Risk Topography: Systemic Risk and Macro Modeling
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| This PDF is a selec on from a published volume from the Na onal Bureau of Economic Research Volume Title: Risk Topography: Systemic Risk and Macro Modeling Volume Author/Editor: Markus Brunnermeier and Arvind Krishnamurthy, editors Volume Publisher: University of Chicago Press Volume ISBN: 0‐226‐07773‐X (cloth); 978‐0‐226‐07773‐4 (cloth); 978‐0‐226‐09264‐5 (eISBN) Volume URL: h p://www.nber.org/books/brun11‐1 Conference Date: April 28, 2011 Publica on Date: August 2014 Chapter Title: Challenges in Iden fying and Measuring Systemic Risk
Chapter Author(s): Lars Peter Hansen Chapter URL: h p://www.nber.org/chapters/c12507 Chapter pages in book: (p. 15 ‐ 30)
15 1.1 Introduction Discussions of public oversight of financial markets often make reference to “systemic risk” as a rationale for prudent policy making. For example, mitigating systemic risk is a common defense underlying the need for mac- roprudential policy initiatives. The term has become a grab bag, and its lack of specificity could undermine the assessment of alternative policies. At the outset of this essay I ask, should systemic risk be an explicit target of mea- surement, or should it be relegated to being a buzz word, a slogan or a code word used to rationalize regulatory discretion? I remind readers of the dictum attributed to Sir William Thomson (Lord Kelvin): I often say that when you can measure something that you are speaking about, express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowl- edge is of the meagre and unsatisfactory kind: it may be the beginning of knowledge, but you have scarcely, in your thoughts advanced to the stage of science, whatever the matter might be. 1
Challenges in Identifying and Measuring Systemic Risk Lars Peter Hansen Lars Peter Hansen is the David Rockefeller Distinguished Service Professor in Economics, Statistics, and the College at the University of Chicago and a research associate of the National Bureau of Economic Research. In writing this chapter, I benefited from helpful suggestions by Amy Boonstra, Gary Becker, Mark Brickell, John Heaton, Jim Heckman, Arvind Krishnamurthy, Monika Piazzesi, Toni Shears, and Stephen Stigler, and especially by Markus Brunnermeier, Andy Lo, Tom Sar- gent, and Grace Tsiang. For acknowledgments, sources of research support, and disclosure of the author’s material financial relationships, if any, please see http:// www .nber .org/ chapters / c12507.ack. 1. From lecture to the Institution of Civil Engineers, London (3 May 1883), “Electrical Units of Measurement,” Popular Lectures and Addresses (1889), Vol. 1, 80– 81.
16 Lars Peter Hansen While Lord Kelvin’s scientific background was in mathematical physics, discussion of his dictum has pervaded the social sciences. An abbreviated version appears on the Social Science Research building at the University of Chicago and was the topic of a published piece of detective work by Merton, Sills, and Stigler (1984). I will revisit this topic at the end of this essay. Right now I use this quote as a launching pad for discussing systemic risk by ask- ing if we should use measurement or quantification as a barometer of our understanding of this concept. One possibility is simply to concede that systemic risk is not something that is amenable to quantification. Instead it is something that becomes self- evident under casual observation. This is quite diVerent from Kelvin’s assertion about the importance of measurement as a precursor to some form of scientific understanding and discourse. Kelvin’s view was that for measurement to have any meaning requires that (a) we formalize the concept that is to be measured, and (b) we acquire data to support the measurement. The need to implement new laws with expanded regulation and oversight puts pressure on public sector research groups to develop quick ways to provide useful measurements of systemic risk. This requires shortcuts, and it also can proliferate superficial answers. These short- term research responses will be revealing along some dimensions by providing useful summaries from new data sources or at least data sources that have been largely ignored in the past. Stopping with short- term or quick answers can lead to bad policy advice and should be avoided. It is important for researchers to take a broader and more ambitious attack on the problem of building quantita- tively meaningful models with macroeconomic linkages to financial markets. Appropriately constructed, these models could provide a framework for the quantification of systemic risk. In the short run, we may be limited in our ability to provide meaningful quantification. Perhaps we should defer and trust our governmental oYcials engaged in regulation and oversight to “know it when they see it.” I have two concerns about leaving things vague, however. First, it opens the door to a substantial amount of regulatory discretion. In extreme circumstances that are not well guided by prior experience or supported by economic models that we have confidence in, some form of discretion may be necessary for prudent policy making. However, discretion can also lead to bad government policy, including the temptation to respond to political pressures. Second, it makes criticism of measurement and policy all the more challenging. When formal models are well constructed, they facilitate discussion and criticism. Delineating assumptions required to justify conclusions disciplines the com- munication and commentary necessary to nurture improvements in models, methods, and measurements. This leads me to be sympathetic to a longer- term objective of exploring the policy- relevant notions of the quantification of systemic risk. To embark on this ambitious agenda, we should do so with Challenges in Identifying and Measuring Systemic Risk 17 open eyes and a realistic perspective on the measurement challenges. In what follows, I explore these challenges, in part, by drawing on the experience from other such research agendas within economics and elsewhere. In the remainder of this essay: (a) I explore some conceptual modeling and measurement challenges, and (b) I examine these challenges as they relate to existing approaches to measuring systemic risk.
