The global financial crisis has revealed the need to rethink fundamentally how financial systems are regulated. It has also made clear a systemic failure of the economics profession. Over the past three decades, most economists have developed and come to rely on models that disregard key factors-?including the heterogeneity of decision rules, revisions of forecasting strategies, and changes in the social context-?that drive outcomes in asset and other markets. It is obvious, even to the casual observer, that these models fail to account for the actual evolution of the real-world economy.
Moreover, the current academic agenda has largely crowded out research on the inherent causes of financial crises. There has also been little exploration of early indicators of systemic crisis and potential ways to prevent this malady from developing. In fact, if one browses through the academic macroeconomics and finance literature, “systemic crisis” seems to be an otherworldly event, absent from economic models. Most models, by design, offer no Immediate handle on how to think about or deal with this recurring phenomenon. 1 In our hour of greatest need, societies around the world are left to grope in the dark thou a theory.
That, to us, is a systemic failure of the economics profession Economists’ Failure to Anticipate and understand the Crisis The Implicit view behind standard equilibrium models is that markets and economies are inherently stable and that they only temporarily get off track. The majority of economists thus failed to warn about the threatening systemic crisis and ignored the work of those who did. Ironically, as the crisis has unfolded, economists have had no choice but to abandon their standard models and to produce hand-waving common-sense remedies.
Common-sense advice, although useful, Is a poor substitute for an underlying model. It is not enough to put the existing model to one side, observing that one needs “exceptional measures for exceptional times. ” What we need are models capable of envisaging such “exceptional times. ” Downloaded by [Sheffield Hall University] at 09:18 07 November 2013 Colander teal. The Failure of Economics 251 The confinement of macroeconomics to models of stable states that are perturbed by limited external shocks, but that neglect the intrinsic recurrent boom-and-bust dynamics of our economic system, is remarkable.
After all, worldwide financial and economic crises are hardly new, and they have had a tremendous Impact beyond the Immediate economic consequences of mass unemployment and hyperinflation In legacy of earlier economists’ study of crises, which can be found in the work of Walter Baggage ( 1873 ), Hyman Minsk ( 1986 ), Charles Kindergärtner 1989 ), and Axel Lei]unfold ( 2000 ), to name a few prominent examples. This tradition, however, has been neglected and even suppressed.
Much of the motivation for economics as an academic discipline stems from the desire to explain phenomena like unemployment, boom-antitrust cycles, and financial crises, but nominate theoretical models exclude many of the aspects of the economy that lead to such phenomena. Confining theoretical models to “normal” times without consideration of these aspects might seem contradictory to the focus that the average taxpayer would expect of the scientists on his payroll.
The most recent literature provides us with examples of blindness against the approaching storm that seem odd in retrospect. For example, in their analysis of the risk management implications of Cods (collateralized debt obligations), Karaoke 2005 and Karaoke and Wiled 2006 mention the possibility of an increase of “systemic risk. But they conclude that such risk should not be the concern of the banks engaged in the COD market, because it is the governments’ responsibility to provide costless insurance against a system-wide crash.
On the more theoretical side, a recent and prominent strand of literature essentially argues that consumers and investors are too risk averse because of their memory of the (improbable) event of the Great Depression (e. G. , Cooley and Sergeant 2008 The failure of economists to anticipate and model the financial crisis has deep methodological roots. The often-heard definition of economics-? that it is concerned tit the “allocation of scarce resources”-?is short sighted and misleading. It reduces economics to the study of optimal decisions in well-specified choice problems.
Such research generally loses track of the complex dynamics of economic systems and the instability that accompanies it. Without an adequate understanding of these processes, one is likely to miss the major factors that influence the economic sphere disregard questions about the coordination Downloaded by [Sheffield Hall university] at 09:18 07 November 2013 252 Critical Review Volvo. 21 , No’s. 2-3 of actors and the possibility of coordination failures. Indeed, analysis of these issues would require a different type of mathematics than that which is generally used in most prominent economic models.
