The Beauty of Imperfection within Quantitative Economic Modeling

Saskia Troy, global education magazine

 Saskia Troy (MSc.) is a business economist specialized in Global Business and Stakeholder Management. In the past Saskia Troy has been Global Chapter Coordinator, Coordinator Europe and Chapter Leader Netherlands of Children of the Earth which is an NGO of the United Nations, and she is a member of the Working Group Sustainable Finance (Changing Finance, Financing Change) of the UNEP/World Resources Institute in Washington. 

 Email: saskia.troy@gmail.com

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Abstract: New forms of risk illustrate the interconnected nature of economic crisis. In the past scholars of economics have studied patterns that cause a behavioral equilibrium that induces no further reaction within the system. Hereby identical agents possess perfect rationality and arrive at shared logical conclusions or expectations about the situation they are in at the particular moment. When these expectations provoke actions that aggregative create a world that values them as predictions, they are in equilibrium. However, there’s now the need to turn to the question of how the actions of individual agents, their strategies and expectations might endogenously change while they adapt to the aggregate patterns they have been creating.

Keywords: Risk management, measuring uncertainty, failure of models that predict failure, complexity theory, standard out of equilibrium level, irrational expectations on economic rational actor model.

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‘’Financial markets can be wilder than the wildest river ever seen in nature. They are hard to measure while they are prone to uncertainty, randomness, luckiness, coincidence, errors and mistakes’’ (Economist, 2010; Taleb 2007; Taleb 2005).

At the moment the subprime crisis struck in the United Sates and particularly when it spread to other sophisticated economies and has been causing a recession for the global economy, the  effective policy response to the crisis became the main objective for international leaders of business and government. The final ambition of widespread central bank and government interventions was to address the fragility of the banking systems and restore confidence in the financial markets. For that reason the sources of stress and the availability of suitable remedies against heightened uncertainty about financial and economical developments have been analyzed (Swagel, 2009).

Nevertheless, as the crisis is a complex phenomenon, no single theory is likely to fully explain what occurred or completely rule out other explanations. A relatively new observable fact in the economic crises is that of leverage which is the usage of borrowed money to increase the expected interested on financial capital. The usage of leverage has been steadily increased within the financial sector within the years before the global financial economic crisis and has created extreme vulnerabilities and dependencies that have increased the severity of the crisis. The credit crisis has disclosed the limitations in regular ways of restraining leverage while the reductionist, narrow view of economics is no longer equipped to suit the real world complexity that is highly dependent on risk models for determining capital needs.

For example, it might have been the case that consequent uncontrolled deleveraging by financial institutions has compounded the crisis. The attempts of financial institutions to deleverage by selling financial assets could cause prices to spiral downward during times of market stress and in this way cause a counterproductive effect by exacerbating a financial crisis. Also deleveraging by restricting lending can cause economic growth to slow down steadily (GAO, 2010).

Measuring Uncertainty within Financial Markets

Trying even harder to capture risk in mathematical formula can be counterproductive if such an extent of accurateness is intrinsically unachievable. Nonetheless, one of the irrational exceptions on rational economic theory which is observable and systematic is that human beings are inherently overconfident about their capability, knowledge and future predictions (Economist, 2010).

The usual approach within  economics consists of the choice of the formulation of a certain assumption, where after a case has been found where one would intuitively think that X causes Y even though the theory yields a negative answer (or vice versa). For example, the case in which Bill and Suzy throw rocks at a bottle. Bill’s rock hits the bottle a split second before Suzy does and as a result the bottle shatters. The first thought would be that Bill’s throwing of the rock has caused the bottle that has been hit to shatter into pieces. But as a matter of fact had he not thrown the bottle the bottle would have shattered anyway. Thus Bill’s throwing does not come out as the cause of the shattering. And yet, we would say it is the case (Reiss, 2008).

Nevertheless, even though in financial markets in the real world past the outcomes of these financial, mathematical models searching for causal relationships within a complex world have proven to be very uncertain, unreliable and inaccurate human beings still have the tendency to develop forecasting models while using the instrument of defining causal relationships without testing them with counterfactuals to answer more fundamental associational questions in order to predict future developments within the world economy. The problem is that when these models fail to answer causal questions they do not answer anything at all and provide a false sense of reality of nowadays problems in financial markets.

According to Reiss (2008) within his book on economics and philosophy “The Error of Economics, The Methodology of Evidence-Based Economics  “No matter whether there’s an economic growth or a decline, investors would always be interested in the following question: “What would have happened to “Y” had “X” been “x”, whereas in this example the variable “Y” would be the target variable, “X” the variable that causes the impact on “Y” and respectively the variable “x” the control variable” These questions are called authentic “What if” questions. Nevertheless, instead of trying to answer “what if questions” with counterfactuals we should pose more fundamental associational questions, so argues Reiss.

Taleb (2005, 2007, 2012) within his books on financial markets and system dynamics Fooled by Randomness, Black Swan and Antifragile argues that our incapability to forecast in environments subjected to extreme events including a lack of the awareness of this state of affairs means that certain experts are claiming to tell the truth while in fact they are not. More important, according to Taleb, who himself is a mathematician but also has been a trader in financial markets, they are better in smoking you with complicated mathematical models while creating a sense of the truth. There recognition as being experts is because of their skills in narrating not in providing a realistic description of the economical truts.