Sparked in part by the ambition set out in the Dodd- Frank Act and similar measures in Europe, the Board of Governors of the Federal Reserve System and some of the constituent regional banks have assembled research groups charged with producing measurements of systemic risk. Such mea- surements are also part of the job of the newly created OYce of Financial Research housed in the Treasury Department. Similar research groups have been assembled in Europe. While the need for legislative responses puts pres- sure on research departments to produce quick “answers,” I believe it is also critical to take a longer- term perspective so that we can do more than just respond to the last crisis. By now, a multitude of proposed measures exist and many of these are summarized in Bisias et al. (2012), where thirty- one ways to measure systemic risk are identified. While the authors describe this catalog as an “embarrassment of riches,” I find this plethora to be a bit disconcerting. In describing why, in the next section, I will discuss briefly some of these measures without providing a full- blown critique. Moreover, I will not embark on a commentary of all thirty- one listed in their valuable and extensive summary. Prior to taking up that task, I consider some basic conceptual issues. I am reminded of Koopmans’s discussion of the Burns and Mitchell (1946) book on measuring business cycles. The Koopmans (1947) review has the famous title “Measurement without Theory.” It provides an extensive discussion and sums things up saying: The book is unbendingly empiricist in outlook. . . . But the decision not to use theories of man’s economic behavior, even hypothetically, limits the value to economic science and to the maker of policies, of the results obtained or obtainable by the methods developed. (172) The measurements by Burns and Mitchell generated a lot of attention and renewed interest in quantifying business cycles. They served to motivate development of both formal economic and statistical models. An unabash- edly empirical approach can most definitely be of considerable value, espe- cially in the initial stages of a research agenda. What is less clear is how to use such an approach as a direct input into policy making without an eco- nomic model to provide guidance as to how this should be done. An impor- 18 Lars Peter Hansen tant role for economic modeling is to provide an interpretable structure for using available data to explore the consequences of alternative policies in a meaningful way. In the remainder of this section, I feature two measurement challenges that should be central to any systemic research measurement agenda. How do we distinguish systemic from systematic risk? How do we conceptualize and quantify the uncertainty associated with systemic risk measurement? 1.2.1 Systematic or Systemic The terms systematic and systemic risk are sometimes confused, but their distinction is critical for both measurement and interpretation. In sharp contrast with the former concept, the latter one is well studied and sup- ported by extensive modeling and measurement. “Systematic risks” are macroeconomic or aggregate risks that cannot be avoided through diversi- fication. According to standard models of financial markets, investors who are exposed to these risks require compensation because there is no simple insurance scheme whereby exposure to these risks can be averaged out. 2 This compensation is typically expressed as a risk adjustment to expected returns. Empirical macroeconomics aims to identify aggregate “shocks” in time series data and to measure their consequences. Exposure to these shocks is the source of systematic risk priced in security markets. These may include shocks induced by macroeconomic policy, and some policy analyses explore how to reduce the impact of these shocks to the macroeconomy through changes in monetary or fiscal policy. Often, but not always, as a separate research enterprise, empirical finance explores econometric challenges associated with measuring both the exposure to the components of sys- tematic risk that require compensation and the associated compensations to investors. “Systemic risk” is meant to be a diVerent construct. It pertains to risks of breakdown or major dysfunction in financial markets. The potential for such risks provides a rationale for financial market monitoring, intervention, or regulation. The systemic risk research agenda aims to provide guidance about the consequences of alternative policies and to help anticipate pos- sible breakdowns in financial markets. The formal definition of systemic risk is much less clear than its counterpart systematic risk. Here are three possible notions of systemic risk that have been suggested. Some consider systemic risk to be a modern day counterpart to a bank run triggered by liquidity concerns. Measurement of that risk could be an essential input to the role of central banks as “lenders of last resort” to prevent failure of large financial institutions or groups of financial institu- 2. A more precise statement would be that these are the risks that could require compensa- tion. In equilibrium models there typically exist aggregate risks with exposures that do not require compensation. Diversification arguments narrow the pricing focus to the systematic or aggregate risks.
