COVID-19 Tail Risk

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COVID-19 Tail Risk

Abstract

We show that such uncertainty is priced in the option market. The cost of option protection against downside tail risks is larger when firms are located in the sub-state where the mobility trend is lower. For the firms that are in the industries most impacted by COVID, the cost of protection against downside tail risk is magnified, and it decreased after the announcement of the government purchase of the vaccine in August 2020.

Keywords: Cross-section, Implied volatility, COVID-19, Tail risk.

JEL classification:G12, G13, G14.

1. Introduction

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This paper focuses on quantifying tail risk of the S&P 500 stocks induced by the COVID-

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19 pandemic in the U.S. during 2020 and 2021. U.S. have become one of the most

3

impacted country by COVID-19 since March 2020 after the first confirmed case was

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reported. The financial market has recognised influence of the pandemic on greater

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economies. On March 12, 2020, S&P 500 plunged 9.5%, its steepest one-day fall since

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1987. Extensive literature studies the impact of the COVID-19 outbreak on performance

7

and stability of global stock markets (see, e.g., Al-Awadhi, Al-Saifi, Al-Awadhi, and Al-

8

hamadi, 2020; Ali, Alam, and Rizvi, 2020; Ashraf, 2020; Baek, Mohanty, and Glambosky,

9

2020; Baig, Butt, Haroon, and Rizvi, 2021; Haroon and Rizvi, 2020; Liu, Manzoor, Wang,

10

Zhang, and Manzoor, 2020; Zhang, Hu, and Ji, 2020). Among the few of it that exam-

11

ines tail risk from a forward looking perspective, Agarwalla, Varma, and Virmani, 2021

12

focus on the Nifty index options and find the importance of higher moments in capturing

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uncertainty during a pandemic. Li, Ruan, Gehricke, and Zhang, 2021 and Li, Ruan, and

14

Zhang, 2022 on the other hand study tail risk of the Chinese index option market and

15

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global index options market and find that changes in COVID-19 brought on by the po-

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litical movement impact the cost of protection against downside tail risk. The lacking of

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the firm level tests raises a question of whether firms with different characteristics and

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within different industries react to such risk differently.

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In this paper, we test whether COVID-19 uncertainty is priced in the option market

20

in the cross-section. By taking into account the firm specific effect, we shed light on the

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relation of exposure to the COVID-19 pandemic and expensiveness of option protection

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at the firm level. Specifically, we explore whether the cost of option protection against

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downside tail risks is larger for firms that are exposed more to COVID due to the loca-

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tion of their headquarters. We also explore whether the cost of option protection against

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increases in return volatility (variance risk) is larger for those firms. The first option

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measure, the implied volatility slope (Slope) borrowed from KPV, is defined as the coeffi-

27

cient of a function relating the left-tail implied volatility to moneyness, measured using

28

the Black-Scholes delta. A more positive value of Slope indicates that deeper OTM puts

29

are relatively more expensive, suggesting a relatively higher cost of protection against

30

downside tail risks. The risk-neutral skewness (RNS) is constructed following Bakshi

31

et al. (2003, BKM hereafter) and quantifies the asymmetry of the risk-neutral distribu-

32

tion. RNS also provides information about the expensiveness of protection against left

33

tail events, though now relative to right tail events. As RNS captures the distribution of

34

the probability mass in the left versus the right tail of the risk-neutral distribution, it can

35

be interpreted as the cost of protection against left tail events relative to the cost of gain-

36

ing positive realizations on the right tail. VRP is computed as the difference between the

37

risk-neutral expected and the realized variance (Carr and Wu, 2009; Bollerslev, Tauchen,

38

and Zhou, 2009).

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Slope and RNS capture the relative cost of protection against left tail risk, whereas

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VRP captures the cost of protection against general uncertainty-related volatility changes

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in down and up directions. We therefore develop three hypotheses. Hypothesis 1: The

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cost of option protection against downside tail and variance risks associated with climate

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policy uncertainty is higher when the mobility trend is lower. Hypothesis 2: The cost of

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option protection against downside tail risks for the firms in the most impacted indus-

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tries by COVID increases more when the mobility trend is low. Hypothesis 3: The cost of

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option protection against downside tail risks associated with the COVID mobility trend

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declined after the announcement of the government purchase of the vaccine.

