Machine Learning for Financial Crises

Below are the slides from my discussion of Helene Rey et al., "Answering the Queen: Machine Learning and Financial Crises", which I gave a few days ago at a fine NBER IFM meeting (program and clickable papers here). I also discussed it in June at the BIS annual research meeting in Zurich. The key development since the earlier mid-summer draft is that they actually implemented a real-time financial crisis prediction analysis for France using vintage data, as opposed to quasi-real-time using final-revised data. Moving to real time of course somewhat degrades the quasi-real-time results, but they largely hold up. Very impressive. Therefore I now offer suggestions for improving evaluation credibility in the remaining cases where vintage datasets are not yet available. On the other hand, I also note how subtle but important look-ahead biases can creep in even when vintage data are available and used. I conclude that the only fully-convincing evaluation involves implementing their approach moving forward, recording the results, and building up a true track record.

Volatility and Risk Institute

NYU's Volatility Institute is expanding into the Volatility and Risk Institute (VRI). The four key initiatives are Climate Risk (run by Johannes Stroebel), Cyber Risk (run by Randal Milch), Financial Risk (run by Viral Acharya), and Geopolitical Risk (run by Thomas Philippon). Details here. This is a big deal. Great to see climate given such obvious and appropriate prominence. And notice how interconnected are climate, financial, and geopolitical risks.

The following is adapted from an email from Rob Engle and Dick Berner:

The Volatility Institute and its V-lab have, for the past decade, assessed risk through the lens of financial volatility, providing real-time measurement and forecasts of volatility and correlations for a wide spectrum of financial assets, and SRISK, a powerful measure of the resilience of the global financial system. Adopting an interdisciplinary approach, the VRI will build on that foundation to better assess newly emerging nonfinancial and financial risks facing today’s business leaders and policymakers, including climate-related, cyber/operational and geopolitical risks, as well as the interplay among them. 

The VRI will be co-directed by two NYU Stern faculty: Nobel Laureate Robert Engle, Michael Armellino Professor of Management and Financial Services and creator of the V-lab; and Richard Berner, Professor of Management Practice and former Director of the Office of Financial Research, established by the Dodd–Frank Wall Street Reform and Consumer Protection Act to help promote financial stability by delivering high-quality financial data, standards and analysis to policymakers and the public. 

The VRI will serve as the designated hub to facilitate, support and promote risk-related research, and external and internal engagement among scholars, practitioners and policymakers. To realize its interdisciplinary potential, the VRI will engage the expertise of faculty across New York University, including at the Courant Institute of Mathematical Sciences, Law School, Tandon School of Engineering, Wagner School of Public Policy and Wilf Family Department of Politics in the Faculty of Arts & Science. 

JFEC Special Issue for Peter Christoffersen

No, I have not gone into seclusion. Well actually I have, but not intentionally and certainly not for lack of interest in the blog. Just the usual crazy time of year, only worse this year for some reason. Anyway I'll be back very soon, with lots to say! But here's something important and timely, so it can't wait:

Journal of Financial Econometrics

Call for Papers

Special Issue in Honor of Peter Christoffersen

The Journal of Financial Econometrics is organizing a special issue in memory of Professor Peter
Christoffersen, our friend and colleague, who passed away in June 2018. Peter held the TMX Chair in Capital Markets and a Bank of Canada Fellowship and was a widely respected member of the Rotman School at the University of Toronto since 2010. Prior to 2010, Peter was a valued member of the Desautels Faculty of Management at McGill University. In addition to his transformative work in econometrics and volatility models, financial risk and financial innovation had been the focus of Peter’s work in recent years.

We invite paper submissions on topics related to Peter’s contributions to Finance and Econometrics. We are particularly interested in papers related to the following topics:

1)   The use of option-implied information for forecasting; Rare disasters and portfolio
management; Factor structures in derivatives and futures markets.

2)   Volatility, correlation, extreme events, systemic risk and Value-at-Risk modeling for
financial market risk management.

3)   The econometrics of digital assets; Big data and Machine Learning.