Challenges in Identifying and Measuring Systemic Risk 19 tions. Others use systemic risk to describe the vulnerability of a financial network in which adverse consequences of internal shocks can spread and even magnify within the network. Here the measurement challenge is to identify when a financial network is potentially vulnerable and the nature of the disruptions that can trigger a problem. Still others use the term to include the potential insolvency of a major player in or component of the financial system. Thus systemic risk is basically a grab bag of scenarios that are sup- posed to rationalize intervention in financial markets. These interventions come under the heading of “macroprudential policies.” Since the Great Recession was triggered by a financial crisis, it is not surprising that there were legislative calls for external monitoring, intervention, or regulation to reduce systemic risk. The outcome is legislation such as the rather cumber- some and still incomplete 2,319 page Dodd- Frank Wall Street Reform and Consumer Protection Act. The sets of constructs for measurement to sup- port prudent policy making remain a challenge for future research. Embracing Koopmans’s call for models is appealing as a longer- term research agenda. Important aspects of his critique are just as relevant as a commentary on current systemic risk measurement as they were for Burns and Mitchell’s business cycle measurement. 3 1.2.2 Systemic Risk or Uncertainty There are important conceptual challenges that go along with the use of explicit dynamic economic models in formal ways. Paramount among these is how we confront risk and uncertainty. Economic models with explicit sto- chastic structures imply formal probability statements for a variety of ques- tions related to implications and policy. In addition, uncertainty can come from limited data, unknown models, and misspecification of those models. Policy discussions too often have a bias toward ignoring the full impact of uncertainty quantification. But abstracting from uncertainty measurement can result in flawed policy advice and implementation. There are various approaches to uncertainty quantification. While there is well- known and extensive literature on using probability models to sup- port statistical measurement, I expect special challenges to emerge when we impose dynamic economic structure onto the measurement challenge. The discussion that follows is motivated by this latter challenge. It reflects my own perspective, not necessarily one that is widely embraced. My perspective is consonant, however, with some of the views expressed by Haldane (2011, 2012) in his discussions of policy simplicity and robustness when applied to regulating financial institutions. 3. One way in which the systemic risk measurement agenda is more advanced than that of Burns and Mitchell is that there is a statistical theory that can be applied to many of the suggested measurements of systemic risk. The ability to use “modern methods of statistical inference” was one of the reasons featured by Koopmans for why formal probability models are valuable, but another part of the challenge is the formal integration with economic analysis.