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We find that mobility has a negative and significant effect on Slope. We cannot detect

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that a higher mobility trend is associated with a more negatively skewed risk-neutral

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distribution of a firm’s stock return (RNS). On the contrary, RNS is negatively related to

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the mobility. The results for RNS may reflect that this measure does not directly capture

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left tail risk. Firms that are exposed more to the COVID-19 risk exhibit a more variance

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risk premium (VRP). The results indicate that a lower mobility trend increases the firm-

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level likelihood of left and more right tail events, and it also has some effect on firm-level

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VRP.

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To test Hypothesis 2 if the industries on the lists are most/least impacted by COVID-

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19 from a perspective of the cost of protection against downside tail risk, we include

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dummy variables in the specification, Most impacted/Least impacted, which equals one

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for the industries that are most/least impacted by COVID-19 according to S&P Global,

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and zero otherwise. Excluding the most impacted industries weakens the explanatory

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power of the mobility trend on Slope. During our sample period, the total effect of the mo-

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bility trend on Slope of the most impacted industries is also statistically significant. On

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the contrary, excluding the least impacted industries does not weaken the explanatory

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power of the mobility trend and the total effect of the mobility trend on Slope of the least

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impacted industries is 0 (=0.10-0.10) and statistically significant. The same patterns can

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be observed for VRP. This result is consistent with the identification of the most and

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least impacted industries during COVID by S&P Global and supports Hypothesis 2.

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We use the middle date of the vaccination announcements in August 2020 as an

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event that reduced COVID-related uncertainty in the short term to test Hypothesis 3.

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The policies and announcements during COVID-19 are relatively unexpected as opposed

71

to major policy shifts during normal times. The announcement of reaching the purchase

72

deal of the vaccine should have lowered the cost of option protection for the U.S. firms

73

especially the most impacted ones because of the COVID pandemic. To quantify the

74

effect of this event, we estimate a regression that includes a dummy variable in the

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specification using daily option data around August 01, 2020. In this regression, Post

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announcement equals one for all firm-day observations after August 01, 2020, and zero

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for all firm-day observations before. We use Slope, RNS, and VRP as the proxy for right

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tail, right tail relative to left tail, and general risk that are revealed by the option prices,

79

and employ a relatively wide event window of [180; +180] days as daily option measures

80

for single names tend to be noisy and driven by idiosyncratic effects. For robustness,

81

we exclude in some tests the [50; +50] days around the event day. We report results

82

with fixed effects. We find that Slope for the firms in the least impacted industries

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are generally smaller than those in other industries, consistent with Hypothesis 3, and

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such difference gets smaller after the announcement. RNS and VRP can capture other

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risk related to COVID that is represented by latent variables. We conclude that Slope

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captures the left tail risk more efficiently comparing with the other two option measures.

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The rest of the paper is organized as follows. Section 2 describes our data and spec-

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ifies the variable construction. Section 3 presents the major empirical results on the

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price premia of option protection against the risk associated with COVID-19-induced

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uncertainty, and Section 4 concludes.

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2. Data and methodology

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2.1. Data

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The daily dataset for this study spans over January 2020 to December 2021 inclusive.¹

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We use option market measures to identify the effects of the COVID-19 pandemic. Option

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prices subsume expectations about investment opportunities (Vanden 2008), and option-

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based variables work well in predicting future assets price dynamics (e.g., Christoffersen,

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Jacobs, and Chang 2013). Most importantly, options-based measures reflect expecta-

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tions about all possible future events. We use options data from the Surface File of

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Ivy DB OptionMetrics. The Surface File contains daily Black-Scholes implied volatili-

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ties for standard maturities and delta points (for absolute deltas from 0.2 to 0.8, with

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0.05 delta increments). The implied volatilities are created from closing options prices

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through inter-and extrapolation in the time and delta dimensions. Although these im-

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plied volatilities do not correspond to traded option contracts and form a standardized

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volatility surface, they reflect the consensus expectations of market participants priced

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into the options. We select OTM calls and puts with absolute deltas smaller than 0.5 and

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constant maturity of 30 days. Return and market capitalization data are from CRSP. We

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use the Treasury yield data and match the interest rate maturity to the option matu-

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rities by interpolation/extrapolation to proxy the risk-free rate.² We also obtain data of

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the US mobility trends report consisting of indexes on driving, walking, and transit from

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Apple.³ The headquarter of firms that we use to match the mobility data is obtained

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from Compustat. We collect the number of the daily new confirmed cases on the U.S.