Economists’ Role in Fostering the Crisis Financial economists gave little warning to the public about the fragility of their models, 2 even as they saw individuals and businesses build a financial system based on their work. There are a number of possible explanations for this failure to warn the public. One is a “lack of understanding” explanation: The researchers did not know the models were fragile. We find this explanation highly unlikely; financial engineers are extremely right, and it is almost inconceivable that such bright individuals did not understand the limitations of their models.
A second, more likely explanation for this failure is that they did not consider it their Job to warn the public. We believe that this view involves a misunderstanding of the role of the economist-?and an ethical breakdown. Economists, as with all scientists, have an ethical responsibility to communicate the limitations of their models and the potential misuse of their research. Currently, there is no ethical code for professional economic scientists. There should be one. Economic textbook models, which focus the analysis on the optimal allocation of scarce resources, are predominantly Robinson Crusoe (representative-agent) models.
Financial-market models are obtained by letting Robinson manage his financial affairs as a sideline to his wildernesses utility minimization over his (finite or infinite) expected lifespan, taking into account with “correct” probabilities all potential future happenings. This approach is mingled with insights from Wallabies general- equilibrium theory, in particular the finding of the Arrow- Debug two-period model hat all uncertainty can be eliminated if only there are enough contingent claims (I. E. , appropriate derivative instruments).
This theoretical result (a theorem in an extremely stylized model) underlies the common belief that the introduction of new classes of derivatives can only be welfare enhancing. It is worth emphasizing that this view is not empirically grounded but is derived from a benchmark model that is much too abstract to be confronted with data. On the practical side, mathematical portfolio and risk-management models have been the academic backbone of the tremendous increase of Downloaded by Sheffield Hall University] at 09:18 07 November 2013 Colander 253 trading volume and diversification of instruments in financial markets.
Typically, new derivative products achieve market penetration only if a certain industry standard has been established for the pricing and risk management of these products. Mostly, pricing principles are derived from a set of assumptions about an “appropriate” process for valuing the underlying asset (I. E. , the primary assets on which options or forwards are written), together with an equilibrium criterion such as arbitrage-free prices. From these assumptions springs advice for hedging the inherent risk of a derivative position (for example, by balancing it with other assets that neutralize the risk exposure).
The most prominent example is the development of a theory of options pricing by Fischer Black and Myron Schools that eventually (in the 1980 s) was preprogrammed into pocket calculators. Simultaneously with Black-Schools options pricing, the same principles led to the widespread introduction of new strategies, under the headings of portfolio insurance and dynamic hedging, that tried to achieve a theoretically risked portfolio composed of both assets and options, and o keep it risk-free by frequent refinancing after changes in its input data (e. . , asset prices). With structured products for credit risk, however, the basic paradigm of derivative pricing-?perfect replication-?is not applicable, so that one has to rely on a kind of rough-and-ready evaluation of these contracts on the basis of historical data. Unfortunately, historical data were hardly available in most cases, which meant that one had to rely on simulations with relatively arbitrary assumptions about correlations between risks and default probabilities.
This made the theoretical inundations of these products highly questionable-?the equivalent to erecting a buildings foundation without knowing the materials of which the foundation was made. The dramatic recent rise of the markets for structured products (most prominently collateralized debt obligations and credit-default swaps) was made possible by the development of such simulation-based pricing tools and the adoption of an industry standard for these under the lead of the bond-rating agencies.
Barry Gingerers ( ) rightly points out that the “development of mathematical methods designed to quantify and hedge risk encouraged commercial banks, investment banks and hedge funds to use more leverage,” as if the managers of these institutions believed that the very use of the mathematical methods diminished the underlying risk. He also notes that the models were estimated on data from periods of low volatility and thus could not deal with the arrival of Downloaded by [Sheffield Hall University] at 09:18 07 November 2013 254 simply be ignored.
A somewhat different aspect is the danger of a control illusion : The mathematical rigor and numerical precision of risk-management and asset-pricing lolls has a tendency to conceal the weaknesses of models and assumptions to those who have not developed them (as Gingerers emphasizes). Naturally, models are, at best, only approximations to realtor dynamics and they are built in part on quite heroic assumptions (most notoriously, the normality of asset-price changes, which can be rejected at a confidence level of 99. 999 percent).