To submit a paper, authors should login to the Journal of Financial Econometrics online submission system and follow the submission instructions as per journal policy.  The due date for submissions is June 30, 2019.  It is important to specify in the cover letter that the paper is submitted to the special issue in honor of Peter Christoffersen, otherwise your paper will not be assigned to the guest editors.

Guest Editors

•    Francis X. Diebold, University of Pennsylvania

•    René Garcia, Université de Montréal and Toulouse School of Economics

•    Kris Jacobs, University of Houston

Gu-Kelly-Xiu and Neural Nets in Economics

I'm on record as being largely unimpressed by the contributions of neural nets (NN's) in economics thus far. In many economic environments the relevant non-linearities seem too weak and the signal/noise ratios too low for NN's to contribute much. 

The Gu-Kelly-Xiu paper that I mentioned earlier may change that. I mentioned their success in applying machine-learning methods to forecast equity risk premia out of sample. NN's, in particular, really shine. The paper is thoroughly and meticulously done. 

This is potentially a really big deal.

Machine Learning, Volatility, and the Interface

Just got back from the NBER Summer Institute. Lots of good stuff happening in the Forecasting and Empirical Methods group. The program, with links to papers, is here.

Lots of room for extensions too. Here's a great example. Consider the interface of the Gu-Kelly-Xiu and Bollerslev-Patton-Quagvleg papers. At first you might think that there is no interface. 

Kelly-Xiu is about using off-the-shelf machine-learning methods to model risk premia in financial markets; that is, to construct portfolios that deliver superior performance. (I had guessed they'd get nothing, but I was massively wrong.) Bollerslev et al. is about predicting realized covariance by exploiting info on past signs (e.g., was yesterday's covariance cross-product pos-pos, neg-neg, pos-neg, or neg-pos?). (They also get tremendous results.)

But there's actually a big interface.

Note that Kelly-Xiu is about conditional mean dynamics -- uncovering the determinants of expected excess returns. You might expect even better results for derivative assets, as the volatility dynamics that drive options prices may be nonlinear in ways missed by standard volatility models. And that's exactly the flavor of the Bollerslev et al. results -- they find that a tree structure conditioning on sign is massively successful.

But Bollerslev et al. don't do any machine learning. Instead they basically stumble upon their result, guided by their fine intuition. So here's a fascinating issue to explore: Hit the Bollerslev et al. realized covariance data with machine learning (in particular, tree methods like random forests) and see what happens. Does it "discover" the Bollerslev et al. result? If not, why not, and what does it discover? Does it improve upon Bollerslev et al.?

Machines Learning Finance

FRB Atlanta recently hosted a meeting on "Machines Learning Finance". Kind of an ominous, threatening (Orwellian?) title, but there were lots of (non-threatening...) pieces. I found the surveys by Ryan Adams and John Cunningham particularly entertaining. A clear theme on display throughout the meeting was that "supervised learning" -- the main strand of machine learning -- is just function estimation, and in particular, conditional mean estimation. That is, regression. It may involve high dimensions, non-linearities, binary variables, etc., but at the end of the day it's still just regression. If you're a regular No Hesitations reader, the "insight" that supervised learning = regression will hardly be novel to you, but still it's good to see it disseminating widely.

Safe Assets

Gary Gorton has a fascinating new paper, "History and Economics of Safe Assets", which contains the quote of the week: "...almost all of human history can be written as the search for and the production of different forms of safe assets".  Not sure that's the first cut I'd take at "all of human history", but it's certainly an interesting perspective.  As the old maxim says: "When you have a hammer, everything looks like a nail".

On the Great Financial Panic of 2007

(a) I've always felt that the "Great Financial Panic of 2007" was a good old-fashioned banking panic, even if the modern version at first looks quite different from those of the nineteenth and early twentieth centuries. Gary Gorton's wonderful book, building on his earlier research, gets it exactly right:

"Holders of short-term liabilities...refused to fund "banks" [that is, various vehicles in the shadow banking system] due to rational fears of loss. ... As with the earlier panics, the problem at root is a lack of information."

(b) Simultaneously, I've always felt that the Great Panic of 2007 was largely driven by "too big to fail" (TBTF) (e.g. see Gary Stern here), which creates incentives that promote excessive risk-taking. (If you win, you make a fortune; if you lose, you get bailed out.)