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1The novel coronavirus was officially identified and reported since 2020. Our data ends on December 31, 2021 due to option data availability.

2The Treasury yield data are downloaded from the website of the United States Department of the Treasury. Retrieved from: https://www.treasury.gov/resource-center/data-chart-center/

interest-rates/Pages/TextView.aspx?data=yield[Online Resource]

3Beginning January 13, 2020, Apple has published daily mobility trends that are based on requests for directions in Apple Maps. Retrieved from:https://www.apple.com/covid19/mobility[Online Re- source]

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county level from the Our World In Data database.⁴

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2.2. Methodology

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The first option measure, the implied volatility slope (Slope) borrowed from KPV, is de-

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fined as the coefficient of a function relating the left-tail implied volatility to moneyness,

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measured using the Black-Scholes delta. Specifically, Slope is the slope coefficient from

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regressing implied volatilities of OTM puts (deltas between -0.5 and -0.1) on the corre-

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sponding deltas and a constant. Because far OTM puts (with smaller absolute deltas)

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are typically more expensive, Slope usually takes positive values. A more positive value

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of Slope indicates that deeper OTM puts are relatively more expensive, suggesting a rel-

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atively higher cost of protection against downside tail risks. Because Slope is defined as

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a regression slope, it measures relative expensiveness and does not depend on the aver-

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age level of the implied volatility. This feature allows us to compare the measure across

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firms with different levels of general risk. Slope is our preferred measure as it most di-

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rectly captures the relative cost of protection against downside tail risk. Intuitively, it

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quantifies the cost of protection against extreme downside tail events relative to the cost

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of protection for less extreme downside events. We derive our results from options with

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1-month maturities.

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The risk-neutral skewness (RNS) is constructed following Bakshi, Kapadia, and Madan

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(2003, BKM hereafter) and quantifies the asymmetry of the risk-neutral distribution. It

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is computed using the standard formula for the skewness coefficient, that is, as the third

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central moment of the risk-neutral distribution, normalized by the risk-neutral vari-

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ance (raised to the power of 3/2) following the procedure of the calculation of SKEW

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index developed by CBOE. By being normalized, RNS also provides information about

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the expensiveness of protection against left tail events, though now relative to right tail

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4Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/coronavirus- data’ [Online Resource]

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events. As changes in the distribution asymmetry are driven by the probability mass

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in the downside relative to the upside region, RNS is affected by both tails. In terms

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of interpretation, more negative values of RNS indicate a relocation of probability mass

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under the risk-neutral measure (i.e., after adjusting for preferences toward risk) from

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the right to the left tail. As RNS captures the distribution of the probability mass in the

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left versus the right tail of the risk-neutral distribution, it can be interpreted as the cost

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of protection against left tail events relative to the cost of gaining positive realizations

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on the right tail.

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VRP is computed as the difference between the risk-neutral expected and the realized

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variance (Carr and Wu 2009; Bollerslev, Tauchen, and Zhou 2009). We use the risk-

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neutral variance (RNV) following BKM using again the interpolated 30-day volatility

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surface. The realized variance (RV) is computed from daily log returns over a future

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window, that is, with a length corresponding to the maturity of the options used for the

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risk-neutral variance. VRP captures the cost of protection against general uncertainty-

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related volatility changes in down and up directions, whereas Slope and RNS capture

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the relative cost of protection against left tail risk (relative to “normal” risks, Slope, or

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relative to the right tail, RNS).

