The report by Stefan Thurner - Systemic Financial Risk - is part of a series of five case studies published within the framework of the OECD Project on Future Global Shocks (2009-2011). These papers were commissioned in the aftermath of the "subprime crisis" that started in 2007-2008.
This report analyses the results of simulations using an agent based model of financial markets to show how excessive levels of leverage in financial markets can lead to a systemic crash. In this scenario, plummeting asset prices render banks unable or unwilling to provide credit as they fear they might be unable to cover their own liabilities due to potential loan defaults. Whether an overleveraged borrower is a sovereign nation or major financial institution, recent history illustrates how defaults carry the risk of contagion in a globally interconnected economy. The resulting slowdown of investment in the real economy impacts actors at all levels, from small businesses to homebuyers. Bankruptcies lead to job losses and a drop in aggregate demand, leading to more businesses and individuals being unable to repay their loans, reinforcing a downward spiral that can trigger a recession, depression or bring about stagflation in the real economy. This can have a devastating impact not only on economic prosperity across the board, but also consumer sentiment and trust in the ability of the system to generate longterm wealth and growth.
Leverage on the personal scale, in the financial markets and on a national scale - has contributed much - if not most - to the financial crisis 2008-2010, for scientific literature, see Buchanan (2008), Bouchaud (2008), Buchanan (2009), Farmer and Foley (2009). Leverage is generally referred to as using credit to supplement speculative investments. It is usually used in the context of financial markets, however the concept of leverage covers a range of scales from personal life, such as buying a home on credit, to governments issuing public debt.
A simple agent based model of financial markets
For the new architecture of any future financial system it is essential to understand the dynamics of leveraged investments. In the following section discuss a model phrased in terms of financial markets. Many results generated there can be straight forwardly applied to other forms of leveraged investments - of course only after taking into account the necessary modifications, identifications etc., appropriate for the particular setup. Such identifications for leveraged investments on national scales will be discussed in Section 6.5.
Evolutionary pressure for increasing leverage
Free market advocates often argue that markets are best left to operate in an unfettered manner. In this section we demonstrate that regulation of leverage is desirable from several different points of view. We first show that, under the parameter values investigated here, increased leverage leads to increased returns. There is thus "evolutionary pressure" driving leverage up, meaning that without exogenous regulation fund managers are under pressure to use higher leverage than their competitors. If this process is left unchecked, leverage rises to levels that are bad for everyone. This can lead to an increase in the number of defaults and lowers returns and profits for banks as well as for the funds themselves.
How leverage increases volatility
We now explain how leverage increases volatility by stating the argument given in Thurner, Farmer and Geanakoplos (2010). Let us begin with the case of noise traders alone, and assume for a moment V =1 for simplicity.
Leverage and systemic risk
Here follows a summary of the most important findings of the presented agent based model of the financial market (Thurner, Farmer and Geanakoplos, 2010). This report showed qualitatively how different market participants such as informed investors, noise traders, leverage providers, and investors perform their roles in their co-evolving environments. It demonstrates how their performance influences actions of others, and study the effects on e.g. the formation of asset prices. Among many other features, these mutual influences cause price fluctuations and volatility patterns which are observed in real markets.
Our findings to this point imply several immediate messages relevant for a future architecture of the financial world:
Global monitoring of leverage levels on the institution level. This could be done by central banks. Data should be made available for research. Without the knowledge of leverage levels at the institution scale, imposing and executing maximum leverage levels is pointless.
Monitoring and analysis of lending/borrowing networks, both of major financial players and of governments. It should be known who holds the debt of whom. Without this knowledge imposing maximum leverage levels becomes hard to implement.
Imposing maximum leverage levels depending on debt structure, trading strategies and position in lending/borrowing networks. Through this measure, regulators could control levels of extreme risk by limiting leverage. This is by no means a trivial undertaking, and needs to be accompanied by massive future research, concerning implications of this step.
The agent based model described above makes one point very clear. In systems with structures like our current financial markets, crisis can and will emerge endogenously, i.e. triggered by the system itself. It does not necessarily need exogenous shocks to trigger and maintain the unfolding of a crises and the eventual collapse of the system. These effects happen far more often than predicted with current models of risk management.
This report studied the effects of leverage on systemic stability in a simple agent based model of financial markets. It argues that this approach - unlike traditional approaches to risk management - allows to understand market mechanisms which can lead to large scale draw-downs and crashes. Even though the model is phrased in terms of financial agents acting in the financial markets, the essence of its findings can be transferred to national scales. This becomes especially important because of the active involvement of governments in the financial markets.
Social unrest and agent based models
In the same way as financial markets are complex systems and show potential systemic large-scale effects, dynamics of social unrest do. As argued above, agent based models - provided they capture essential features - allow for an understanding of a series of fundamental issues of dynamics of social unrest. In particular these models may help to estimate the role of certain conditions that lead to the outbreak of phases of large-scale non-cooperative behaviour. One of these models, which was mentioned in the context of financial markets, is on the verge of becoming sophisticated enough to be of actual use.
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