What didn't occur to me until a dinner with Gorton during the 2015 PIER Workshop is that (a) and (b) are largely incompatible. That is, if the Panic of 2007 really is like those of the nineteenth century, then it can't have been driven by TBTF, which didn't exist back then. And the Panic of 2007 really was like those of the nineteenth century. 

So my view has evolved significantly: TBTF may well have made the Great Panic more likely than it otherwise would have been, and TBTF may well have increased its severity relative to what otherwise would have been, but TBTF simply can't be "responsible." Thanks, Gary for pushing me forward.

Bond Yields, Macro Fundamentals, and Policy

Greetings my friends from Eurovision in Vienna. Yes, OK, that's not exactly the real reason I'm here, but still...

As I said in an earlier post that stressed DNS/AFNS yield-curve modeling with the zero lower bound imposed, "although Nelson-Siegel is almost thirty years old, and DNS/AFNS is almost a teenager, interesting and useful new variations keep coming." Another intriguing DNS/AFNS literature strand concerns the interaction of bond yields and macro fundamentals. That's hardly a new area, but recent work has some interesting twists.

Mesters, Schwaab and Koopman (2015) (MSK) focus on the effects of central bank policy on bond yields. There's lots of interesting new tech (stochastic volatility in measurement errors, interactions with non-Gaussian variables, a novel importance sampler for likelihood evaluation, ...). But most interestingly, MSK explore not only conventional policy tools like the overnight lending rate, but also direct measures of bond purchases.

MSK build on Diebold, Rudebusch and Aruoba (2006) (DRA), but the DRA emphasis is different. DRA were interested in whether and how the yield curve is linked to "standard" macro fundamentals. So DRA emphasized inflation, with an eye toward the yield curve level, and real activity, with an eye toward the yield curve slope. DRA also included an overnight lending rate, but certainly no measures of bond purchases.

Lots of interesting MSK-style work remains to be done. For example, someone needs to do an MSK-style analysis in a shadow-rate model that respects the zero lower bound and imposes no-arb. Also, someone needs to explore both causal directions more thoroughly. (Of course central bank bond purchases might influence the yield curve, but so too does the yield curve influence central bank bond purchases.)

Nassim Taleb Graphic

This arrived a couple weeks ago from Nassim Taleb. Regardless of where your view falls on the black swan spectrum, I hope you'll like the graphic. One hallmark of a good graphic is that it repays careful study, as with a good map (which is a good graphic). Nassim's Genealogy certainly passes that test. I found myself thinking about its contents and assertions for a long time. (You can blow it up in your browser by clicking on it. That should do the trick, but if it's still not big enough, start hitting ctrl+.)   

Quantitative Tools for Macro Policy Analysis

Penn's First Annual PIER Workshop on Quantitative Tools for Macroeconomic Policy Analysis will take place in May 2015.  The poster appears below (and here if the one below is a bit too small), and the website is here. We are interested in contacting anyone who might benefit from attending. Research staff at central banks and related organizations are an obvious focal point, but all are welcome. Please help spread the word, and of course, please consider attending. We hope to see you there!

FinancialConnectedness.org Site Now Up

The Financial and Macroeconomic Connectedness site is now up, thanks largely to the hard work of Kamil Yilmaz and Mert Demirer. Check it out at http://financialconnectedness.org. It implements the Diebold-Yilmaz framework for network connecteness measurement in global stock, sovereign bond, FX and CDS markets, both statically and dynamically (in real time). It includes results, data, code, bibliography, etc. Presently it's all financial markets and no macro (e.g., no global business cycle connectedness), but macro is coming soon. Check back in the coming months as the site grows and evolves.

Models Didn't Cause the Crisis

Some of the comments engendered by the Black Swan post remind me of something I've wanted to say for a while: In sharp contrast to much popular perception, the financial crisis wasn't caused by models or modelers.