According to Taleb (2007) people are permitted to be fortunate thanks to “aggressive trial and error” and not by giving rewards for “mathematical skills”. Due to the change in the nature of risk over the past ten years and the trans-disciplinary nature of problems many experts now find themselves lacking the skills to cope with dynamic nature of these problems. Taleb (2007) argues that in measuring uncertainty one simple observation that can be characterized as an extreme event with low-probability and high impact can invalidate a statement derived from a million of observations. Such events could explain almost everything in the world, from the rise and fall of empires, the upcoming of religions but also elements of our personal career, family- and romantic life’s.

The Failure of Models that predict Failure

New forms of risk illustrate the interconnected nature of economic crisis and the way in which distinct events can cause other problems within a nested system. For example, think about the phenomenon of leveraging within financial markets. In the past scholars of economics have studies steady models of market behavior which are patterns that cause a behavioral equilibrium that induces no further reaction within the system. However, these models which are known as steady, robust might not be as reliable as they seem.

In the past in economic analysis it was presumed that identical agents possess perfect rationality and arrive at shared logical conclusions or expectations about the situation they are in at the particular moment. When these accumulated expectations aggregative create a world that values them as predictions, they are according to micro- and macro-economic theory in a so-called perfect market equilibrium.

However, scholars are now elaborating on the former equilibrium approach by turning to the question of how the actions of individual agents, their strategies or expectations might endogenously change while adapting to the aggregate patterns these create. The moment we emphasize the formation of structures rather than their given existence, the problems that occur when trying to forecast the developments within the economy are different by nature (Arthur, 1999).

The complexity theory that holds this perspective on analyzing the economy portrays it not as deterministic, predictable and mechanistic but as process dependent, organic and always evolving. It is not an adaption to the conventional economic equilibrium theory but a theory at a standard out-of-

equilibrium level whereby reaching a perfect equilibrium is moreover the exception than conventional behavior (Arthur, 1999).

This way of thinking might be much more appropriate when it comes to analyzing current economic problems like the highly complex phenomenon of the financial crisis of 2008 than the former neoclassical models, because the latter seem in complete lack of practical applicability. Next to that they disregard the significance of the behavioral and humanizing aspect of modern economics.

The current models do not emphasize the more philosophical, associational insight but instead imply a false sense of precision with mathematical numbers. This is also the case with the models that have been used for the packaged mortgage securities and which failed in their predictions. One needs to be more honest about the limitations of these models in order to prevent future disaster in financial markets and the fallibility of the mathematics that has been used in order to make future forecasts (Taleb, 2007).

For example, a model using data on securitized subprime mortgage issues in 2006 demonstrates that as the degree of securization increases, interest rates and new loans rely increasingly on hard information about borrowers. As a result the model fitted in a low securization period breaks down in the high securization period while it underpredicts defaults among borrowers. In July 2006 the ABX index that tracks credit default swaps based on AAA subprime tranches fell by about 45% over the course of eight months. Behind the valuations was a statistical model that estimates defaults on the underlying collateral. Bankers and investors were left completely surprised with this new information (Rajan et all, 2009).

However, Rajan (2009) debates this breaking down of the financial model while underestimating the political, social and economic risks should not have been very much of a surprise while the models relied entirely on hard information and ignored soft control variables such as the incentives of lenders to collect information about borrowers, which was one of the fundamental causes for their failure (Rajan et all., 2009).The moment we are trying to quantify the behavior of human beings, while making use of mathematical models, it is as if we “force the ugly stepsister’s foot into Cinderella’s pretty glass slipper” (Economist, 2010).

Bibliography

Arthur, W.B., Increasing Returns and Path Dependency in the Economy, University of Michigan Press, Michigan, 1995

Rajan, U., Sen, A., Vig, V., The Failure of Models that predict Failure: Distance, Incentives and Defaults, January 2009

Reiss, J., Error in Economics, The Methodology of Evidence-Based Economics, London, Routledge, 2008

 Swagel, P., The Financial Crisis; An Inside View, Brooking Papers on Economic Activity, March 30, 2009

Taleb, N., N., Antifragile, Things that Gain from Disorder,  Random House Publishers,  New York, November 27th 2012

Taleb, N., N., Fooled by Randomness, The  Hidden Role of Chances in Life and in the Markets, 2005

Taleb, N.; N.; The Black Swan, The Impact of the Highly Improbable, Allen Lane, Great Britain, 2007

Valencia, M., The Economist, Special Report: Risk, The Gods strike Back, Februari 13th-19th, 2010

Financial Markets Regulation; Financial Crisis Highlights need to Improve Oversight of Leverage at Financial Institutions and across System, Testimony before the Subcommittee on Oversight and Investigations on Financial Services, House of Representatives, General Accounting Office (GAO), United States, Washington, 2010

This article was published on August 12, 2015, for the International Youth Day, in Global Education Magazine.

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