In an interview with CBS News on May 17, 2020, Federal Reserve Chairman Jay Powell highlighted the role an effective vaccine will play in ending the COVID-19 pandemic and enabling the economy to recover:
“For the economy to fully recover, people will need to be fully confident. And that may have to wait for the arrival of a vaccine. “
The next day, Moderna, one of the vaccine development companies, announced progress in its Phase I clinical trials and US stocks gained more than $ 1,000 billion in market capitalization.
Quantifying the extent of the economic damage caused by the COVID-19 pandemic is a crucial step in assessing policy responses to the social, medical, fiscal and monetary dimensions. We hypothesize that stock markets may contain valuable information for assessing the value of end the pandemic.
Stock markets, which corrected as much as 40 to 50% when the coronavirus pandemic broke out in February and March 2020, rebounded sharply in six months. A market narrative concerns advances in vaccine development. On the one hand, only the arrival (and delivery) of an effective vaccine is considered a definitive event that will end the pandemic and lead to a solid economic recovery. On the other hand, stock prices – reflecting forward-looking expectations – should reflect the economic value of credible progress in vaccine development; this value stems from the ability of vaccines to end the pandemic and is naturally linked to the extent of the economic damage caused by the pandemic.
Acharya et al. (2020) propose an asset valuation perspective to estimate the value of a cure, that is, the amount of wealth that a representative agent would be willing to pay to obtain a vaccine ending the pandemic. Our approach is directly analogous to the seminal work of Lucas (1987) in assessing welfare costs associated with business cycle risk. Just as this paper provides a framework for assessing the consequences of policy responses aimed at mitigating production volatility, our work focuses on the cost-benefit analysis of potential public sector investments to mitigate the threat of current and future pandemics.
While Lucas (1987) finds small improvements in well-being to reduce this risk, Barro (2009) reports that in a model with rare catastrophes, moderate risk aversion, and an intertemporal elasticity of substitution greater than one, the company would gladly pay up to 20% permanent income to eliminate disaster risk. Our pandemic model is close to that of rare catastrophes in the literature on asset pricing (Barro 2006, Gabaix 2012, Tsai and Wachter 2015) but with endogenous exposure of the agent to catastrophes as well as endogenous consumption, labor and asset prices.
Vaccine progress indicator and its covariance with stock yields
To test our hypothesis that the stock market can convey important information about the social value of resolving the pandemic, we begin by empirically documenting the behavior of stocks and the expected timeframe before vaccine deployment. We are building a new “vaccine progress indicator” that summarizes the state of vaccine research throughout 2020. Then, we estimate the stock market’s response to changes in the indicator.
Our indicator is based on the timeline of the step-by-step progress of individual vaccines1 and related news.2 Using data from vaccine development for past outbreaks and surveys during the current COVID-19 pandemic, we calibrate the probabilities of transition through the various stages of vaccine development and use the news to “exploit »These probabilities upward or downward. We then simulate more than 200 vaccine “trials” corresponding to vaccines under development, taking into account a correlation structure between trials based on relevant characteristics such as their approach (“platform”), membership in a joint venture, etc.
The result of this exercise is an indicator of vaccine progress using all the information available at any given time, expressed in terms of the expected time before a vaccine is deployed. The evolution of our indicator is illustrated in Figure 1.
Figure 1 Expected timeframe for vaccine deployment
Remarks: The figure shows our estimate of the expected time for the widespread deployment of a COVID-19 vaccine in years. Dashed lines show a standard deviation band.
Figure 2 presents the vaccine progress indicator (inverted) with the performance of the market portfolio since the start of the year.
Figure 2 Vaccine progress and market performance
Remarks: The figure represents the progress of the vaccine (reversed axis and left axis) as well as the cumulative excess yield since the start of the year on the CRSP value-weight index (right axis). The risk-free rate is the one-month Treasury bill rate.
We then relate stock returns to changes in the expected timeframe before a vaccine is deployed by regressing returns on changes in our vaccine progress indicator, controlling lagged returns as well as large movements due to the release of the vaccine. other macroeconomic news. Allowing for a certain lead-lag structure in the relationship – for example, due to information leaks or dating rumors in our news data – we estimate that a reduction of one year in the expected timeframe for the deployment of a vaccine results in an increase in the stock return as a whole between 4% and 8% on a daily basis. The joint relationship presents the expected cross-sectional properties, with the co-movement between returns and changes in the vaccine progress indicator being stronger for the sectors most affected by the COVID-19 pandemic (see Figure 3).
figure 3 Industry sensitivity to vaccine advances
Remarks: The figure represents the industry’s sensitivity to vaccine advances versus COVID-19 exposure, measured by cumulative yields. Cumulative returns are from February 1, 2020 to March 22, 2020. Sensitivity to vaccine progress is estimated from March 23, 2020 to October 31, 2020.