Rather, the crisis was caused by huge numbers of smart, self-interested people involved with the financial services industry -- buy-side industry, sell-side industry, institutional and retail customers, regulators, everyone -- responding rationally to the distorted incentives created by too-big-to-fail (TBTF), sometimes consciously, often unconsciously. Of course modelers were part of the crowd looking the other way, but that misses the point: TBTF coaxed everyone into looking the other way. So the key to financial crisis management isn't as simple as executing the modelers, who perform invaluable and ongoing tasks. Instead it's credibly committing to end TBTF, but no one has found a way. Ironically, Dodd-Frank steps backward, institutionalizing TBTF, potentially making the financial system riskier now than ever. Need it really be so hard to end TBTF? As Nick Kiefer once wisely said (as the cognoscenti rolled their eyes), "If they're too big to fail, then break them up."

[For more, see my earlier financial regulation posts:  part 1, part 2 and part 3.]

The Black Swan Spectrum

Speaking of the newly-updated draft of Econometrics, now for some fun. Here's a question from the Chapter 6 EPC (exercises, problems and complements). Where does your reaction fall on the A-B spectrum below?

Nassim Taleb is a financial markets trader (and Wharton graduate) turned pop author. His book, The Black Swan, deals with many of the issues raised in this chapter. "Black swans"' are seemingly impossible or very low-probability events -- after all, swans are supposed to be white -- that occur with annoying regularity in reality. Read his book. Where does your reaction fall on the A-B spectrum below? 

A. Taleb offers crucial lessons for econometricians, heightening awareness in ways otherwise difficult to achieve. After reading Taleb, it's hard to stop worrying about non-normality, model misspecification, and so on.

B. Taleb belabors the obvious for hundreds of pages, arrogantly "informing"' us that non-normality is prevalent, that all models are misspecified, and so on. Moreover, it takes a model to beat a model, and Taleb offers nothing new.

The book is worth reading, regardless of where your reaction falls on the A-B spectrum.

A New Center to Watch for Predictive Macroeconomic and Financial Modeling

Check out USC's fine new Center for Applied Financial Economics, led by the indefatigable Hashem Pesaran. The first event is a fascinating conference, "Recent Developments on Forecasting Techniques for Macro and Finance."  Lots of information here, and program below.

PROGRAM

Wednesday, November 20th, 2013

8:00-8:45 a.m. Registration and Continental Breakfast

8:45-9:00 a.m. WELCOME
Hashem Pesaran, John E. Elliott Distinguished Chair of Economics and Director of the Centre for Applied Financial Economics (CAFE), USC Dornsife

9:00-9:50 a.m. SESSION I Chair: Robert Dekle
Speaker: Òscar Jordà
Title: Semiparametric Estimates of Monetary Policy
Effects: String Theory Revisited. With Joshua D. Angrist and Guido Kuersteiner.
Discussant: Eleonora Granziera

9:50-10:40 a.m. SESSION II Chair: Yu-Wei Hsieh
Speaker: Michael W. McCracken
Title: Evaluating Forecasts from Vector Autoregressions Conditional on Policy Paths. With Todd E. Clark.
Discussant: Andreas Pick

11:00-11:50 p.m. SESSION III Chair: Michael Magill
Speaker: Jose A. Lopez
Title: A Probability-Based Stress Test of Federal Reserve Assets and Income. With Jens H.E. Christensen and Glenn D. Rudebusch.
Discussant: Wayne Ferson

11:50-12:40 p.m. SESSION IV Chair: Yilmaz Kocer
Speaker: Tae-Hwy Lee
Title: Density and Risk Forecast of Financial Returns Using Decomposition and Maximum Entropy. With Zhou Xi and Ru Zhang.
Discussant: Hyungsik Roger Moon

2:00-2:50 p.m. SESSION V Chair: Juan D. Carrillo
Speaker: Allan Timmermann
Title: Equivalence Between Out-of-Sample Forecast
Comparisons and Wald Statistics. With Peter Reinhard Hansen.
Discussant: Hashem Pesaran

2:50-3:40 p.m. SESSION VI Chair: Jeffrey B. Nugent
Speaker: Gloria Gonzalez-Rivera
Title: In-Sample and Out-of-Sample Performance of
Autocontour-Testing in Unstable Environments. With
Yingying Sun.
Discussant: Cheng Hsiao

