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Stress Testing the Global Impacts of a U.S.-China Trade War

Market and Economics

By Ian Hissey  |  September 10, 2018

Over the last several months, the U.S. and China have exchanged numerous salvos in an escalating trade war. Just last month, in the same week that trade talks between the countries stalled in Washington, a new 25% U.S. tariff went into effect on 279 Chinese goods, matched by new Chinese tariffs on 333 U.S. product categories. Tensions are high with strong statements from both nations’ leaders, but it seems that financial markets have become desensitized.

The question on the minds of many investors and risk managers is, “what happens if the trade war continues to intensify and how will this impact my portfolio?”

Creating a Range of Possible Scenarios

One way of modeling this is through stress testing, using a risk model to help us to predict the market impact of a potential future scenario. Stress testing allows us to insert our human experience into our risk analysis. The risk model is then used to provide a framework to “fill the gaps” in our experience and propagate our expectations across the factors which drive financial markets. This means we only need to predict outcomes for a small number of factors instead of the hundreds or thousands of individual factors that impact our portfolio.

By its very nature, stress testing is subjective. This means that the likelihood that we will accurately predict the impact of some future event is very small. Therefore it is important to gain an understanding of the potential future outcomes by testing a range of possible scenarios.

How do we define a stress test for a U.S.-China trade war?

The economic intuition seems simple. Tariffs hurt the economies of both trading parties by creating inefficiencies and lowering future economic growth. This would have a negative impact on equity market valuations. In turn, sudden dramatic falls in equity valuations likely create a flight to quality assets.

How do we make reasonable assumptions about the specific factors on which to focus and the potential impact upon them? One way is to look at a similar scenario and draw parallels from it. The most recent one that comes to mind is Brexit. On June 23, 2016, Britain voted on a referendum to decide whether the UK should leave or remain in the European Union. Following the vote to leave, dubbed “Brexit,” there was sudden concern about future economic growth in the UK. We observed big knock-on effects in exchange rates and U.S. treasury yields; for example, the pound depreciated by 15% against the JPY and the U.S. 10-year bond dipped by 40 bps.

GBP falls against the yen following Brexit

US 10Y treasury drop following Brexit

Using this information, we can test three scenarios to determine the market impact of a U.S.-China trade war:

  • Base Scenario – Events continue along current path, with gradual escalation of trade tensions
  • Optimistic Scenario – The U.S. and China come to an agreement, but newly imposed tariffs remain in place
  • Conservative Scenario – Worst case scenario, with rapidly deteriorating trade relationship between the two countries

 

Using FactSet’s Multi Asset Class (MAC) risk model, I ran some common indices through the stress testing function to get an understanding of the market impact in each of these three scenarios.Table of scenario assumptions2

Equity Index Impacts

The table below illustrates the impact on the MSCI World from the perspective of a U.S.-based investor. An escalation in tariffs costs the investor between 8.43% and 16.97% of their portfolio’s value. Looking at the top/bottom five countries impacted by each scenario, it’s not a surprise to see that Japanese and European investments tend to perform best given our assumptions for the USD exchange rate. Interestingly, the biggest losers, aside from the U.S., are Hong Kong, the UK, Canada, and Israel. These are economies closely tied to the U.S. or linked via a pegged exchange rate.

Stress Testing Asset Detail Equity Index2

Fixed Income Index Impacts

Next, I performed the same analysis using the Bloomberg Barclays Global Aggregate from the perspective of a U.S.-based investor. Here we see that the investor is making money because of the flight to quality, with gains ranging between 3.59% and 6.86%. But these gains are significantly smaller than the losses we saw in the previous example with our equity index. Looking at the top/bottom five countries impacted, the biggest winners in this example are Japanese investments but also the bonds issued by smaller European nations such as Bulgaria, Estonia, and Liechtenstein, as well as Ireland. The biggest losers are the Middle Eastern countries of Saudi Arabia, Oman, and the UAE, as well as Peru and Malaysia. Note that we see a wider range of outcomes across the various country assets compared to our previous example.

 Stress Testing Asset Detail FI Index2

From this exercise, we see a wide range of potential outcomes across our three scenarios. For example, the returns for the optimistic and conservative scenarios on the MSCI World range between -8% and -17%. This illustrates the importance of testing a range of potential scenarios; while it’s highly unlikely we will correctly guess the exact impact of the factors specified, the level of their specification has a very significant impact on the outcome. In the case of the escalating trade tensions between the U.S. and China, while financial markets still appear to be discounting the global impact of a trade war, our analysis shows that if/when the market does react, the effects will be widespread. 

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Ian J. Hissey

Vice President, Specialty Sales Manager, AsiaPac Analytics

Ian Hissey is Vice President in the Portfolio Analytics group at FactSet and has been at FactSet since 2008. Ian is responsible for the Sale and Support of FactSet’s Multi Asset Class (MAC) risk product in Asia Pacific. His role is also to guide the development of that product to meet the diverse set of challenges faced by financial market participants in Asia. Previous to his current role, Ian was the manager of FactSet’s Portfolio Analytics team based in Australia for four years where he worked with FactSet’s largest clients in Australia and NewZealand. Ian has a holds a bachelor degree in Economics and Social Sciences from the University of Sydney.

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