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Stress-testing multi-asset class portfolios: best practices and future challenges

Risk and investment professionals will need a sophisticated mix of historical and what-if scenarios to navigate today’s volatile markets, says Ivan Mitov, director of risk research at FactSet.

Almost two years have passed since the outbreak of Covid-19 and the effects of the pandemic, and the actions taken by governments around the world are already showing longer-term effects. High energy prices and the relatively rapid recovery of the global economy from the pandemic have driven up costs, with global inflation now reaching unprecedented levels for many young people in developed countries and economies.

On the other hand, the breakdown of supply chains during the pandemic has further hampered the supply of goods to meet the growing demand. The further worsening of the global geopolitical situation following Russia’s invasion of Ukraine has increased uncertainty and amplified the slowdown in the global economy.

There are emerging signs of a recession. The World Bank recently lowered its forecast for global growth by a third to 2.9% for 2022 and warns of stagflation. Such economic events have happened in the past and will happen again because they are part of the business cycle. On a positive note, cycles of recession are reversible and are often followed by expansion. Therefore, risk and investment professionals need to consider various scenarios for both recession and expansion. Depending on the current situation, data and technology can assess the likelihood of relevant events and their impacts.

Stress testing frameworks

There are well-developed risk management and stress testing practices to analyze investment portfolio profiles under different hypothetical scenarios or in the event of a recurrence of adverse historical events (e.g. Black Monday of 1987; financial crisis that started in 2007-08; and the Covid-19 pandemic). What-if and historical scenarios added value in a robust risk management and stress testing framework. It is very important for risk monitoring and for investment professionals to work together to make the best possible decisions.

In what-if scenarios, assumptions are made about the behavior of key market or macroeconomic indicators over a given time horizon. This time horizon can vary depending on the profile of the investment portfolio and the type of company (asset owners, asset managers or hedge funds, for example).

Historical scenarios generally relate to a past period in which, for example, there was a significant decline in the value of a particular asset class. An interesting factor in this case is that historical crises are different in nature and duration until markets fully recover. This period should be taken into account when applying the respective scenario to the current composition of the portfolio.

In both types of scenarios, we have a time interval that indicates a period of a past event or the hypothetical occurrence of an adverse event in the future.

The difference between the two types of scenarios lies in the data required to assess the impact of the respective scenario on the portfolio. In hypothetical scenarios, historical data is needed to assess the relationship between individual risk factors, asset classes and macro factors, such as GDP and unemployment.

Indeed, what-if scenarios generally predict a limited number of variables but, in reality, a multi-asset class portfolio depends on hundreds of risk factors. For example, the third generation of FactSet’s Monte Carlo-based multi-asset class risk model has more than 1,000 risk factors, including interest rates, equity-related factors (style, sector and country) , exchange rates and credit spread factors. Since the portfolio return is modeled as a linear combination of the factor exposures multiplied by the respective factor returns (see Equation 1), one needs to project the factor returns – and sometimes the factor exposures – for each factor in the portfolio. respective stress scenario.

The difficulty here is to assess the relationship between the various risk factors, given the specifics of the hypothetical scenario. This requires high quality data, adequate technological solutions and people to interpret the results. Only a combination of these three factors can achieve a truly meaningful stress test.

In historical scenarios, data is required for a specific historical period, such as Black Monday. The main task is to approximate the behavior of an asset class that exists now and is part of our portfolio but did not exist during the crisis. Again, this analysis requires high quality data, appropriate technology, and people to analyze and interpret the data.

Just as the approach to modeling and measuring portfolio risk depends on the investment horizon, we are interested in a robust stress testing approach that uses several types of “lenses” with different focal lengths to look ahead. One of the lenses is a wide angle (short focal length) lens that observes the situation in front of us in detail but, in turn, focuses relatively close. The other type of lens is a telephoto (long focal length) lens, which is used to zoom in and focus on specific objects that are relatively far away. In both approaches, the current reality must be taken into account, namely that markets and economies are closely interdependent.

Global Market Connectivity

It is increasingly difficult to envisage managing a portfolio concentrated in a single asset class, country or sector without taking into account the global interdependencies between them. For example, companies’ profits do not necessarily come from their country of domicile. Looking specifically at the UKFactSet Revere Geographic Revenue (GeoRev) Exposure data can be leveraged to provide a highly structured and normalized view of business revenue by geography.

Figure 1 highlights GeoRev data showing that for the 25% UK‑based, less than 10% of turnover comes from UK. It is clear that these companies depend much more on the rest of the world than on their domestic market. Measuring the impact of an exchange rate scenario where the British pound depreciates against other major currencies would not have the same impact on companies that are almost 100% exposed to GeoRevUK markets. On the other hand, the impact would be different for a company that fully exploits and sells its products or services in the UK. Without such a relevant source of data, one would rely on historical correlations between the local stock returns of these companies and movements in exchange rates, which generally cannot provide the full picture.

This is just one example of how good data can be leveraged and combined with the right technology and people to analyze results and improve risk management practices.


Future challenges

All of the issues discussed in this article are relevant to business cycles that have been observed in the past. However, there are new challenges beyond what we have observed so far, such as climate change. It’s something bigger than a local political dispute or a standard recession cycle. Here it is appropriate to create scenarios and estimate the impact not only on a given financial portfolio, but on the economy and humanity.

Climate change is accompanied by environmental, technical, social and cultural changes. Therefore, we must seek even deeper use of financial and non-financial data in our stress testing frameworks. Since there is no historical data from which to draw conclusions, some prior beliefs about events must be introduced. As these processes constantly evolve and change, people become a key factor in stress testing frameworks. It will no longer be enough to have only high-quality data and technology – companies will also need to rely on competent and open-minded people to put in place adequate risk management practices.

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