Value of a cure
We relate this empirical co-movement of stock returns and the advance of vaccines to the value of a cure using a general equilibrium regime change model of pandemics with implications for asset prices. In our model, the economy can be “normal”, that is to say without a pandemic, or a pandemic. Within the pandemic, several regimes correspond to the stages of vaccine development.
A key feature of our model is that the agent withdraws from work in pandemic states in order to mitigate economic exposure to a health shock. In other words, the onset of a pandemic and the impact of a health shock for the agent within the pandemic can be considered rare disasters, the exposure of which is partly controlled by the agent.
A primary insight from our asset valuation perspective is that the improvement in agent well-being upon exiting a pandemic is related to the extent of labor contraction in the pandemic state versus in a non-pandemic state; this same labor force contraction is an important statistic (modulated by preference and pandemic parameters) that determines the sensitivity of stock prices to progress towards vaccine deployment.
The model implies that the value of the transition from a pandemic state to a non-pandemic state is simply the ratio of the marginal propensity to consume in the pandemic state to that in the non-pandemic state, increased by the intertemporal elasticity of the substitution. Thus, the desire to resolve uncertainty earlier is informed by endogenous consumption choices made by the household in pandemic states.
We can therefore easily relate our empirical work to the theoretical perspective of asset valuation. With standard preference parameters used in the literature, the value of a cure turns out to be 5 to 15% of wealth (formally, the stock of capital in our model). In our basic assumptions for the other parameters, this corresponds to a contraction of about 25% of labor during the pandemic compared to the non-pandemic state.
The reason the economy would place such a high value on the vaccine is that the pandemic causes a permanent loss of capital stock when it affects agents, which in turn is reflected in the large contraction of the workforce. work as a precaution during the pandemic.
Learning and uncertainty
Given the rare nature of pandemics and the evolving understanding of the links between various pandemics (SARS, H1N1, COVID-19, etc.), we assess the effect of imperfect information on the value of a remedy. We specialize our framework on just two states – non-pandemic and pandemic – but allow uncertainty about the frequency and duration of pandemics.
It turns out that the remedy’s value increases sharply when there is uncertainty about the frequency and duration of pandemics. Indeed, we find that the representative agent would be willing to pay as much for the resolution of the uncertainty as for the cure in the absence of such uncertainty. This effect is stronger – not weaker – when agents have a preference for later resolution of uncertainty, as this induces greater labor contraction during pandemics.
An important policy implication is that understanding the fundamental biological and social determinants of future pandemics – for example, whether pandemics are linked to zoonotic diseases triggered more frequently by climate change – may be as important in mitigating their economic impact as resolving the immediate problem caused by the pandemic. crisis.
A number of papers have modeled climate risk using an approach similar to the one we take to model pandemics. Pindyck and Wang (2013), for example, explore the welfare costs associated with climate risk and estimate the amount that society should be willing to pay to reduce the likelihood or impact of a disaster. Our framework can also be useful in the context of climate risk issues, such as how much society should be willing to incur as costs for the adoption of clean technologies.
Acharya, V, T Johnson, S Sundaresan and S Zheng (2020), “The Value of a Cure: An Asset Valuation Perspective”, Economics of Covid 61: 1-72.
Barro, RJ (2006), “Rare disasters and asset markets in the 20th century”, The Quarterly Journal of Economics 121 (3): 823–66.
Barro, RJ (2009), “Rare disasters, asset prices and welfare costs”, American Economic Review 99 (1): 243–64.
Gabaix, X (2012), “Rare variable disasters: An exactly solved framework for ten enigmas in macro-finance”, Quarterly economic review 127 (2): 645-700.
Lucas, R (1987), Business cycle models, Oxford: Blackwell.
Pindyck, r S and N Wang (2013), “The Economic and Political Consequences of Disasters”, American Economic Journal: Economic Policy 5 (4): 306-39.
Tsai, J and JA Wachter (2015), “Catastrophe risk and its implications for asset pricing”, Annual review of financial economics 7 (1): 219-52.
1 Obtained from the Vaccine Center of the London School of Hygiene and Tropical Medicine.
2 Obtained from FactSet.