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Each monthly measure is calculated as the average across daily values. We use

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(one plus) the cases’ natural logarithm because confirmed cases are usually 0 at the

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start of the pandemic and when the containment measures take into effect. We con-

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trol for firm characteristics that prior work identified as determinants of firm risk, no-

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tably log(Assets), Dividends/net income, Debt/assets, EBIT/assets, CapEx/assets, Book-

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to-market, measured at the previous year, and Past returns measured at the previous

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day/month. Table 1 shows that the mobility trend is negatively skewed. During the two-

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years COVID-19 period, the mobility trend experienced small recovery of the permitted

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activity and large restrictions of it.

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3. Empirical Results

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3.1. Mobility and downward option protection: Cross-sectional results

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Table 2 tests Hypothesis 1 and reports firm-level regressions of the effects of Apple Mo-

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bility trends on option market measures. Column 1 shows that mobility has a negative

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and significant effect on Slope. A one-standard-deviation increase in the mobility trend of

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the county where a firm’s headquarter is located increases Slope by 0.019, which equals

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6% of the variable’s standard deviation. Slope is generally lower for firms that are larger

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and more profitable. It is higher for firms with higher leverage and with higher book-

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to-market ratios. Column 2 shows that we cannot detect that a higher mobility trend is

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associated with a more negatively skewed risk-neutral distribution of a firm’s stock re-

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turn (RNS). On the contrary, RNS is negatively related to the mobility. A one-standard-

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deviation increase in the mobility decreases the RNS by 0.0312, or 9% of the standard

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deviation. The results for RNS may reflect that this measure does not directly capture

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left tail risk. Instead, RNS captures the cost of protection against left tail events relative

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to right tail events. The cost of protection of downside tail risk increases to a lower extent

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compared with it to which the cost of exploiting an extreme profit increases. In column

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3, we find that firms that are exposed more to the COVID-19 risk exhibit a more vari-

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ance risk premium (VRP): a one-standard-deviation decrease in the mobility increases

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the VRP by 0.0128, or 4% of the standard deviation. Results hold after considering firm

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and date fixed effects in Columns 4 to 9. The results indicate that a lower mobility trend

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increases the firm-level likelihood of left and more right tail events, and it also has some

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effect on firm-level VRP.

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S&P Global Market Intelligence (S&P Global hereafter) published the list of indus-

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tries that are most and least impacted by COVID-19 in terms of probability of default in

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January 2022.⁵ We test if the industries on the lists are most/least impacted by COVID-

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19 from a perspecitve of the cost of protection against downside tail risk. Specifically,

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we include dummy variables in the specification in Table 2, Most impacted/Least im-

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pacted, which equals one for the industries that are most/least impacted by COVID-19

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according to S&P Global, and zero otherwise. Columns 1, 3, and 5 of Table 3 interact the

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mobility trend with the most impacted industries and Columns 2, 4, and 6 with the least

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impacted ones. Excluding the most impacted industries weakens the explanatory power

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of the mobility trend on Slope reported in the first column of Table 2, which is -0.06 with

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a statistic of -1.90, as compared to -0.08 with a statistic of -4.95. During our sample

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period, the total effect of the mobility trend on Slope of the most impacted industries

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equals -0.18(=-0.12-0.06), which is also statistically significant. On the contrary, exclud-

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ing the least impacted industries does not weaken the explanatory power of the mobility

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trend, if not strengthen, with the coefficient -0.10 and a a statistic of –3.20. The total

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effect of the mobility trend on Slope of the least impacted industries is 0 (=0.10-0.10)

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and statistically significant. The same patterns can be observed in Columns 5 and 6 of

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the specifications for VRP. This result is consistent with the identification of the most

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and least impacted industries during COVID by S&P Global and supports Hypothesis

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2. It is worth noting that the results for RNS in Columns 3 and 4 of Table 3 contradict

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the above observation. Excluding the most/least impacted industries does not change

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the explanatory power of the mobility trend on RNS, and the total effect of the mobility

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trend on Slope of the most/least impacted industries is not significant. Combining with

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the results from Table 2, it is possible that the predictivity of the mobility on RNS can be

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5Top five most impacted industries are: Airlines, Hotels, Restaurants & Leisure, Energy Equipment & Services, Automobiles, and Specialty Retail. Top five least impacted industries are: Health Care Equipment & Supplies, REITs (Mortgage, Equity, Management Develop- ment), Life Sciences Tools & Services, Pharmaceuticals, and Communications Equipment. Re- trieved from: ‘https://www.spglobal.com/marketintelligence/en/news-insights/blog/industries-most-and- least-impacted-by-covid-19-from-a-probability-of-default-perspective-january-2022-update’ [Online Re- source]

