

This week, I build an index of epidemic intensity that is negatively related to equity market performance, both in EMs and DMs. The index relation with currency performance is weaker, especially in DMs, which reflects the wider range of factors driving currencies.
Overall, the severity of the COVID epidemic is somewhat worse in EMs than DMs, though of course cross-country comparisons are limited by data quality and availability. I am going to focus on confirmed cases rather than mortality data due to inconsistent death reporting and attributions across countries.
The MSCI classification of countries between developed and emerging markets is based on financial markets development rather than income per capita. Since the latter is a more important driver of countries responses to COVID-19, I have included high income EMs, namely the Czech republic, South Korea, Taiwan, Qatar, Saudi Arabia, and the UAEs in my DM list. I have also excluded China due to data issues.
This article is only available to Macro Hive subscribers. Sign-up to receive world-class macro analysis with a daily curated newsletter, podcast, original content from award-winning researchers, cross market strategy, equity insights, trade ideas, crypto flow frameworks, academic paper summaries, explanation and analysis of market-moving events, community investor chat room, and more.
This week, I build an index of epidemic intensity that is negatively related to equity market performance, both in EMs and DMs. The index relation with currency performance is weaker, especially in DMs, which reflects the wider range of factors driving currencies.
Overall, the severity of the COVID epidemic is somewhat worse in EMs than DMs, though of course cross-country comparisons are limited by data quality and availability. I am going to focus on confirmed cases rather than mortality data due to inconsistent death reporting and attributions across countries.
The MSCI classification of countries between developed and emerging markets is based on financial markets development rather than income per capita. Since the latter is a more important driver of countries’ responses to COVID-19, I have included high income EMs, namely the Czech Republic, South Korea, Taiwan, Qatar, Saudi Arabia, and the UAEs in my DM list. I have also excluded China due to data issues.
Chart 1: COVID-19 Incidence and Testing
Source: John Hopkins, Macro Hive
Chart 2: COVID-19 Adjusted Incidence and Testing
Source: John Hopkins, Macro Hive
COVID-19 incidence is lower in EMs than DMs (chart 1). There could be legitimate reasons for that, for instance scientists have identified a strong negative relationship between the incidence of COVID-19 and that of malaria, that is endemic in about half of my EMs, especially in India, Indonesia and Pakistan.
At the same time, EMs’ lower incidence is bound to reflect reflects much lower testing in line with their more limited medical resources. There are of course exceptions: Japan’s and Taiwan’s testing is more in line with EMs’ rather than DMs. Conversely, Russia and Turkey testing is more in line with that of DMs.
A crude but nonetheless interesting way to correct for testing differences is to divide confirmed COVID-19 cases by the number of tests (chart 2). With this transformation, the relation between EMs prevalence and testing becomes much weaker. In addition, by contrast with unadjusted incidence, the difference between EMs and DMs average adjusted incidence is statistically insignificant (table 1). That said, testing data needs to be taken with a grain of salt as it is not clear whether countries are recording the number of tests or the number of individual tested and the quality of testing varies across, and sometimes, within countries.
Chart 3: Days to Double Cases
Source: John Hopkins, Macro Hive
As mentioned above Taiwan and Japan stand out among DM for their limited testing, yet Taiwan is much more of a success story. This can be shown by a much flatter epidemic S curve in Taiwan. Chart 3 shows two indicators of the curve slope: the number of days it took for cases to double to their value of April 15th (backward looking) and the number of days it would take for cases to double based on the five day average growth to April 15th (forward looking).
Based on these indicators, epidemic curves are generally flatter in DMs than EMs; in addition DMs have made more progress than EMs in flattening their curve (their dots are further away from the 45 degree line). Among EMs, Thailand, Malaysia and Greece have made the most progress and Russia, Peru and India the least.
Among DMs, Korea, Hong Kong and Taiwan have the flattest curves while Singapore, Saudi Arabia, Qatar and the UAE have the least flat curves. The last four countries have large guest workers populations that in Singapore have been the source of a second wave of new cases. Guest workers tend to live in tight housing that can make infections difficult to control. Other DMs lagging with curve flattening include France, Ireland and Japan.
Chart 4: Epidemic Intensity Index
Source: John Hopkins, Macro Hive
Based on this analysis, I have built an index of epidemic intensity that consists of the normalized ratio of test adjusted cases to days to double (forward looking). The index shows that the epidemic is somewhat more intense on average in EMs than DMs. It is least severe in Hong Kong, Taiwan, Korea, Australia and New Zealand and most intense in Brazil, France, the UK, Peru and Belgium (chart 4).
Chart 5: Epidemic Intensity and Equity Markets
Source: Macro Hive
Chart 6: Epidemic Intensity and FX Performance
Source: Macro Hive
The index is negatively correlated with equity market performance, both in EMs and DMs (chart 5). This is consistent with markets expecting a V-shaped recovery: as the epidemic abates, growth is expected to recover. By contrast, the relationship between epidemic intensity and currency performance is not as strong, especially with DM currencies (chart 6). This likely reflects that currencies are driven by a wide range of factors, monetary and otherwise and for EMs access to external funding.
Dominique Dwor-Frecaut is a macro strategist based in Southern California. She has worked on EM and DMs at hedge funds, on the sell side, the NY Fed , the IMF and the World Bank. She publishes the blog Macro Sis that discusses the drivers of macro returns.

(The commentary contained in the above article does not constitute an offer or a solicitation, or a recommendation to implement or liquidate an investment or to carry out any other transaction. It should not be used as a basis for any investment decision or other decision. Any investment decision should be based on appropriate professional advice specific to your needs.)