Of course, considerable progress has been made by moving to more refined models with, e. G. , “fat-tailed” Levy processes as their driving factors. However, while such models better capture the intrinsic volatility of markets, their improved performance, taken at face value, might again contribute to enhancing the control illusion of the naive user. The increased sophistication of extant models, moreover, does not overcome the robustness problem and should not absolve the authors of the models from explaining their limitations to the users in the financial industry.
As in nuclear physics, the tools provided by financial engineering can be put to very different uses, so that what is designed as an instrument to hedge risk can become a weapon of “financial mass destruction” (in the words of Warren Buffet) if used for increased average. This seems to have been the case with derivative positions that were built up to profit from high returns as long as the downside risk did not materialize.
Researchers who develop such models can claim they are merely neutral academics developing tools that people are free to use or reject. We do not find that view credible. Researchers have an ethical responsibility to point out to the public when the tools that they developed are misused. And it is the responsibility of the researcher to make clear from the outset the limitations and underlying assumptions f his models and to warn of the dangers of their mechanistic application.
Because researchers did not point out the difficulties with their models, the new derivatives markets were flawed in ways that contributed to the financial crisis. One of the most important problems was that while the possibility of systemic risk was not entirely ignored, it was defined as lying outside the responsibility of market participants. In this way, moral hazard concerning systemic risk was a built-in attribute of the system.
The neglect of systemic externalities by market participants ND policy makers is not only unethical; it is a prudential lapse as well: Market 255 participants’ use of these models undermines the stability of the system that the models imply is stable, meaning that participants should not the models if they want to avoid being the victims of the endogenous boom-and-bust fluctuations so typical of markets. Blame should not only fall on market participants and policymakers; it should also fall on economists, who insisted on constructing models that ignored the systemic risk factors.
In failing even to point out their weaknesses to the public, they were artisans in what might be called academic moral hazard. What follows from our diagnosis? Market participants and regulators have to become more sensitive towards the potential weaknesses of restatement models. Since we do not know the “true” model, robustness should be a key concern. The uncertainty of models should also be taken into account by applying more than a single model.
Ignoring Market Participants’ Own Models of Markets A related flaw of asset-pricing and risk-management tools is their individualistic perspective, which takes as given ( sisters Paramus ) the behavior of all other market participants. However, if popular asset-pricing and restatement models are used by a large number (or even the majority) f market participants, then the individualistic assumption is false and can be expected to produce misleading predictions.
By the same token, a market participant (e. G. , the notorious Long-Term Capital Management) might become so dominant in certain markets that the assumption becomes unrealistic. The simultaneous pursuit of identical micro strategies leads to synchronous behavior and built-in contagion. This simultaneous application might generate an unexpected macro outcome that Jeopardizes the success of the underlying micro strategies. A perfect illustration was the U. S. Stock market crash of October 1987
The model was self-reflexive; people’s collective use of the model changed the model, and brought bout a result not predicted by the original model. Similarly, many economic models are built upon the twin assumptions of “rational expectations” and a representative agent: That is, all market participants are homogeneity into a single agent with rational expectations, and these are defined to be fully consistent with the structure of the economist’s own model.
Since the economist’s model is, of course, treated as true (which is odd given that even economists are divided in their views about the correct model of the economy), the implication is that the representative individual, hence everyone in the economy, behaves as if he had a complete understanding of the economic mechanisms governing the world. Such models do not attempt to formalize individuals’ actual expectations: Specifications are not based on empirical observation of how people form expectations.
Thus, even when applied economics research or psychology provide insights about how individuals actually form expectations, they cannot be used within rational-expectations models. Leaving no place for real-world individuals’ imperfect knowledge and adaptive adjustments, rational-expectations models are happily found to have dynamics that are grossly inconsistent with economic data. Technically, rational-expectations models are often framed as solving dynamic- programming problems in macroeconomics.
But demographically models as models of the aggregate economy have serious limitations (Colander 2006; Colander et al. 2008). If they are to be analytically tractable, not more than one dynamically maximizing agent can be considered, and consistent expectations have to be imposed on this agent. Therefore, dynamic- programming models are hardly imaginable without the assumptions of a representative agent and rational expectations. This