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sourced from the correlation between the mobility and a latent variable that determines

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RNS.⁶

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3.2. Effect of the vaccine announcement: Event study results

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To test Hypothesis 3, we use the middle date of the vaccination announcements in August

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2020 as an event that reduced COVID-related uncertainty in the short term. Isolating

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exogenous variation in tail risk is hard when the government introduces various and

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significant efforts to stop the market downturn and stimulate the economy during the

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COVID-19 period.⁷ The policies and annoucements during COVID-19 are relatively un-

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expected as opposed to major policy shifts during normal times. The announcement of

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reaching the purchase deal of the vaccine should have lowered the cost of option protec-

218

tion for the U.S. firms especially the most impacted ones because of the COVID pandemic.

219

To quantify the effect of this event, we estimate a regression that includes a dummy vari-

220

able in the specification in Table 2 using daily option data around August 01, 2020. In

221

this regression, Post announcement equals one for all firm-day observations after August

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01, 2020, and zero for all firm-day observations before. We use Slope, RNS, and VRP as

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the proxy for right tail, right tail relative to left tail, and general risk that are revealed

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by the option prices, and employ a relatively wide event window of [180; +180] days as

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daily option measures for single names tend to be noisy and driven by idiosyncratic ef-

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6We conjecture that the government economics stimulas package can be one of the possibilities. When the pandemic gets severe, the goverment may implement a stricter containment policy including staying at home and travelling bans, which decreases the mobility trend, and in the meantime the industries on a wide extent receive the government package support. It has a positive effect on the investors confidence on the overall market and therefore a greater RNS, which is consistent with the evidence in Table 2 and Table 3.

7The Department of Defense (DOD) and HHS reach a deal with Pfizer BioNTech for the delivery and distribution of 100 million doses of the Pfizer BioNTech COVID-19 vaccine candidate in December 2020, upon confirmation that the vaccine is safe and effective July 22, 2020. The Trump Administration agrees to pay 1.5bill ion,or15 per-dose, to Moderna for 100 million doses of COVID-19 vaccine August 11, 2020.

These are the first events that can be accounted for as “good news” since the US became the most influenced country during the COVID-19 pandemic in March 2020. There have been more mixed news thereafter. We then use the middle day, August 01, 2020, as the event date.

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fects. For robustness, we exclude in some tests the [50; +50] days around the event day.

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We report results with fixed effects.

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Our test relies on the sharp expectation differences between before and after the an-

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nouncement of letting out the vaccine. Table 4 shows that the coefficients of the post

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announcement is negative for Slope during the event window of [180; +180] days, indi-

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cating that the option investors recognize the announcement as positive news that could

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reduce the downside tail risk. In economic terms, Column 1 implies that Slope of firms

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increased by 0.12 after the event, relative to before the event. Results are similar for

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VRP and during the event window of [180; +180] days excluding the window of the [50;

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+50] days in Columns 3, 4, and 6. In Column 2, the coefficient of the post announcement

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is positive for RNS during the event window of [180; +180] days, which is consistent

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with the result above that the event reduced the relative expensiveness of protection of

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left tail risk to right tail risk. However, the significance disappeared after excluding the

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window of the [50; +50] days in Columns 5, indicating that the predictive power of the

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mobility trend data on RNS is no longer significant for the structured sample. It proves

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again that RNS does not directly capture the cost of protection against downside tail

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risk.

