By Prachi Saraf
Illustration by Keo Morakod Ung
Introduction
Financial crises can be varied in their causes, impacts, duration and spread. Often, they lead to questions about the failure of economists in forecasting and preventing the crisis. They are instrumental in shaping economic stability and policymaking due to the economy-wide impacts, which emphasises the importance of evaluating and looking to improve the economic models monitoring economic activity. Common patterns of financial crises include banking issues, such as bank runs which can quickly worsen. Such crises generally take place after periods of risky lending and consequent defaults on loans, as was the case with the 2008 Financial Crisis.
The 2008 Financial Crisis: A Historical Overview
The 2008 Financial Crisis, which started in the US in 2007, was driven by a number of factors. These include the increase of subprime lending and mortgages, increasing house prices, the risky actions of financial institutions, and the lack of regulation.
In the early 2000s subprime lending (lending to borrowers with poor credit history, typically with higher interest rates) increased as even credit constrained individuals were able to borrow and became prime borrowers. With banks no longer responsible for mortgages due to securitisation of assets, packaged into what are known as mortgage-backed securities (MBS) which investment banks were heavily investing in, banks became more lenient in handing out mortgages. This, combined with rising house prices, caused a house price bubble. When the housing market collapsed, causing the bubble to burst, and the inflated house prices to decline. Thus, people defaulted on their loans, and those who invested in these financial securities were in trouble as the value of the MBS plummeted, causing huge losses for these financial institutions. Furthermore, the lack of responsibility of financial institutions in understanding these new complex financial products, and insufficient oversight by regulatory agencies enabled these poor, risky practices to occur and loosened lending standards.
The crisis in the US had far-reaching impacts on the global economy. The immediate aftermath of the crisis within the US involved GDP falling significantly and unemployment rates increasing to result in the largest decline in employment since the Great Depression. Globally, there was a severe global economic downturn with global unemployment reached the highest level on record (over 7%). This was partially exacerbated by the credit crunch whereby banks became increasingly unwilling to lend. Many financial institutions also faced bankruptcy, such as Lehman Brothers, and governments had to intervene and issue bailouts to stabilise the sector. Overall, people lost faith in the financial services industry.
The Role of Economic Models in Understanding and Predicting Financial Phenomena
Economic models are crucial tools used by economists to help forecast and put mitigatory measures in place for such periods of economic volatility or crisis. These models, which are generally mathematical, help institutions like the Bank of England and the Government decide on monetary and fiscal policies. However, these mathematical models also have limitations, as evidenced by economic disasters like in 2008; there are several explanations for the failure of these models in predicting the 2008 Financial Crisis.
1. Overconfidence in Economic Models and Underestimation of Risks
The problem of overconfidence in these new financial instruments, combined with an overreliance on credit rating agencies, translated into a false reliability on the economic models and their results. Credit rating agencies were competitors with one another for business, meaning that they were not objective in assessments and ratings, leading to inflated ratings of financial products, such as MBS. Further, many of these quantitative risk models for capital setting and risk assessment underestimated extreme risks and failed to factor in conceptual thinking about risk, involving more nuanced analysis in contrast to the assumptions traditional models make. This emphasises how these models were risky instruments that were inaccurate reflections and predictors of the market, giving financial institutions and regulators a misleading sense of reliability.
2. Problems with Core Theories Behind the Models
The fundamental assumptions of the theories backing these economic models have some discordance with the real world. For example, in assuming rational behaviour, economists undermine the psychological aspects of irrational human behaviour, which can also affect people’s expectations about the future. As explored in the Dahlem Report, a paper condemning the growing reliance on mathematical models with improper assumptions about the behaviour or actors and markets, the models disregard the pivotal elements that drive outcomes in real-world interconnected markets.
3. The Limits of Traditional Models in a Globalised World
Traditional economic models did not account for the volatility of financial markets and the complexities of an increasingly interconnected global financial system. Orthodox economics is limited in attributing this limited importance to banks and other financial systems. However, as seen in the 2008 Crisis, they played an instrumental role in bringing about the crisis by creating risky products, encouraging excessive borrowing, and engaging in high risk behaviour.
Reflections and the Future of Economic Modelling
Economic modelling has significantly evolved over time, and been shaped by lessons from past financial crises. They have highlighted the importance of incorporating the complexities of the modern world into traditional models. Particularly in today’s post-COVID, conflict-ridden world, and consequently volatile and uncertain global markets, there is a need for adaptive and more robust models. Looking ahead to the future development of economic modelling, it will increasingly see the incorporation of data analytics and machine learning. A recent article by the World Economic Forum explores the benefits of harnessing AI and machine learning to enhance financial crisis forecasting; these technologies will transform the ability to identify alarming patterns early on, increasing prediction accuracy and helping mobilise better preemptive responses for such crises.
Adapting economic modelling to the increasingly uncertain global economic environment is thus crucial in order to better navigate economic challenges and prevent future crises.
UCL Rethinking Economics 