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Stress Testing the Russia-Ukraine Conflict

Companies and Markets

By Raul Diaz de Cerio, CFA  |  June 28, 2022

The conflict between Russia and Ukraine has impacted markets globally. Based on the market impacts we have witnessed, we set out to stress test a broad global portfolio under two scenarios: the first one based on a broad surge in commodity prices and the second one focusing on the direct impacts of the conflict plus the commodity price surge.

This analysis allowed us to isolate the countries, currencies, and industries most impacted by recent events. Our goal is to show how stress testing a portfolio or benchmark can highlight the relative exposures to exogenous events.

Defining Our Stress Tests

We created two stress-test scenarios combining several relevant factors to access and understand the impact on a portfolio replicating holdings of the iShares MSCI World ETF from a variety of market shocks. We used the FactSet Daily Global Equity Model for our analysis, including data as of April 29, 2022.

Commodities Rise Scenario

  • Gold: $2500/oz
  • Crude Oil: $150/bbl
  • Natural Gas: $13/MMBtu
  • Wheat: $15/bushel
  • Nickel: $100,000/ton

Russia-Ukraine Scenario

  • Russia sub-index: 30% decrease
  • Europe sub-index: 30% decrease
  • VIX: 50
  • USD/RUB: 30% decrease
  • Commodities Rise scenario

Analysis of Factor Contribution to Returns

Let´s examine the factor contribution to the return in each scenario. To start with, we found a large difference between event-weighted and time-weighted returns, defined as follows:

  • Event weighted: Historical periods are sorted in order of similarity to the stressed factor shock amount
  • Time weighted: More recent periods receive higher weights, regardless of how similar they are to the shock amount


Event-Weighted Return (%)

Time-Weighted Return (%)

Commodities Rise






Source: FactSet

To help understand the large differences between the event- and time-weighted returns, let’s look at how the weights of the different periods are included in our stress test.


This table is sorted by event weight. We can see that the periods that compose nearly half of the sample (49.36%) for the event-weighted scenario make up only 3% in the time-weighted scenario. This explains why the big difference in the results between both methodologies, as the samples represent completely different periods.

Another interesting point here is that only three of the periods with the highest event weights are recent events (highlighted in yellow), while most of the others took place during the 2008-2009 global financial crisis.

For extreme scenarios, where correlations are likely to change, using event-weighted returns makes the most sense. Therefore, we focus on the event-weighted results, as these scenarios can be considered quite extreme (most of them never happened before).

Commodities Rise Scenario

Looking at the percent factor contribution in descending order, we see the global market as the largest negative factor contributor.


Let’s examine some of the other factors more closely.


This factor has a contribution to return (CTR) of 3.75%, indicating that in this scenario, the portfolio exposure to different exchange rates would give us a positive return. With the report currency set to USD, we see an implied factor return of zero. For the other currencies, we are comparing their performance with the U.S. dollar. The Japanese yen, euro, and Swiss franc are the largest contributors to return due to their positive exposure and high implied return.



The CTR for the country factor is 0.96%, with the main contributors being the U.S., Canada, and Australia. The last two countries are known for their high exposure to the commodities sector, with both having a high weight in their respective country benchmarks represented by Mining and Oil & Gas companies.



The industry factor has a -2.64% CTR, with the main detractors coming from the Insurance and Internet sectors, while the industries benefitting the most from the increase in commodities prices are Metals & Mining and Oil & Gas.



Size is a stand-alone style exposure standardized by country group. It represents the size of the company based on its market capitalization. Typically, small cap tends to outperform, even after considering the Beta factor. This factor helps to indicate if the size of the company itself or portfolio has a size tilt that presents obvious positive return.

How should we interpret the 0.27 size exposure in our portfolio? If our portfolio has an exposure greater than 0, we have companies with a larger market cap than the average of the universe that the risk model is using. A negative exposure indicates that the portfolio consists of smaller cap companies compared to the total universe. The exposure of 0.27 indicates that our “portfolio” has a large cap bias.

Russia-Ukraine Scenario

Now let’s turn to our Russian-Ukraine stress test, focusing on the Country, Industry, and Currency factors.


We shocked our model with the VIX at a level of 50, a number not seen since the beginning of the COVID-19 pandemic, and a decline in the Europe sub-index of 30%. Therefore, it’s not surprising to see the U.S. and many European countries with the highest negative returns, while Canada and Australia have strong positive returns.



Among industries, the exposures most negatively affecting our benchmark are Hotels, Restaurants & Leisure, and Insurance; on the positive side, Metals & Mining, and Oil & Gas contribute the most.

Bear in mind that we are weighting these exposures and the table below only displays a small number of industries. There are sectors with more extreme returns but because of their low weight in the benchmark we haven’t included them here.



According to our stress test, GBP, AUD, and NZD would weaken in this scenario while all the other currencies, especially the euro and yen, would strengthen against the U.S. dollar.



For our analysis, we stress tested a portfolio, but a portfolio manager could also analyze a benchmark to understand the relative exposures to the different factors in the model. This analysis could also be extended to study the correlation between factors or what happens when the shock horizon is adjusted.

This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.


Raul Diaz de Cerio, CFA

Senior Support Specialist, Analytics and Trading Solutions, APAC

Mr. Raul Diaz de Cerio is a Senior Support Specialist for Analytics and Trading Solutions at FactSet, based in Sydney, Australia. In this role, he works with FactSet clients to help leverage the power of FactSet's analytics and quant suite of products. Mr. Diaz de Cerio earned a master’s degree in stock exchanges and financial markets from Instituto de Estudios Bursátiles and a degree in Accounting and Business/Management from the Universidad de La Rioja, having studied for one year at the University of Southern Denmark.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.