4:00-4:50 p.m. SESSION VII Chair: Giorgio Coricelli
. Speaker: Gareth M. James
Title: Functional Response Additive Model Estimation with
Online Virtual Stock Markets. With Yingying Fan, Natasha Foutz, and Wolfgang Jank.
Discussant: Dalia A. Ghanem

4:50-5:40 p.m. SESSION VIII Chair: Joel David
Speaker: Marcelle Chauvet
Title: Nowcasting of Nominal GDP. With William A. Barnett and Danilo Leiva-Leon.
Discussant: Michael Bauer

5:40 p.m. Concluding Remarks

Is Economics too Important for Economists?

Like piranha fish in a feeding frenzy, different research tribes fight furiously to stake claims in new areas like financial engineering and risk management. Fringe players, in particular, often strive to move toward the center, or to redefine the center in ways that feather their nests.

Such competition is desirable, but healthy competition is based on merit, not mudslinging. Hence my disappointment when watching the video preview for "Financial Engineering and Risk Management Part I," a massively open online course (MOOC) by Martin Haugh and Garud Iyengar (H&I) at Columbia, to be given soon on Coursera. H&I come from Industrial Engineering and Operations Research, and they conclude their sales pitch with the brazen proclamation, "... it's often said that economics is too important to be left to economists. Well, we feel the same way about finance and financial engineering. It's too important to be left to economists..."

Wow, strong words. So what's in their syllabus? Here it is:

- Introduction to forwards, futures and swaps
- Introduction to options and the 1-period binomial model
- The multi-period binomial model and risk-neutral pricing
- Term structure models and pricing fixed income derivative securities
- Introduction to credit derivatives
- Introduction to mortgage mathematics and mortgage-backed securities

Huh? What? Isn't that largely financial economics, pioneered and continuously refined by an ongoing parade of financial economists? Of course. Indeed what else could it be?

A quick glance at the web indicates that H&I's research is high-quality, and I hope that the same will be true for their course. (I have registered.) I'm also glad that H&I are contributing to the wonderful Coursera MOOC phenomenon, and I applaud their declared desire to increase lay financial literacy. But I suggest that they and their tribe give credit where credit is due -- is that not necessary for true literacy? -- and think twice before glibly biting the financial economics hand that feeds them.

Financial Regulation, Part Three: The Known, the Unknown and the Unknowable

Dick Herring, Neil Doherty and I recently worked on an eye-opening research project that resulted in our book, The Known, the Unknown and the Unknowable in Financial Risk Management. I always liked the cover art (thanks to Princeton University Press, which did its usual fine job throughout).  I feel bad for the poor guy in the necktie, in the maze. But that's life in financial markets.

Poor Little Guy

We abbreviate the known, the unknown and the unknowable by K, u and U, respectively. Roughly, K is risk (known outcomes, known probabilities), u is uncertainty (known outcomes, unknown probabilities), and U is ignorance (unknown outcomes, unknown probabilities).

The book blurb on my web page reads as follows:

"On the successes and failures of various parts of modern financial risk management, emphasizing the known (K), the unknown (u) and the unknowable (U). We illustrate a KuU-based perspective for conceptualizing financial risks and designing effective risk management strategies. Sometimes we focus on K, and sometimes on U, but most often our concerns blend aspects of K and u and U. Indeed K and U are extremes of a smooth spectrum, with many of the most interesting and relevant situations interior."

The blurb continues:

"Statistical issues emerge as central to risk measurement, and we push toward additional progress. But economic issues of incentives and strategic behavior emerge as central for risk management, as we illustrate in a variety of contexts."

