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Managing my investments
Managing my investments

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6.2.1 Risk and the global financial crisis: the great eye-opener

An image of the Lehman Brothers sign being carried into Christie's auction house.
Figure 2 Lehman Brothers – the biggest banking casualty of the 2007/8 banking crisis.

The belief that all relevant risks are those that can be measured from a quantitative perspective is certainly an accurate reflection of how large organisations, such as banks and financial institutions, as well as regulators and governments, have tended to treat risk.

Quantitative models provide the basis for a tangible assessment of risk and such models can be flexible in their applications. The flexibility of this approach to measuring risk helped to facilitate the boom in financial services in recent decades, in particular in the area of complex financial instruments like derivatives (for example, futures) and securities (for example, mortgage backed securities).

Financial institutions believed that their quantitative models of risk were accurate and were a genuine revolution in risk management. This allowed banks to develop and trade in ever more complex instruments. The regulators of financial institutions and the credit rating agencies also used the same types of quantitative risk models when assessing the actions of the financial institutions and the securities they were issuing.

The practice of assessing risk more or less exclusively from quantitative methods became severely challenged in the late 2000s with the onset of the financial crisis. A fall in US property values resulted in banks in the US incurring huge financial losses. The interconnectedness between US and other banks internationally subsequently triggered the most severe global financial crisis since the 1930s.

What quickly became clear was that the risk management models had failed in a number of areas – not least through the failure to take into consideration just how interconnected global markets had become. As banks revalued their investments, it became evident that the banking sectors in many developed countries were financially vulnerable. Government intervention, on a scale unimaginable prior to 2007, was required to avert a financial catastrophe, with many of the world’s largest banks requiring injections of capital from public money.

In response to the financial crisis, there have been two significant changes in attitude towards risk. Firstly, there has been an increase in regulation worldwide in addition to new legislation. This has been seen in higher capital requirements for banks, the potential ringfencing of retail banking from the more risky activities of investment banking to protect depositors and the curtailment of certain trading practices. In the US, for example, the Dodd-Frank Wall Street Reform and Consumer Protection Act, passed in 2010, imposes restrictions and greater regulation on the trading of financial instruments. Additionally, the Act has required greater public transparency of trading activities.

The second significant development has been a more open-minded attitude towards indicators of risk. In essence, the events of the financial crisis and its subsequent fallout have put a major question mark over the effectiveness of existing approaches to evaluating, managing and measuring investment risk. This has naturally led to an extensive debate as to why existing risk practices failed so conclusively in predicting both the onset and extent of the financial crisis. Have these models merely miscalculated the risks financial organisations were taking? Was something missing for the inputs they used to make their predictions in the first place?

Classical finance makes the assumption that not only do people act in a rational way when it comes to financial decision-making, but that they will also act in a way that delivers the optimum outcomes. For example, the Efficient Market Hypothesis (EMH) – which we explored in Week 3 – promotes the belief that markets are efficient, with market prices reflecting the value of assets accurately and fairly. But for the quantitative models to be both effective and consistent, it must be assumed that people act in the same rational and predictable way when presented with the same set of parameters. If behaviour is not rational and predictable, risk models are not capturing factors that will affect (and potentially make more divergent) the outcomes of decision-making.

Activity 6.1 Discounting human behaviour

Do you think that human behaviour has been omitted from conventional risk management practices because:

  • it was not perceived as a risk?
  • it did not easily fit a quantitative approach?

Why do you think the dominant risk models at the time failed to take into consideration a greater degree of human psychology?

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Several reasons may apply. Primarily, an accepted quantitative model of human behaviour simply does not exist. It may also have been driven by economic and business demands for neat solutions. The longer the financial boom of the 1990s and early 2000s went on without material problems, the more confidence the banks, regulators and governments had that the quantitative models were monitoring and assessing risk appropriately.

A man holding his head as he looks at two computer screens filled with graphs.
Figure 3 A bad day in the dealing room.