# **Table 1. Summary Statistics.**

_This table presents summary statistics for key variables used in our analyses. N designates the number of nonmissing observations for the variable. Our analyses include 43 U.S. oil and gas firms (see Appendix Table A1 for the list). Data are from Jan 2, 2020 to June 30, 2020. U.S. holidays are not included because the financial market was closed (Jan 20; Feb 17; April 10; May 25). April 20 and 21 are dropped because of extreme values in_ ∆ l _(oil) (WTI oil price was negative on April 20)._ ll

|Variable<br> <br>|N|Mean|Standard<br>Deviation|Min|Max|<br>Number of<br>firms|
|---|---|---|---|---|---|---|
|∆ ll (oil)<br>|122|-0.0012|0.0888|-0.2814|0.3747||
|<br>∆ ll(stock)|5,332|-0.0037|0.1072|-1.1418|2.4761|43|
|<br>ST Debt/Assets|43|0.009|0.018|0.000|0.098|43|
|<br>LT Debt/Assets|43|0.261|0.188|0.000|0.721|43|
|Total Debt/Assets|43|0.270|0.185|0.000|0.724|43|
|Cash/Assets|43|0.076|0.155|0.000|0.742|43|
|Beta|43|1.028|0.520|-0.645|2.088|43|
|Market-to-Book|42|0.992|1.113|-1.685|5.375|42|
|Log(Assets in USD million)|43|7.050|2.595|1.850|11.602|43|
|Profitability|43|0.014|0.070|-0.275|0.091|43|
|Momentum|43|0.856|0.271|0.301|1.396|43|

# **II.2 Empirical Framework**

An important challenge in our empirical analysis is identifying the _causal_ effects of oil prices on the stock prices of U.S. oil firms. Other macroeconomic factors such as shifts in aggregate demand or interest rate changes could simultaneously affect oil and stock prices. These omitted variables driving both oil and stock prices might bias our estimates if we were not to address the issue of endogeneity. In this section, we discuss our identification in more detail.

# **Identification through Heteroscedasticity**

To address the issue of identification, we use the heteroscedasticity-based identification strategy following Rigobon (2003). Let us consider the following system of equations: + β (1) tt tt tt tt (2) ∆pp = γγ∆ss + εε where tt is the change in oil prices, tt tt tt is the change in a firm’s stock prices, and is a set of common ∆ss = αα∆pp + δδββ + μμ aggregate demand factors that could affect oil prices and stock prices (such as interest rates, news about tt tt tt global growth, or other aggregate demand news). ∆pp ∆ss represents oil shocks that only affect oil prices. ββ captures events that affect current or future oil supply, such as an agreement for production cuts by the tt tt Organization of the Petroleum Exporting Countries (OPEC) and its allies. Similarly, εε represents the εε idiosyncratic shocks that only affect stock prices. Our goal is to estimate the value of : the causal impacts tt of changes in oil prices on changes in stock prices.<sup>11</sup> μμ αα We divide the days in our sample into two types: event ( _E_ ) and non-event ( _N_ ) days. Event days are days with important announcements or developments about the current or future oil supply (see Appendix Table A2 for our selected event days between January and June of 2020). A useful feature of the approach is that it does not require the complete absence of common aggregate demand or shocks

> 11 Note that in this framework, the effects of oil price increases and decreases are symmetric.

5