Charting the Course for COVID19: Market Implications of the Pandemic’s Estimated Daily Growth Rate

March saw the COVID-19 pandemic explode across developed market economies, most notably in the United States and Europe. Governments imposed lockdowns that brought consumer travel, leisure, dining, and entertainment activities to what is essentially a total halt of indefinite length. Markets crashed precipitously: at its extremes, the S&P 500 fell over 30% from its February peak.

Pricing the impact of COVID-19

We view the market decline in March as pricing in the first-order effect of higher risk premiums associated with the uncertain impact of COVID-19 on developed markets. In a recently released draft paper by the National Bureau of Economic Research (Alfaro et al., 2020), the authors show that changes in aggregate stock returns are forecasted by day-to-day changes in the predictions of simple models of the spread of infectious diseases. For this month’s commentary, we produce a simplified model of the same using data from the Johns Hopkins Center for Systems Science and Engineering.

Modeling the spread of infectious diseases begins with a time-series of the number of new cases reported each day (known as the daily incidence).

Source. Johns Hopkins CSSE, Orthogonal

However, daily incidence is noisy. It’s difficult to tell whether a day-to-day increase or decline is significant, or simply just noise. Daily incidence, by itself, is not a reliable way to gauge the spread of pandemic disease. To address this, we could instead use a time-series of daily incidence to make rolling estimates of the daily growth rate of a pandemic based on simple model. The simplest model, called the exponential model, assumes that each individual spreads the virus at the same rate, which has the effect of increasing infections exponentially over time. As pointed out in Alfaro et. al, this model is consistent with early stage pandemic growth patterns.

By making rolling estimates of the growth rate, r, updated daily based on the latest available data each day, it is possible to obtain a more accurate gauge of the spread of pandemic disease. Where incoming data is close to what the prior day’s estimate predicted it would be, then the updated growth rate would be relatively unchanged from the prior day. Where incoming data is significantly greater or smaller than predicted, then the updated growth rate would increase or decrease, respectively. By tracking the evolution of the estimated growth rate, it is possible to determine whether incoming data should be read as being within expectations, or alternatively as a positive/negative surprise.

The chart below plots the rolling estimated daily growth rate r for Europe and the United States using all data available as of each day in the time period January 24 up to and including April 5, 2020.

Source. Johns Hopkins CSSE, Orthogonal