20 Lars Peter Hansen I find it useful to draw a distinction between risk and alternative concepts better designed to capture our struggles with constructing fully specified probability models. Motivated by the insights of Knight (1921), decision theorists use the terms uncertainty and ambiguity as distinguished from risk. See Gilboa and Schmeidler (1989) for an initial entrant to this literature and Gilboa, Postlewaite, and Schmeidler (2008) for a recent survey. Alternatively, we can think of statistical models as approximations and we use such models in sophisticated ways with conservative adjustments that reflect the poten- tial for misspecification. This latter ambition is sometimes formulated as a concern for robustness. For instance, Petersen, James, and Dupuis (2000) and Hansen and Sargent (2001) confront a decision problem with a family of possible probability specifications and seek conservative responses. To appreciate the consequences of Knight’s distinction, consider the fol- lowing. Suppose we happen to have full confidence in a model specifica- tion of the macroeconomy appropriately enriched with financial linkages needed to capture system- wide exposure to risk. Since the model specifies the underlying probabilities, we could use it both to quantify systemic risk and to compute so-called counterfactuals. While this would be an attractive situation, it seems not to fit many circumstances. As systemic risk remains a poorly understood concept, there is no “oV- the- shelf ” model that we can use to measure it. Any stab at building such models, at least in the near future, is likely to yield, at best, a coarse approximation. This leads directly to the question: how do we best express skepticism in our probabilistic mea- surement of systemic risk? Continuing with a rather idealized approach, we could formally articulate an array of models and weight these models using historical inputs and sub- jective priors. This articulation appears to be overly ambitious in practice, but it is certainly a good aim. Subjective inputs may not be commonly agreed upon and historical evidence distinguishing models may be weak. To make this approach operational leads naturally to a sensitivity analysis for priors including priors over parameters and alternative models. A model by its very nature is wrong because it simplifies and abstracts. Including a formal probabilistic structure enriches predictions from a model, but we should not expect such an addition to magically fix or repair the model. It is often useful to throw other models “into the mix,” so to speak. The same limitations are likely to carry over to each model we envision. Perhaps we could be lucky enough to delineate a big enough list of possible models to fill gaps left by any specific model. In practice, I suspect we can- not achieve complete success and certainly not in the short term. In some special circumstances, the gaps may be negligible. Probabilistic reasoning in conjunction with the use of models is a very valuable tool. But too often we suspect the remaining gaps are not trivial, and the challenge in using the models is capturing how to express the remaining skepticism. Simple models can contain powerful insights even if they are incomplete along
Challenges in Identifying and Measuring Systemic Risk 21 some dimensions. As statisticians with incomplete knowledge, how do we embrace such models or collections of them while acknowledging skepticism that should justifiably go along with them? This is an enduring problem in the use of dynamic stochastic equilibrium models and it seems unavoidable as we confront the important task of building models designed to measure systemic risk. Even as we add modeling clarity, in my view we need to aban- don the presumption that we can measure fully systemic risk and go after the conceptually more diYcult notion of quantifying systemic uncertainty. See Haldane (2012) for a further discussion of this point. What is at stake here is more than just a task for statisticians. Even though policy challenges may appear to be complicated, it does not follow that policy design should be complicated. Acknowledging or confronting gaps in modeling has long been conjectured to have important implications for economic policy. As an analogy, I recall Friedman’s (1960) argument for a simplified approach to the design of monetary policy. His policy prescrip- tion was premised on the notion of “long and variable lags” in a monetary transmission mechanism that was too poorly understood to exploit for- mally in the design of policy. His perspective was that the gaps in our knowl- edge of this mechanism were suYcient that premising activist monetary policy on incomplete models could be harmful. Relatedly, Cogley et al. (2008) show how alternative misspecification in modeling can be expressed in terms of the design of policy rules. Hansen and Sargent (2012) explore challenges for monetary policy based on alternative specifications of incom- plete knowledge on the part of a so-called “Ramsey planner.” The task of this planner is to design formal rules for implementation. It is evident from their analyses that the potential source of misspecification can matter in the design of a robust rule. These contributions do not explore the policy ramifi- cations for system- wide problems with the functioning of financial markets, but such challenges should be on the radar screen of financial regulation. In fact, implementation concerns and the need for simple rules underlie some of the arguments for imposing equity requirements on banks. See, for instance, Admati et al. (2010). Part of policy implementation requires attaching numerical values to parameters in such rules. Thus concerns about systemic uncertainty would still seem to be a potential contributor to the implementation of even seemingly simple rules for financial regulation. Even after we acknowledge that policymakers face challenges in forming systemic risk measures that could be direct and explicit tools for policy, there is another layer of uncertainty. Sophisticated decision makers inside the models we build may face similar struggles with how to view their economic environments. Why might this be important? Let me draw on contributions from two distinct strands of literature to speculate about this. Caballero and Simsek (2010) consider models of financial networks. In such models financial institutions care not only about the people that they interact with, say, their neighbors, but also the neighbors of neighbors, and
