Highlights This paper analyzes the effects of COVID-19 on the U.S. stock market volatility at the industry level. The market switching AR model is used to identify regime change from lower volatility to higher volatility. Petroleum and natural gas, restaurants, hotels and lodgings industries exhibit large increases in risk. Machine learning (ML) feature selection methods are used to identify influential economic indicators. Changes in the volatility are found to be more sensitive to COVID-19 news than economic indicators.

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We estimate oil price risk exposures of the U.S. oil and gas sector using the FamaFrench-Carhart's four-factor asset pricing model augmented with oil price and interest rate factors. Results show that the market, book-to-market, and size factors, as well as momentum characteristics of stocks and changes in oil prices are significant determinants of returns for the sector. Oil price risk exposures of U.S. oil and gas companies in the oil and gas sector are generally positive and significant. Our study also finds that oil price risk exposures vary considerably over time, and across firms and industry subsectors.

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and the UK, countries that account for the vast majority of foreign bank activity in the US. Simultaneously, we look at the borrowing patterns of nonbank US affiliates of firms from those countries. We find that banks from four of the six countries (Japan, Canada, the Netherlands, and the UK) allocated a majority of their loans to non-home-country borrowers for some or all of the 1981-1992 period. These findings suggest that the "follow the customer" hypothesis may have a more limited applicability than previously supposed.

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