The book's table of contents reveals the breadth of that insight, from risk management, asset allocation, and asset pricing, to insurance, crisis management, real estate, corporate governance, monetary policy, and private investing:

TABLE OF CONTENTS

Chapter 1: Introduction by Francis X. Diebold, Neil A. Doherty, and Richard J. Herring 1

Chapter 2: Risk: A Decision Maker's Perspective by Sir Clive W. J. Granger 31

Chapter 3: Mild vs. Wild Randomness: Focusing on Those Risks That Matter by Benoit B. Mandelbrot and Nassim Nicholas Taleb 47

Chapter 4: The Term Structure of Risk, the Role of Known and Unknown Risks, and Nonstationary Distributions by Riccardo Colacito and Robert F. Engle 59

Chapter 5: Crisis and Non-crisis Risk in Financial Markets: A Unified Approach to Risk Management by Robert H. Litzenberger and David M. Modest 74

Chapter 6: What We Know, Don't Know, and Can't Know about Bank Risk: A View from the Trenches by Andrew Kuritzkes and Til Schuermann 103

Chapter 7: Real Estate through the Ages: The Known, the Unknown, and the Unknowable by Ashok Bardhan and Robert H. Edelstein 145

Chapter 8: Reflections on Decision-making under Uncertainty by Paul R. Kleindorfer 164

Chapter 9: On the Role of Insurance Brokers in Resolving the Known, the Unknown, and the Unknowable by Neil A. Doherty and Alexander Muermann 194

Chapter 10: Insuring against Catastrophes by Howard Kunreuther and Mark V. Pauly 210

Chapter 11: Managing Increased Capital Markets Intensity: The Chief Financial Officer's Role in Navigating the Known, the Unknown, and the Unknowable by Charles N. Bralver and Daniel Borge 239

Chapter 12: The Role of Corporate Governance in Coping with Risk and Unknowns by Kenneth E. Scott 277

Chapter 13: Domestic Banking Problems by Charles A. E. Goodhart 286

Chapter 14: Crisis Management: The Known, The Unknown, and the Unknowable by Donald L. Kohn 296

Chapter 15: Investing in the Unknown and Unknowable by Richard J. Zeckhauser 304

I say that the project was "eye opening" (for me) because I went into it thinking about econometrics, but I came out of it thinking about economics.  Econometrics is invaluable for risk measurement, systemic aspects of which are embodied for example in the Diebold-Yilmaz network connectedness framework, or in parts of Rob Engle's V-Lab. But risk management, in particular risk prevention, is equally (or more) about creating incentives to guide strategic behavior, particularly in situations of u and U.

The real question, then, is how to write contracts (design organizations, design policies, design rules) that incent firms to "do the right thing," whatever that might mean, in myriad situations, many of which may be inconceivable at present -- that is, across the KuU spectrum.

How to do it?

To be continued...

Financial Regulation, Part Two: Rules

I concluded lmy last post with the question, "Do you really believe that ... this time we've fixed the too-big-to-fail (TBTF) incentive problem, that this time is different?" Presumably your answer depends on your feelings regarding the efficacy of Dodd-Frank's (DF's) increased capital requirements and intensified scrutiny of financial institutions.

Needless to say, I have my doubts.

Left to their own devices, lawyerly types (and politicians and regulators come disproportionately from that realm) tend to aspire to write exhaustive sets of rules that dictate what can and can't be done, when, and by whom. Economists call that a "complete contract." DF, at 2000+ pages, is an example of an attempt at such a complete contract, which financial institutions were forced to "sign."

One can entertain the idea of a complete contract in principle, but the idea of making rules to govern all possible contingencies is preposterous in practice. (Note that many important possible contingencies are surely not even remotely conceivable now -- more on that in the next post.) No one is so naive as to believe that DF is truly a complete contract, but the spirit of the attempt is one of complete contracting. Let's call it "rules-based" regulation.

Of course all legislation, regulatory or otherwise, must be rules-based. Rules, after all, are the essence of law. And rules, even massive sets of rules, can be very good things. There is little doubt, for example, that rules enforcing contractual and property rights can play a large role in generating economic prosperity. And there are some good entries in the DF rulebook.

But there are three key related problems with naive implementations of rules-based regulation, and it's important to be aware of them vis-à-vis DF. First, naive rules-based regulation is mostly backward-looking, effectively regulating earlier crises, with potentially very little relevance for future crises. It's terribly hard, as they say, to drive forward when looking only in the rear-view mirror. When the next major financial crisis hits, it will likely arrive via avenues that DF missed, and then DF will be augmented with another 2000+ pages of rules looking backward at that crisis, and so on, and on and on.