22 Lars Peter Hansen so forth. One possibility is that financial entities know well what is going on at all nodes in the financial network. Another is that while making probabi- listic assessments about nearby neighbors in a network is straightforward, this task becomes considerably more diYcult as we consider more indirect linkages, say, neighbors of neighbors of neighbors and so forth. This view is made operational in the model of financial networks of Caballero and Simsek (2010). In a rather diVerent application Hansen (2007) and Hansen and Sargent (2010) consider models in which investors struggle with alternative models of long- term economic growth. While investors treat each of the models as misspecified, they presume that the models serve as useful benchmarks in much the same way as in stochastic specifications of robust control theory. Historical evidence is informative, but finite data histories do not accurately reveal the best model. Important diVerences in models may entail subtle components of economic growth that can have long- term macroeconomic consequences. Concerns about model misspecification become expressed more strongly in financial markets in some time periods than others. This has consequences for the valuation of capital in an uncertain environment and on the market trade- oVs confronted by investors who participate in financial markets. In the example economies considered by Hansen (2007) and Hansen and Sargent (2010), what they call uncertainty premia become larger after the occurrence of a sequence of bad macroeconomic outcomes. In summary, the implications of systemic uncertainty whether in contrast or in conjunction with systemic risk are both important for providing policy advice and understanding market outcomes. External analysts, say, statisti- cians, econometricians, and policy advisors, confront specification uncer- tainty when they build dynamic stochastic models with explicit linkages to the financial markets. Within dynamic models with micro foundations are decision makers or agents that also confront uncertainty. Their resulting actions can have a big impact on the system- wide outcomes. Assessing both the analysts’ and agents’ uncertainties are critical components to a produc- tive research agenda.
Let me turn now to some of the recent research related to systemic risk. Just the wide scope of the Bisias et al. (2012) survey reminds us that there is not yet an agreed upon approach to this measurement. To me, it suggests that identifying what measurements will be the most fruitful to support our understanding of linkages between financial markets and the macroecon- omy is an open issue. In a superficial way, the sheer number of approaches would seem to address the Kelvin dictum. The problem is complex and it has many dimensions to it and thus requires multiple measurements. But I am doubtful that this is a correct assessment of the situation. Alternative Challenges in Identifying and Measuring Systemic Risk 23 measures are supported implicitly by alternative modeling assumptions and it is hard to see how the full array of measurements provides a coherent set of tools for policy makers. Many of the measurements to date seem closer in spirit to the Burns and Mitchell approach and fall way short of the Koop- mans standard. From a policy perspective, I fear that we remain too close to the Potter- Stewart “we know it when we see it” view of systemic risk. What follows is a discussion of a few specific approaches for assessing systemic risk along with some modeling and data challenges going forward. 1.3.1 Tail Measures One approach measures codependence in the tails of equity returns to financial institutions. Some form of codependence is needed to distin- guish the impact of the disturbances to the entire financial sector from firm- specific disturbances. Prominent examples of this include the work of Adrian and Brunnermeier (2008) and Brownlees and Engle (2011). Measur- ing tail dependence is particularly challenging because of limited historical data. To obtain estimates requires implicit extrapolations from the historical time series of returns because of the very limited number of extreme values of the magnitude of a financial crisis. While codependence helps to identify large aggregate shocks, all such shocks are in eVect treated as a conglomerate when extracting information from historical evidence. The resulting mea- surements are interesting, but they put aside some critical questions that are needed to understand better policy advice. For example, while equity returns are used to identify an amalgam of aggregate shocks that could induce cri- ses, how does the mechanism by which the disturbance is transmitted to the macroeconomy diVer depending on the source of the disturbance? Not all financial market crises are macroeconomic crises. The big drops in equity markets on October 19, 1987, and April 14, 2000, did not trigger major macroeconomic declines. Was this because of the source of the shock or because of the macroeconomic policy responses? Understanding both the source and the mechanism of the disturbance would seem to be critical to the analysis of policy implications. Further empirical investigation of financial linkages with macroeconomic repercussions should be an important next step in this line of research. It is wrong to say that this tail- based research is devoid of theory, and in fact Acharya et al. (2010) suggest how to use tail- risk measures as inputs into calculations about the solvency of the financial system. Their paper includes an explicit welfare calculation, and their use of measurements of tail dependence is driven in part by a particular policy perspective. Their theoretical supporting analysis is essentially static in nature, however. The macroeconomic