Tail Risks - Peculiar, yet Pervasive
Updated: Jul 1, 2020
Shruti Agarwal
B Com (H), Hansraj College
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The idea of tail risks was not famous until the Global Financial Crisis happened. These are the risks which act in rarest of the rare events but are always hanging over our heads. In layman's terms, tail risk is an event that takes place in rare and extraordinary circumstances but still can happen. They are important because there is an exponential relation between the size of the event and the frequency of its occurrence. We will later understand the stupendousness of these risks by analysing how they gave rise to the financial crisis of 2008.
Technically, these risks are present at the extreme ends of a normal distribution. A normal distribution is the distribution function for random, independent variables. It is characterised by- mean and standard deviation, where mean is the maximum of the graph and about which the graph is always symmetric whereas standard deviation represents the amount of dispersion from the mean (average). We can study tail risks in the context of an investment portfolio. Normally the returns are in normal distribution but the theory of tail risk states that the returns are not always normally distributed and are mostly skewed, which means that the investment may move more than 3 standard deviations. This implies that the amount of dispersion from the average returns is huge and it generates a need to study tail distribution because tail risks are often difficult to model on the normal distribution graph. In other words, it is the risk that there are more actual events in the tail of a probability distribution than probability models would predict.
Let us recall a series of events that drew the attention of greedy and profit-seeking CEOs and bankers toward tail risks. Before the Global Financial Crisis (2008) happened, CEOs, investment bankers and traders in the United States of America, paid little attention to risk management and followed some risk management norms only because they were regulatory. This is well depicted by an instance narrated by Dr Raghuram G Rajan in his book 'The Fault Lines' wherein a meeting of risk managers and academicians, one astute risk manager told him, "You must understand that anyone who was worried, was fired a long ago and is not in this room". In light of enough evidence, we can infer that bankers and traders were hungry for hefty returns, superior profits, more customers and enormous business. This led them to ignore tail risks, whose chances of default are very low but once they occur, the cost is humongous.
There were two types of risks involved- default risk (the risk of default on securities) and liquidity risk (not having enough cash to service the debt). So, default risks were ignored because the securities involved were high rated securities such as AAA-rated bonds (they would rarely default given the supremacy of the management) and the liquidity risks were ignored merely based on the thought 'we have enough financing!'. As it was believed that both these events were rare in nature, it gave rise to the tail risks. Well, these risks were worth taking at that point of time because the portfolios were highly diversified (backed by numerous mortgages and loans across industries) and default of hundreds of securities across sectors was needed to default the senior securities bearing these risks. Despite low probability, these risks should not be ignored. In search of alpha (superior profits) and enormous compensation, bankers and CEOs took virtually unbounded risks. In the end, the event that was least expected happened- mortgage-backed securities, collateralized debt obligations and eventually high rated bonds began to default which led to the collapse of the whole financial system. No one thought it would happen but it did and the price paid is known to all.
In light of this example, the study and measurement of tail risks became an important area of study in the field of finance. The tools that can be used to measure tail risks are, namely, Historical Scenario Analysis, Hypothetical Scenario Analysis and Correlation Scenario Analysis. All these tools are not completely effective but with time, have emerged to be useful while measuring risk whose occurrence is very rare. The Historical Scenario Analysis creates an analogy between a historical event whose features are similar to the features of the event whose risk has to be calculated. The comparison drawn is used to decipher the chronology of the risks. Hypothetical Scenario Analysis is an extension of the historical scenario analysis, where an event that 'might happen' is curated and used as a model to determine the tail risks. Lastly, Correlation Scenario Analysis tries to determine the frequency of the occurrence of a peculiar event by assessing correlation among independent variables. For example, at the time of a crisis, the correlation between the prices of bonds of different sectors approaches to one, creating an observable pattern which helps in anticipating unusual circumstances. Apart from these tools, the technique of 'portfolio diversification' is also used to manage the tail risks and to hedge them. Instruments such as options and credit default swaps also act as insurance against such risks.