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We perform several further tests and find some interesting results. To further quan-

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tify the effect of the event, we estimate a difference-in-differences (DiD) model using the

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same window as Table 4. We include one more dummy variable in this regression, that

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is, we use Most (Least) impacted, which equals one for the ten industries with the most

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(least) impacted industries, and zero otherwise in Panel A (B) of Table 5. After the an-

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nouncement, Slope and RNS of the firms that are not in the most impacted industries

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decreased and increased in all specifications, which is consistent with the results in Ta-

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ble 4 for all firms. However, VRP of the same set of firms after the event increased,

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indicating that the option investors expect more variance risk for those firms after the

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announcement. It can be explained by shifts of attention to risk, that is, a implicit trade-

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off between left tail risk versus general variance risk among the option investors. Panel

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A shows that firms in the most impacted industries or otherwise all have lower Slope,

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higher RNS, and higher VRP in general terms. Slope and VRP are impacted by the

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announcement around the event regardless the industries that firms are in are most

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impacted or not. Panel B shows similar results that Slope for the firms in the least im-

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pacted industries are generally smaller than those in other industries, consistent with

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Table 3, and such difference gets smaller after the announcement. Similar to Panel A,

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VRP are positively impacted by the announcement around the event regardless the in-

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dustries that firms are in are least impacted or not. Table 6 shows that Slope and RNS

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do not correspond to the daily confirmed cases significantly whereas VRP is determined

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by cases to a significant extent.

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4. Conclusion

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Policy implementations and signals have an impact on the COVID-19 risk. We show

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that COVID-19 uncertainty is priced in the option market in the cross section using the

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S&P 500 stocks. Specifically, the cost of option protection against downside tail risk is

268

larger when firms are located in the sub-state where the mobility trend is lower. We con-

269

firm our results using options of the firms that are in the industries most impacted by

270

COVID. Moreover, it significantly decreased at most impacted firms after the announce-

271

ment of the government purchase of the vaccine in August 2020, relative to other firms.

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Interestingly, we find that Slope is a better measure to capture the risk than RNS and

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VRP.

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References

275

Agarwalla, Sobhesh Kumar, Jayanth R. Varma, and Vineet Virmani, 2021, The impact

276

of COVID-19 on tail risk: Evidence from Nifty index options,Economics Letters 204,

277

109878.

278

Al-Awadhi, Abdullah M., Khaled Al-Saifi, Ahmad Al-Awadhi, and Salah Alhamadi, 2020,

279

Death and contagious infectious diseases: Impact of the COVID-19 virus on stock mar-

280

ket returns,Journal of Behavioral and Experimental Finance27, 100326.

281

Ali, Mohsin, Nafis Alam, and Syed Aun R. Rizvi, 2020, Coronavirus (COVID-19)—An

282

epidemic or pandemic for financial markets,Journal of Behavioral and Experimental

283

Finance27, 100341.

284

Ashraf, Badar Nadeem, 2020, Stock markets’ reaction to COVID-19: Cases or fatalities?

285

Research in International Business and Finance54, 101249.

286

Baek, Seungho, Sunil K. Mohanty, and Mina Glambosky, 2020, COVID-19 and stock

287

market volatility: An industry level analysis,Finance Research Letters37, 101748.

288

Baig, Ahmed S., Hassan Anjum Butt, Omair Haroon, and Syed Aun R. Rizvi, 2021,

289

Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic,

290

Finance Research Letters38, 101701.

291

Bakshi, Gurdip, Nikunj Kapadia, and Dilip Madan, 2003, Stock return characteristics,

292

skew laws, and the differential pricing of individual equity options,Review of Finan-

293

cial Studies16(1), 101–143.

294

Bollerslev, Tim, George Tauchen, and Hao Zhou, 2009, Expected stock returns and vari-

295

ance risk premia,Review of Financial Studies22(11), 4463–4492.

296

Carr, Peter, and Liuren Wu, 2009, Variance risk premiums,Review of Financial Studies

297

22(3), 1311–1341.

298

Haroon, Omair, and Syed Aun R. Rizvi, 2020, COVID-19: Media coverage and finan-

299

cial markets behavior—A sectoral inquiry, Journal of Behavioral and Experimental

300

Finance27, 100343.

301

Li, Jianhui, Xinfeng Ruan, Sebastian A. Gehricke, and Jin E. Zhang, 2021, The COVID-

302

19 risk in the Chinese option market,International Review of Finance(pp. 1–10).

303

Li, Jianhui, Xinfeng Ruan, and Jin E. Zhang, 2022, The price of COVID-19-induced

304

uncertainty in the options market,Economics Letters211, 110265.