consequences of crises events and how they unfold over time is largely put to the side. Instead, the focus is on providing a mea- sure of the public cost of providing capital in order to exceed a specific threshold. This research does result in model- based measurements of what 24 Lars Peter Hansen is called marginal expected shortfall and systemic risk. These measurements are updated regularly on the V-Lab web page at New York University. The use by Acharya et al. (2010) is an interesting illustration of how to model systemic risk and may well serve as a valuable platform for a more ambi- tious approach. The focus on equity calculations limits the financial institutions that can be analyzed. The so-called shadow banking sector contains potentially important sectors or groups of firms that are not publicly traded. One could argue that if the monitoring targets are only SIFI’s (so- called systemically important financial institutions), then the focus on publicly traded financial firms is appropriate. But system- wide policy concerns might be directed at the potential failure of collections of nonbank financial institutions includ- ing ones that are not publicly traded and hence omitted by calculations that rely on equity valuation measures. 1.3.2 Contingent Claims Analysis In related research, Gray and Jobst (2011) apply what is known as contin- gent claims analysis. This approach features risk adjustments to sectoral bal- ance sheets while featuring the distinct roles of debt and equity. It builds on the use of option pricing theory for firm financing where there is an under- lying stochastic process for the value of the firm assets. Equity is a call option on these assets and debt is the corresponding put option. Gray and Jobst (2011) discuss examples of this approach extended to sectors of the econ- omy including the government. In their applications, they measure sectoral balance sheets with a particular interest in financial crises. This approach neatly sidesteps statistical challenges by using “market expectations” and risk- adjusted probabilities in conjunction with equity- based measures of uncertainty and simplified models of debt obligations. Extending contingent claims analysis from the valuation of firms to systems of firms and govern- ments is fruitful. Note, however, if our aim is to make welfare assessments and direct linkages to the macroeconomy, then the statistical modeling and measurement challenges that are skirted will quickly resurface. Market expectations and risk- neutral probability assessments oVer the advantage of not needing to distinguish actual probabilities from the marginal utili- ties of investors in financial markets, but this advantage can only be pushed so far. A more fundamental understanding of the market- based “appetite for risk” and a characterization of the macroeconomic implications of the shocks that command large risk prices require further modeling and a more prominent examination of historical evidence. Such an understanding is central when our ambition is to engage in the analysis of counterfactuals and hypothetical changes in policies. 4 4. The potential omission of firms not publicly traded limits this approach for the reasons described previously. Challenges in Identifying and Measuring Systemic Risk 25 1.3.3 Network Models Network models of the financial system oVer intriguing ways to sum- marize data because of its focus on interconnectedness. These models open the door to some potentially important policy questions, but there are some critical shortcomings in making these models fully useful for policy. A finan- cial firm in a network may be highly connected, interacting with many firms. Perhaps these links are such that the firm is “too interconnected to fail.” A critical input into a policy response is how quickly the networks structure will evolve when such a firm fails. As is well recognized, in a dynamic setting these communications links will be endogenous, but this endogeneity makes modeling in a tractable way much more diYcult and refocuses some of the measurements needed to address policy concerns. 1.3.4 Dynamic, Stochastic Macroeconomic Models Linking financial market disruption to the macroeconomy requires more than just using “oV- the- shelf ” dynamic stochastic equilibrium models, say, of the type suggested by Christiano, Eichenbaum, and Evans (2005) and Smets and Wouters (2007). By design, models of this type are well suited for econometric estimation and they measure the consequences of multiple shocks and model explicitly the transition mechanisms for those shocks. Identification in these multishock models is tenuous. More importantly they are “small- shock” models. In order to handle a substantial number of state variables, they appeal to small noise approximations for analyti- cal tractability. Since the financial crisis, there has been a rush to integrate financial market restrictions into these models. Crises are modeled as times when ad hoc financial constraints bind. 5 To use local methods of analysis, separate approximations are made around crisis periods. See Gertler and Kiyotaki (2010) for a recent development and discussion of this literature. There is some promising recent research developing and applying compu- tational methods that allow for a more global approach to analyzing non- linear dynamic economic models. More application and experience with such methods should open the door to a better understanding of stochastic models with linkages between financial markets and the macroeconomy. Enriching dynamic stochastic equilibrium is a promising research agenda, but this literature has only scratched the surface on how to extend these models to improve our understanding of the macroeconomic consequence to upheaval in financial markets. It remains an open research question as to how best (a) to model financial constraints, both in terms of theoretical grounding and empirical importance; (b) to characterize the macroeconomic 5. I use the term ad hoc in a less derogatory manner than many other economists. I re- mind readers of a dictionary definition: concerned or dealing with a specific subject, purpose, or end.
