as a good thing; many economists I believe find it downright frightening.

That is my impression as an outsider, but am pleased to see that economist Peter Dorman believes something similar, a point he made in commenting on Noah Smith's post (h/t Lars Syll):

“….macroeconomists are definitely thinking about heterogeneity.” Come on, you must surely see that one can have both heterogeneity and representative agents. If there are 300 million agents in an economy and you model them as two or three decision-makers, on balance you are doing a lot more homogenizing than heterogenizing. This matters because just about everything we know about complex systems tells us that the density of interaction effects is central. An economy of you, me and a few other people simply isn’t going to have the same dynamics as an economy of millions of interacting agents. This is true even if agent-based modeling turns out to be unproductive. It’s enough to know that the model people are using is systematically giving bad advice. Microfounding macro is a choice, and if the there aren’t any good microfoundations at hand, you don’t have to do it.

“….there’s no clear alternative [to rational expectations].” The previous paragraph applies here as well. If the only microfoundations you can find are empirically disconfirmed, regularly and broadly, then you may just have to postpone this microfounding business until you can come up with better models. Beyond that, I think the core problem is that the models are structured to permit the solution of equilibrium conditions, and that this imposes a restrictive framework for thinking about rationality, optimization. If the point were to model adjustment, we could use a much looser but more empirically defensible conception of rationality. Of course, that would also mean severing economic analysis from welfarism: we’d have to give up trying to answer questions like“what’s the welfare cost of this situation compared to the optimum?” In the end, the attachment of economists, micro and macro alike, to equilibrium models with rational agents is that they want to be able make definitive judgments about what society should do. I prefer Keynes’ dentists: they don’t tell you whether you have an optimal dental structure, but they can help you get the structure you tell them you want.

“….[macro] looks like a vigorous, energetic field full of excited young true believers and respected older figures who are still blazing new trails.” The more accurate criticism of macro is not that it is simply an ideological smokescreen or an unthinking herd, but that it operates on a tilted playing field. It is openly acknowledged that the “leading” (i.e. career-determining) journals have engaged in tendentious selection practices for the past generation. Lots of shoddy research (which in my book includes calibration exercises promoted as “testing” theories) has gotten the star treatment, alongside a stream of genuinely significant macro work. Ideologically loaded assumptions, such as those typically used in public choice, are dropped in without any justification. Not all macro is bad! But the problem is that (a) the bad stuff of a certain ideological orientation gets an extra push that the good stuff doesn’t get, and (b) there isn’t a clear process by which the bad stuff is weeded out over time as its badness becomes evident. We refight the same damned macro battles year after year.

Again, I think economists like to tell stories of a certain kind, stories that fit into a certain framework they find familiar, a framework that links up to all the nice (?) welfare theorems they learned as students. The only problem is that these theorems only apply to a fictitious world that is not at all like our world. So we have the top journals filled with useless theory. Wonderful.

Stop banking abuse with radical information transparency

Anyone who has read this blog consistently knows that I've written on and off about an important developing body of work showing how algorithms -- closely linked to Google's PageRank algorithm -- could be used to greatly increase transparency surrounding issues of systemic risk. The systemic risk linked to any one bank or even to any single financial transaction could be made apparent to everyone; call it "radical transparency." Coupled with other mechanisms, such transparency could provide a route to making the system safer on is own, through normal economic self organization. The idea, in essence, is to use computation to give everyone in the market the information they need to make better choices.

I've written about this here, here and most recently here. I've just put a kind of short summary article of all this up at Medium.com. But I'd also like to just quote some of the nice discussion from the most recent paper of Stefan Thurner and Sebastian Poledna. I think they describe the idea with beautiful clarity; so here's a whole big section. After describing various ideas currently under consideration for dealing with Too Big to Fail and systemic risk more generally, they note that..

No matter how well intended these developments might be, they miss the central point about the nature of SR, and might not be suitable to improve stability of the financial system in a sustainable way. SR is tightly related to the network structure of financial assets and liabilities in a financial system. Management of SR is essentially a matter of re-structuring financial networks such that the probability of cascading failure is reduced, or ideally eliminated.

Credit risk is the risk that a borrower will default on a given debt by failing to make the full pre-specified re-payments. It is usually seen as a risk that emerges between two counterparties once they engage in a financial transaction. The lender is the sole bearer of credit risk, and figures the likelihood of failed repayments into a risk premium. Lenders usually charge higher interest rates to borrowers that are more likely to default (risk-based pricing). Credit risk is relatively well understood, and can be mitigated through a number methods and techniques [18]. The Basle accords provide an extensive framework dealing foremost with the mitigation of credit risk [19–21].

When two counterparties are part of a financial system, for example as nodes in a financial network, the situation changes, and their transaction may affect the financial system as a whole. The lender no more is the sole bearer of credit risk, nor does credit risk depend on the financial conditions of the borrower alone. The impact of a default of the borrower is no longer limited to the lender, but it may affect the other creditors of the lender (who also lend to the same borrower) as well as their creditors, and so on. Similarly, the lender is not only vulnerable to a default of the borrower but also to defaults from all debtors of that borrower as well as their debtors, etc. In financial networks credit risk loses the local character between two counterparties, and becomes systemic.

SR is the risk that the financial system as a whole or a large fraction of it can no longer perform its function as a credit provider and collapses. SR is a result of the network nature of financial transactions and liabilities in the financial system. It unfolds as secondary cascades of credit defaults, triggered by credit defaults between individual counterparties. These cascades can potentially wipe out the financial system by a de-leveraging cascade [22–29]. It is obvious that lenders have a strong incentive to mitigate credit risk. In the case of SR the situation is less clear, since the loss-bearers will in general not be directly involved in those transactions that trigger systemic damage. It is not obvious which players in the financial system have a true interest to mitigate SR. Management of SR is foremost in the public interest.