305

Liu, Haiyue, Aqsa Manzoor, Cangyu Wang, Lei Zhang, and Zaira Manzoor, 2020, The

306

COVID-19 outbreak and affected countries stock markets response, International

307

Journal of Environmental Research and Public Health17(8), 2800.

308

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Zhang, Dayong, Min Hu, and Qiang Ji, 2020, Financial markets under the global pan-

309

demic of COVID-19,Finance Research Letters36, 101528.

310

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Table 1:Descriptive statistics

A: Statistics

Mean Std. Dev. Skew Min P25 Median P75 Max

Slope 0.46 0.34 3.01 −0.04 0.26 0.36 0.54 3.06

RNS −0.49 0.39 −0.40 −2.11 −0.72 −0.47 −0.25 1.03

VRP 0.03 0.16 2.14 −0.90 −0.03 0.03 0.09 1.05

MOB 4.84 0.24 −1.29 3.68 4.72 4.89 5.01 5.43

Cases 3.49 1.21 0.34 0.51 2.73 3.56 4.33 6.64

B: Correlations

RNS VRP MOB Cases

Slope -0.37 0.64 0.03 0.01

RNS -0.09 -0.03 0.03

VRP 0.02 0.04

MOB -0.14

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Table 2: Mobility trend and option measures during 2020 and 2021

Dependent variable: Slope RNS VRP Slope RNS VRP Slope RNS VRP

(1) (2) (3) (4) (5) (6) (7) (8) (9)

MOB -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 (-4.95) (-7.22) (-5.82) (-3.37) (-4.90) (-8.50) (-2.80) (-4.10) (-4.83) Assets -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00

(-40.87) (-2.03) (-18.44) (-3.18) (-0.35) (-3.05) (-3.18) (-0.34) (-3.05) Market Value -0.00 0.00 -0.00 -0.00 0.00 -0.00 -0.00 0.00 -0.00

(-29.51) (9.19) (-0.13) (-3.72) (2.06) (-0.12) (-3.70) (2.02) (-0.09) Dividends/net income 0.00 -0.00 0.00 0.00 -0.00 0.00 0.00 -0.00 0.00

(0.05) (-5.43) (3.75) (0.00) (-0.57) (0.64) (0.00) (-0.57) (0.65) Debt/assets 0.08 -0.01 0.03 0.08 -0.01 0.03 0.08 -0.01 0.03

(21.89) (-2.42) (9.61) (1.79) (-0.23) (1.96) (1.79) (-0.23) (1.97) EBIT/assets -0.15 -0.36 -0.15 -0.15 -0.36 -0.15 -0.15 -0.36 -0.15 (-9.86) (-17.96) (-12.46) (-1.30) (-2.45) (-2.96) (-1.30) (-2.44) (-3.03) CapEx/assets -0.55 1.34 -0.57 -0.55 1.34 -0.57 -0.55 1.34 -0.57

(-11.67) (28.55) (-2.88) (-1.67) (3.88) (-3.07) (-1.66) (3.87) (-2.16)

Book-to-market 0.27 0.04 0.01 0.27 0.04 0.01 0.27 0.04 0.01

(35.87) (3.95) (0.97) (4.88) (0.97) (0.60) (4.86) (0.95) (0.52) Return -0.01 -0.72 1.18 -0.01 -0.72 1.18 -0.01 -0.72 1.18

(-0.04) (-4.09) (1.79) (-0.25) (-14.50) (15.69) (-0.04) (-4.03) (1.79)

Constant 0.75 -0.13 0.44 0.75 -0.13 0.44 0.75 -0.13 0.44

(10.29) (-2.44) (6.67) (6.72) (-1.55) (9.27) (5.66) (-1.32) (5.45)

Day fixed effects Yes Yes Yes No No No Yes Yes Yes

Firm fixed effects No No No Yes Yes Yes Yes Yes Yes

Adj. R² 6.4% 1.7% 1.9% 6.4% 1.7% 1.9% 6.4% 1.7% 1.9%

(17)

Table 3: Mobility trend, most/least impacted industries, and option measures

Dependent variable: Slope Slope RNS RNS VRP VRP

(1) (2) (3) (4) (5) (6)