26 Lars Peter Hansen consequences of crisis level shocks that are very large but infrequent; and (c) to model the origins of these shocks. 6 1.3.5 Pitfalls in Data Dissemination and Collection Measurement requires data. Going forward, there is great opportunity for the OYce of Financial Research in the United States and its counter- parts elsewhere to provide new data for researchers. Some of the data in its most primitive form will be confidential. Concern for confidentiality will create challenges for sharing this information with external researchers. One approach is to restrict the use of such data to be “in house.” This will limit the value of the data collection. If the objective is to ensure the high quality of research within government agencies, it is valuable to make important com- ponents of the data available to external researchers. This external access permits replication of results, and nurtures innovative modeling and mea- surement. 7 Moreover, external analysis can provide a check against research with preordained conclusions or inadvertent support for policies such as “too big (or too something) to fail.” While providing external access requires that data be distributed in manners that respect individual confidentiality, the possibility of making such data available is a reality. The Census Bureau has already confronted such challenges successfully. There are additional data issues that require scrutiny. Distortions in the collection of publicly available data can hinder the measurement of aggre- gate risk exposures because of the temptation to disguise the problematic nature of policies in place. Moreover, even when intentions are good, pre- existing policies can make the assessment of risk using historical data more challenging by partially mitigating risks in ways that are not sustainable in the future. Brickell (2011) identifies this latter challenge and argues that it may have contributed to errors in assessing housing market risk in the years before the Great Recession. These types of concerns place an extra burden on empirical researchers to model the biases in data collection induced by both public and private incentives for distortion. Given this state of econometric modeling and measurement, I see a big gap to fill between statistical analyses measuring comovements in the tails of financial market equity returns and empirical analyses measuring the impact of shocks to the macroeconomy. This gap limits, at least temporar- ily, the scope of the analysis of systemic risk. Closing this gap provides an important opportunity for the future. The compendium of systemic risk measures identified in Bisias et al. (2012) should be viewed merely as an inter- 6. For instance, the Macroeconomic Financial Modeling group funded by the Alfred P. Sloan Foundation explores the challenges to building quantitatively ambitious models that address these and other related challenges. 7. Andy Lo has made the related point that potentially relevant sectors, such as the insurance sector, are not under the formal scrutiny of the federal government and hence there may be an important shortfall in the data available to the OYce of Financial Research.
Challenges in Identifying and Measuring Systemic Risk 27 esting start. We should not lose sight of the longer- term challenge to provide systemic risk quantification grounded in economic analysis and supported by evidence. The need for sound theoretical underpinnings for producing policy- relevant research identified by Koopmans many decades ago still applies to the quantification of systemic risk. Policy analysis stemming from econometric models aims to push beyond the realm of historical evidence through the use of well- grounded economic models. It is meant to provide a framework for the conduct of hypothetical policies that did not occur during the historical observation period. To engage in this activity with the ambi- tion to understand better how to monitor or regulate the financial sector to prevent major upheaval in the macroeconomy requires creative adjustments in both our modeling and our measurement.