Second, naive rules-based regulation invites regulatory arbitrage. That is, as soon as rules are announced, firms start devising ways to skirt them. Indeed modern finance is in many respects an industry of very smart people whose job, for a given set of rules, is to work furiously to reverse-engineer those rules (they're doing it right now with respect to DF), devising clever ways to legally bear as much risk as possible while holding as little capital as possible, by finding and taking risks missed by the rules.

Third, naive rules-based regulation invites regulatory capture. That is, the regulated and the regulators get cozy, and the regulated eventually "capture" the regulator. First the regulators and regulated work side by side, implicitly or explicitly as in DF. Next the regulated are making "suggestions" for creative rule interpretation. Before long the regulated are helping to write the rules, crafting the very loopholes that they'll later exploit.

What to do? How to deal with the fundamental incompleteness of the regulatory contract? More specifically, given the long-term impotence of naive implementations of rules-based regulation, how really to thwart the adverse incentives of TBTF? If rules are unavoidable, and if naive rule implementations are problematic, are there better, sophisticated, implementations?

To be continued...

Financial Regulation, Part One: Too Big to Fail

I promised not to torture you with boring policy drivel ("Questions," May 17, 2013). What follows is about financial regulatory policy, but I insist that it's neither boring nor drivel. Rather it's about a key and deep issue.

Let's get right to it. The massive elephant in the room is, and always has been, the free insurance, or the free put option, or the bailout entitlement -- call it whatever you want -- associated with financial institutions with the coveted status of "too big to fail" (TBTF). Rest assured, there's no better way to incent financial institutions to take massively undesirable risks, with disastrous macroeconomic consequences, than to give them "get of of jail free" cards.

Effete cognoscenti will yawn and note that this is hardly a new observation. They're right. Countless observers have been shouting for decades about the adverse incentives generated by TBTF. Gary Stern, former President of the Federal Reserve Bank of Minneapolis, and David Skeel, Professor of Law at the University of Pennsylvania, are two good examples. Take a look at their books here (Stern) and here (Skeel).

The problem is that the shouts have fallen on largely deaf legislative ears. Indeed Dodd-Frank effectively institutionalizes TBTF through a nasty cocktail of government "partnership" with financial institutions, combined with discretionary government distress-resolution procedures. A nightmare scenario if ever there was.

The same cognoscenti will now snap to attention and assert that Dodd-Frank recognizes TBTF's adverse incentives and thwarts them by increasing capital requirements and intensifying regulatory scrutiny. TBTF is no longer relevant, the story goes, because now we're regulating the large institutions so effectively that they'll never again be in danger of failing.

Do you really believe that? That is, is there any real reason to believe that this time the rain dance will work, that this time we've fixed the TBTF incentive problem, that this time is different?

To be continued...

Structural Models, Reduced-Form Models, and Financial Econometrics

The scene at SoFiE 2013 leads me to reflect on structural vs. reduced-form modeling approaches in econometrics. Much financial-econometric work is reduced-form, whereas structural modeling has recently become fashionable in certain other areas of econometrics. The structure police, especially new recruits, are often fanatical.  But the reality is that reduced-form "statistical" models are every bit as scientific as structural models. Structural models are simply restricted reduced-form models, and it's a delicate and situation-specific matter as to whether imposing structural restrictions on reduced forms is necessary or desirable.

Many central activities in finance involve descriptive and predictive tasks, which are often most effectively executed in reduced-form mode. That is, we don't necessarily need deep structural understanding to succeed, for example, at prediction, which is wonderful, because we often don't have deep structural understanding. (Admit it.)

One key example, on my mind because it features prominently at SoFiE, is financial market volatility modeling.  GARCH, stochastic volatility, realized volatility, whatever -- all such approaches are reduced-form, essentially autoregressive. Yet financial econometric volatility modeling has been hugely successful in both academic and industrial finance. It is now used routinely and productively in risk management, portfolio management, spot and derivative asset pricing, and more.

And financial econometric volatility modeling is just one example. All told, for descriptive and predictive tasks in a variety of sub-areas of econometrics, reduced-form modeling often provides the best of all worlds, delivering major advances while avoiding structural modeling pitfalls.