MOB×Most impacted -0.12 -0.05 -0.11

(-2.66) (-1.03) (-3.30)

MOB×Least impacted 0.10 -0.04 0.06

(2.35) (-1.07) (3.17)

Most impacted 0.53 0.35 0.50

(2.64) (1.45) (3.33)

Least impacted -0.50 0.26 -0.26

(-2.68) (1.49) (-3.15)

MOB -0.06 -0.10 -0.08 -0.07 -0.07 -0.10

(-1.90) (-3.20) (-3.78) (-3.23) (-4.19) (-4.98)

Controls Yes Yes Yes Yes Yes Yes

Day fixed effects Yes Yes Yes Yes Yes Yes

Firm fixed effects Yes Yes Yes Yes Yes Yes

Adj. R² 6.6% 6.8% 2.3% 2.1% 2.0% 1.9%

(18)

Table 4: Mobility trend and option measures after the announcement of the government purchase of the vaccine

Dependent variable: Slope RNS VRP Slope RNS VRP

[-180,+180] [-180,+180] [-180,+180] [-180,+180], [-180,+180], [-180,+180],

Event window: excl. [-50,+50] excl. [-50,+50] excl. [-50,+50]

(1) (2) (3) (4) (5) (6)

Post announcement× 0.12 -0.06 0.33 0.12 -0.03 0.54

MOB (2.82) (-1.76) (5.16) (2.32) (-0.58) (6.07)

MOB -0.19 0.02 -0.36 -0.20 0.03 -0.57

(-6.12) (0.82) (-5.55) (-5.89) (1.07) (-6.67)

Post announcement -0.63 0.33 -1.25 -0.66 0.17 -1.98

(-3.25) (1.98) (-5.25) (-2.76) (0.88) (-5.97)

Adj. R² 9.4% 3.3% 9.2% 10.8% 3.1% 17.4%

(19)

Table 5: Most/least impacted industries and option measures after the announcement of the government purchase of the vaccine

A

[-180,+180] [-180,+180] [-180,+180] [-180,+180], [-180,+180], [-180,+180],

(1) (2) (3) (4) (5) (6)

Post announcement× -0.03 0.01 0.08 -0.04 0.01 0.11

Most impacted (-1.25) (0.31) (1.99) (-1.25) (0.28) (2.07)

Most impacted 0.02 0.12 -0.04 0.03 0.12 -0.07

(0.89) (4.69) (-1.05) (1.11) (4.44) (-1.54)

Post announcement -0.12 0.04 0.15 -0.15 0.07 0.23

(-5.83) (2.54) (3.12) (-5.51) (2.94) (3.70)

Adj. R² 6.6% 4.6% 3.5% 7.6% 4.3% 5.6%

B

[-180,+180] [-180,+180] [-180,+180] [-180,+180], [-180,+180], [-180,+180],

(1) (2) (3) (4) (5) (6)

Post announcement× 0.05 0.01 -0.04 0.07 0.00 -0.06

Least impacted (2.40) (0.31) (-1.80) (2.65) (0.06) (-1.92)

Least impacted -0.10 0.05 0.02 -0.11 0.06 0.03

(-5.04) (2.30) (0.84) (-5.22) (2.43) (1.19)

(-6.23) (2.53) (3.01) (-5.98) (3.04) (3.51)

R-squared 7.3% 3.6% 3.4% 8.3% 3.4% 5.5%

(20)

Table 6: Cases and option measures after the announcement of the government purchase of the vaccine

[-180,+180] [-180,+180] [-180,+180] [-180,+180], [-180,+180], [-180,+180],

(1) (2) (3) (4) (5) (6)

Post announcement× -0.01 0.03 -0.38 -0.09 0.01 -0.45

COVID cases high (-0.41) (1.44) (-5.08) (-2.41) (0.33) (-5.10)

COVID cases high 0.00 -0.04 0.36 0.08 -0.08 0.44

(0.14) (-2.93) (4.84) (2.64) (-4.91) (5.00)

(-4.40) (1.65) (4.13) (-3.43) (4.16) (4.44)

Adj. R² 6.6% 3.4% 8.2% 7.9% 3.6% 10.4%