We should not underestimate the diYculty of measuring systemic risk in a meaningful way. But success oVers the potential for valuable inputs into policy making. Wearing my econometrician’s hat has led me to emphasize measurement challenges and the associated uncertainty caused by limited data or unknown statistical models used to generate the data. Of course clever econometricians can always invent challenges, and in many respects part of the econometrician’s job is to provide credible ways to quantify measurement uncertainties. After all, quantitative research in economics grounded by empirical evidence should be more than just reporting a single number but instead ranges or distributions that include sensitivity to model specification. Good econometrics is supported simultaneously by good eco- nomics and good statistics. Exploring the consequences of potential model misspecification necessarily requires inputs from both economics and statis- tics. Economic models help us understand what statistical inputs are most consequential to economic outcomes and good statistics reveal where the measurements are least reliable. Moreover, such econometric explorations will benefit discussions of policy by providing repeated reminders of why gaps in our knowledge can have important implications. Allow me to close by returning to the Kelvin dictum and drawing on some intellectual history of it as it relates to social science research. The decision to place this dictum on the Social Science Research building at the Univer- sity of Chicago caught the attention of some distinguished scholars. This building housed the economics department for many years and the Cowles Commission for Research in Economics during the years 1939 to 1955 when many young scholars came there to explore linkages between economics, mathematics, and statistics. 8 Two of the original pillars of the “Chicago 8. After moving to Yale in 1955, the Cowles Commission was renamed the Cowles Foun- dation.
28 Lars Peter Hansen school,” Knight and Viner, had notable reactions to the use of the Kelvin quote and proposed amendments: 9 Knight: If you cannot measure a thing, go ahead and measure it anyway. Viner: and even when we can measure a thing, our knowledge will be meager and unsatisfactory. Perhaps just as intriguing as Knight’s and Viner’s scepticism are the major challenges that were levied to Lord Kelvin’s own calculations about the age of the sun. These challenges provide an object lesson in support of model uncertainty. Kelvin argued that the upper bound of the sun’s age was twenty to forty million years, although his earlier estimates included the possibil- ity of a much larger number, up to 100 million years. Kelvin’s evidence and that provided by others were used to question the plausibility of the Darwinian theory of evolution. Darwin’s own calculations suggested that much more time was needed to justify the evolutionary processes. In hind- sight, Lord Kelvin’s estimates were over one hundred times lower than the current estimate of 4.5 billion years. Kelvin’s understatements were revised upward by substantive advances in our understanding of radioactivity as an energy source. This historical episode illustrates rather dramatically an impact of model uncertainty on the quality of measurement. While Knight’s and Viner’s words of caution were motivated by their perception of social science research several decades ago, their concerns extend to other research settings as well. It is diYcult to fault Lord Kelvin for not anticipating the discovery of a new energy source. Nevertheless, I do not wish to conclude that the potential for model misspecification should induce us to abandon earnest attempts at quantification. Instead quantification should be a val- ued exercise, and part of this exercise should include a characterization of sensitivity to alternative model specifications. Unfortunately, there are no guarantees that we have captured the actual form of the misspecification among the possibilities that we consider, but at least we can avoid some of the pitfalls of using models in naive ways. Quantitative ambitions have the virtue of providing clarity for what is to be measured. Models provide measurement frameworks and facilitate communication and criticism. While simple quantifications of systemic risk may be a naive hope, producing better models to support policy discussion and analysis is a worthy ambition. Building a single consensus model is unrealistic in the near term, but even exploring formally the consequences of alternative models adds discipline to policy advice. Without such model- ing pursuits, we are left with a heavy reliance on discretion in governmental course of action. Perhaps discretion is the best we can do in some extreme circumstances, but formal analysis should provide coherency and transpar- ency to economic policy. 9. See Merton, Stills, and Stigler (1984).
Challenges in Identifying and Measuring Systemic Risk 29 While systemic- risk modeling and measurement is a promising research agenda, caution should prevail about the impact of model misspecification on the measurements and the consequences of those measurements. A criti- cal component to this venture should be to assess and guard against adverse impacts of the use of measurements from necessarily stylized models. Com- plete success along this dimension is asking too much, otherwise we would just “fix” our models. Nevertheless, confronting the various components of uncertainty with some formality will help us to use models in sensible and meaningful ways. As our knowledge and understanding advance over time, so will our comprehension and characterization of uncertainty in our model- based, quantitative assessments. References Acharya, Viral V., Christian Brownlees, Robert Engle, Farhang Farazmand, and Matthew Richardson. 2010. “Measuring Systemic Risk.” In Regulating Wall
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Smets, Frank, and Rafael Wouters. 2007. “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review 97 (3): 586– 606. Yüklə 262,65 Kb. Dostları ilə paylaş:
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