It is important to note that SR spreads by lending. If a systemically risky node lends to a systemically non-risky one, the later inherits SR from the risky node, since if the non-risky borrower should (for whatever reason) not repay the loan, the risky node would trigger systemic damage. In this sense SR spreads from the risky through lending.

SR is predominantly a network property of liability networks. Different financial network topologies will have different probabilities for systemic collapse, given the link density and the financial conditions of nodes being the same. The management of SR becomes a technical problem of managing the network topology of financial networks. The goal is to do this in a way that does neither reduce the credit provision capacity, nor the transaction volume of the financial system. Data on the topology of credit networks is available to many central banks. Several studies on historical data show typical scale-free connectivity patterns in liability networks [30–35], including overnight markets [36], and financial flows [37]. As a network property, SR can be quantified by using networkmetrics [38, 39]. In particular a relative risk measure (DebtRank) can be assigned to all nodes in a financial network that specifies the fraction of SR they contribute to the system (institution- or node-specific SR) [39]. As shown later, it is natural to extend the notion of node-specific SR to individual liabilities between two counterparties (liability-specific SR), and to individual transactions (transaction-specific SR).

The central idea of this paper is to introduce an incentive structure in form of a transaction tax that dynamically structures liability networks such that SR is minimized. Since every transaction in a financial network has an impact on the overall SR of a system, we suggest a transaction tax on all transactions between any two market participants that increase the SR of the entire system. The size of the tax is proportional to the SR contribution of the particular transaction. Market participants looking for credit will try to avoid this tax by looking for credit opportunities that do not increase SR and are thus tax free. As a consequence the network arranges toward a topology that, in combination with the financial conditions of individual institutions, will lead to a defacto elimination of SR, meaning that cascading failures can no longer occur. In the spirit of risk-based pricing as it is used for credit risk, here we propose a systemic risk premium. It was shown in [39] that SR can be drastically reduced by reducing borrowing from systemically risky nodes. This is achieved by distributing SR evenly over the network and by preventing the emergence of systemically super-risky nodes. The mechanism works in a self-organized way: risky nodes reduce their SR because they are blocked from lending, non-risky nodes become more systemically risky through their lending. A SR premium encourages borrowers to borrow from safer lenders (since the borrower pays the tax). Further, lenders have an incentive to become systemically safe so that no (or only little) SRT is added to their loan offers, and they can offer competitive rates. Since mitigation of SR is foremost in the public interest we propose to charge a systemic risk tax as a margin on every financial transaction that increases global SR.

Of course it will take lots of further thought to bring this into a practical form. But big ideas always start out small.

Physics of Finance: Medium collection

I've started what is called a collection over at Medium.com. The collection's name is the same as this blog. To follow content added to the collection, go here and click the link. This IS an experiment, so I'm not sure where it will go, but this is how we learn, we hope. At Medium, anyone can submit a story to a collection and the collection editor acts as the curator. In time I hope it won't be just me writing there.

Economists' "just so" stories...

A "just so" story is one that gives a nice comforting but ultimately fantastic explanation for some puzzling and unexplained thing. Rudyard Kipling of course made the idea famous in his book of that name. The Leopard's spots, in his story, were originally painted on by an Ethiopian, after that Ethiopian had first painted himself black.

Modern economists have picked up the ball and now, with their sophisticated Dynamic Stochastic General Equilibrium models, tell similar just so stories to explain (after the fact, of course) how economies work. It's all comes down to a lot of infinite forward thinking, rational optimization and equilibrium. I do wonder in fact if there is anything that could conceivable happen in an economy that DSGE modellers wouldn't be able to "explain" after sufficient work. Indeed, that would be an interesting exercise for DSGE lovers: can you make a short list of economic happenings that would clearly be inconsistent with your theories?

On a related matter, I think there is something very fishy about economists' defense of DSGE models as being useful for "telling stories." I've heard this excuse several times recently. No, they may not find much empirical support, and no, they're not much good for prediction, and no again, no one on Wall St. uses them in making practical investment decisions. BUT STILL -- these things are really very useful because they let us tell stories about how the economy works. That to me smells rotten.

I've written an essay on these "just so" stories over at Medium.com. I'm experimenting with writing over there a little, and will continue exploring themes relevant to The Physics of Finance. I'll always put a link here so anyone interested can go through. 

Make banks less risky with a tax -- one they don't have to pay

I wrote recently in Bloomberg about a really cool proposal to introduce a new kind of tax on financial transactions. The tax would be specifically linked to how much systemic risk a transaction -- say a loan from one bank to another -- creates. It's easy to roll your eyes when you hear about transaction taxes, thinking A. it will never happen and B. it might not do much good even if it did. There are real reasons to believe that a transaction tax might damage market liquidity. But the thing I'm writing about here is VERY different.

This is a tax institutions DON'T HAVE TO PAY. Institutions that work hard to borrow and lend in a way that doesn't increase risk to the overall financial system (by piling up debt on particular institutions, for example) would end up paying no tax. The idea is to bring systemic risk into the pricing system so institutions have an incentive to avoid it. In so doing, you provide a mechanism for the entire financial network to reconfigure itself to have lower systemic risk. The paper I'm writing about proposes a concrete method to do this, although it would in practice require giving central banks more information on financial transactions of many kinds.

This is the kind of really creative thinking we need a lot more of. Read more here.