COVID-19 Rocks Risk Management

By: Toviya Slager  |  December 20, 2020
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By Toviya Slager

Risk carries many definitions. Sometimes it is referred to as the chance of losing money while other times it is seen as the chance that the actual outcome will be different from the expected outcome. This summer, the reality was very different from the expected and required many banks’ and financial institutions’ risk departments to redevelop the way they model, analyze, and assess risks within a very short time frame. There were many “black swanevents that were beyond the normal expectation; the 10-year treasury yield fell below 1%, unemployment abruptly skyrocketed to 13%, and mortgage-backed securities delinquency rates spiked. Each of the risk departments had to address these issues in their own way. 

Perhaps the most catastrophic failure happened within the model risk management groups. The models that banks and financial institutions relied on had options of partial economic shutdowns, but did not account for the possibility of a full market shutdown. They relied on historical data that was not agile enough to move to high-frequency data, which may have helped put the models back on course. High-frequency data uses very recent data to build a model instead of historical data which is usually worthless in a crisis. This meant that many firms found themselves without useful models, leaving them with options such as relying solely on expert opinion, recalibrating using only recent data, or using a modified model with an expert opinion. This, however, led to inconsistent modified models within the firm. Moreover, rating systems could not properly calculate risk, early warning systems lost accuracy, and liquidity models errors put positions at risk. For example, models that are not designed for the event they are predicting can lead to results such as produce many false alarms or discount the importance of a preemptive warning. Many of the fundamental ratios (including hedging ratios) become inaccurate during a crisis. Another problem was that there were no back-up models or procedures to fall back on in the event of current models becoming unreliable. Banks and financial institutions will need to build models in the immediate future that can account for extreme black swan events in the case that they occur again.

Credit risk, the department responsible for calculating the risk of a loan not being paid back, fared much better in the COVID-19 crisis than they did in the Financial Crisis of ‘08. This was partially because of the better cash positions that were mandated by the  federal regulations. Most countries loan-coverage ratios have increased since 2008. While most European countries only increased slightly (between 1-10%), the United States increased almost 60%, and China increased almost 70%. However, there are still problems that need to be faced such as rapidly changing credit scores, devaluation of underwritten property, and macroeconomic changes that need to be included in risk calculation. When the lockdowns were lifted, companies once again began asking institutions for loans; however, the metrics to determine the risk of default could no longer be calculated on the standard models. Additionally, banks needed to recalculate the risk of previous loan default and how that would affect capital reserves required. 

The most noticeable risk area for most is that of operational risk. With the rising popularity of the WFH culture, the entire operational risk, specifically regarding cyber risk,  has changed dramatically. But moving entirely to home offices within days hasn’t come without challenges. Some firms miscalculated the bandwidth needed, causing servers to become overwhelmed and resulted in many employees working offline. This seemingly small change led to the  increasing risk of hacking, as the distance from co-workers made fishing emails easier since minimal verification is needed when coworkers are sitting an arms length away. This data is further reinforced by a report conducted by FINRA that claimed there was a spike in cyber attack reports within the first two quarters of 2020 than all of 2019. Additionally, domain names related to COVID-19 have increased four-fold in the second quarter and pretend to carry important COVID-19 related information to perform phishing attacks. Perhaps the most creative attack is criminals congratulating people on a recent presentation over LinkedIn and including a malicious link in the comment. Cyber insurers have become much stricter due to this increased risk and insurance premiums have been increasing as well. Safety requirements such as 2-step verification processes and AI-driven security systems are now being required to remain insured. 

Although the crisis led to many difficulties, it did highlight the shortcomings in the risk departments and allowed for major adjustments. Additionally, as many firms adjust their models, they are often including new variables that will help them model future disasters before they happen. One such factor is climate change, which includes risks of rising sea levels, changing weather patterns, and increased atmospheric carbon dioxide. Many of these factors are changing the productivity and danger level of many areas on earth. Many institutions have been pushing off incorporating these factors into their models thinking that the hard work can be done later, yet, COVID-19 has forced the creation of new models and allowed many of these factors to be included. 

To highlight the reason many firms realized the importance of including these new variables, one needs to just look at firms that label themselves Environment, Social and Corporate Governances (ESG). According to Titan Investors, hedge funds that focused on ESG saw the risk of the pandemic in January, a month earlier than other funds. This allowed them to plan ahead and protect their portfolios for when the market crash was going to happen. Such an example of how using newer variables help create greater accuracy in predicting the markets highlights why many firms are looking at updating many of their models.

For many of the new traders who began after the crash, looking at the burden institutional investors faced from a downturn may help give a word of caution in the risky space of equities. It is fun when the market is up, but when it dips, even the smartest have difficulty knowing what will happen.

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