Paul Cottrell's lecture on his dissertation research.
The financial crisis of 2008 was the worst financial crisis since the Great Depression of the 1930’s. Many books have been written on why the 2008 crisis happened. Irwin (2013) stated that central bankers knew that the housing bubble was a serious problem, but the central banks’ policy makers failed to have the imagination to understand the confluence of events that could magnify and bring down the whole global economy. In Geithner (2014), lack of firepower to bailout institutions in the banking sector was lacking in the early stages for the crisis. In terms of risk models, in Deman(2011), quant models are based on assumptions and those assumptions can become invalid over time—leading to inappropriate pricing of assets and risk assessments. Others have suggested that over indebtedness in the public and private sector was the cause of the decline of developed economies (Ferguson, 2013). The point of this book is not to comprehensively explain the crisis of 2008, but to suggest that economic systems evolve and emergent properties result, albeit some good and some bad properties. It is fair to say that the lack of imagination and the true risk of contagion are good starting points. We need better tools for assessing risk in the financial system, whereby we can evaluate endogenous and exogenous risk.
The financial crisis of 2008 resulted in the threat of total global financial collapse, whereby even the assumptions of free market capitalism and democracy were seriously questioned. This was a dangerous time and still is as of this writing. For example, European countries are still floundering economically, especially in the peripheral nations of the European Union. European countries tied to the Euro currency no longer have the ability to adjust their own monetary policy, and in that respect have lost economic sovereignty and self–determination. The problems are not just in Europe. There seems to be a global trend of a zero interest rate policy in developed nations, which is new to central bankers’ experiences. Is it possible that monetary policy that created the great moderation has led to new monetary dynamics that the central banks will have a hard time modulating. For example, when the United States of America starts to eliminate quantitative easing and the economy starts to wobble, will the Federal Reserve reverse their tightening policy? It is not at all clear that the central banks are in control of zero interest rate policy epochs. For example, in an inflationary situation the central banks can just raise rates, which they have a lot of room to do so—but might led to further erosion of aggregate demand. In extreme increases in aggregate demand the central banks can modulate the economy. This is not the case in a deflationary condition where the rates are already zero bound. What can the central banks do if the aggregate demand continues to erode with zero bound rates? The only thing that the central bank can do is severe asset purchases of all kinds—even ketchup.
The financial crisis also involved massive financial bailouts of banks, larger corporations, and citizens. For example—even though Lehman was not bailed out—General Motor, AIG, Chrysler Motors, and many banking firms were. Even with the bailouts, the money markets were not functions properly and a severe recession ensued. In the United States quantitative easing was initiated by purchasing many different asset backed securities but other countries were forced into austerity policies, especially in the European region—creating more financial harm than good. This austerity was promoted by Germany and the IMF when countries, such as Greece, were in trouble of defaulting on their sovereign bonds. At the time of this writing the ECB is starting to adopt a different stance and invoking a lender-of-last-resort policy regime and asset purchase buyer.
What can surfaces provide us? Surfaces provide an easier way to interpret data, especially big data. Surfaces can be used in conjunction with 2D visualizations for additional confirmation. For example, surfaces of the market can be used with normal 2D price curves to get a better understanding of the market’s dynamics. Therefore surfaces can fill in gaps of understanding that lower dimensions do not seem to reveal.
As for the theoretical framework, chaos theory is used because of the emerging properties that the market seems to exhibit. Complexity science—which is a branch from chaos theory—highly suggests emergent behavior of nonlinear systems, such as the financial markets. We can envision three different state–spaces composing of buyers and sellers. In first state–space the population is heterogeneous but equally distributed, whereby influence by neighboring traders are considered minimum. In second state–space the populations of buyers and sellers are still equal, but the traders of similar types are coalescing into segregated masses. Within this second state–space you can see that a phase transition is about to take place but it is not clear if buyers or the sellers will triumph. In technical trading this can be seen clearly in a Bollinger band pinch, whereby a big move either up or down can result. Lastly, in the third state–space we see that a certain population type has overcome the other population and exhibits a severe herd effect. It is important to mention that this herd effect is also present in second state–space example but only local to its community.
There are two main types of automata: simple and complex. Simple automata are a cybernetic system. This type of automata does not evolve with their environment. Communication is also of limited capacity, which is usually unidirectional. Simple automata are well defined and are considered a bounded system, i.e. a closed system. Since simple automata are a bounded system, emergent behavior does not exist within single automata, but emergence could be present within a cluster of cybernetic agents. This emergent behavior is quite limited in scope compared to complex automata.
Complex automata are evolving systems. Communication with internal and external environments is multi–directional for complex automata, due to their feedback loop properties. Complex automata have all the properties of complex adaptive systems: (a) simple rules for agents, ability to communicate with environment, to have stochastic functions within their decision making, and the ability to learn from doing. Complex automata are unbounded systems—actions are not defined completely to allow for emergent behavior. Since these automata are open systems, they are more adaptive to their environment and possess the ability to machine learn.
In terms of optimization problems, automata could roam around a state–space to determine possible solutions. Many of these financial applications are utilizing complex automata, e.g. ant algorithms, bee algorithms, and grasshopper algorithms. There are many variant automata algorithms but the main functions are similar—the automata is allowed to wander to find a solution that converges. These wandering complex automata are learning the solution landscape looking for a global optimum. Simple automata could be considered just calculators of certain tasks, but not actually learn from their environment.
We must start with what the theory of emergence means. Emergence spawned from cosmology, the study of the cosmos. Within cosmology the origins and evolution of the universe are explained. If we start at the big bang, this event led to further particle evolution and emergence of materials was set forth. For example, within unified string theory there are membranes behind the singularity of the big bang that intersected releasing all the energy in our present universe. As this released energy turned into plasma and cooled, other forces and particles emerged from the singularity. This emergence of subatomic particles led to hydrogen and star formation. Within these first stars further nucleosynthesis continued and produced heavier atoms. This process continues leading to further complex arrangements of life, e.g. social organization.
Economic or financial emergence is similar to cosmological emergence. Within economic emergence there are the following: (a) economic development, (b) systemic risk, and (c) contagion. Economic development can be easily explained in the stages of growth of a society. For example, a hunting and gathering society evolving into a domesticated farming community shows the emergence of higher social order.
In terms of systemic risk, financial systems are complex systems and these systems can behave in unexpected ways. Systemic risk usually evolves from a somewhat benign state to a much more malignant state, whereby it reaches escape velocity and threatens the financial fabric of a society (Cottrell, 2014). During the malignant state of systemic risk, contagion spreads. For example, during the subprime crisis in 2007 many bond holders were suffering losses. At a myopic perspective, subprime borrowers should not have affected the overall economy in theory, but it did—leading to the commercial paper markets freezing up. The key takeaway is that a complex system can evolve into unpredicted pathways; hence this follows the concept of non-deterministic chaos.
Complexity science is the study of complex systems. Within complex systems there are agents that are defined with basic rules of interaction. Within the state space that agents operate self–organizing behavior emerges over time. Within these self–organizing behaviors we have interactions that have magnifying effects.