Uncertainty due to pandemics and epidemics and the behavior of Travel & Leisure stocks in the UK, the USA and Europe

Abstract

PurposeThe purpose of this paper is to examine the response of Travel & Leisure (T&L) stocks of some advanced economies (the USA and United Kingdom) as well as Europe to uncertainty due to pandemics and epidemics. The motivation for the study is derived from the expectation that pandemics and epidemics which are infectious would limit activities and events that require physical interactions such as those associated with T&L, and therefore, returns on related investments may decline during this period.Design/methodology/approachThe authors formulate a model in line with Westerlund and Narayan (2012, 2015) where uncertainty due to infectious diseases is included as a predictor in the valuation of T&L stocks while also controlling for endogeneity bias (for omitted variables bias), conditional heteroscedasticity effect (typical of high frequency data) and persistence (typical of most financial and economic time series).FindingsThe authors’ results suggest that contrary to the negative impact of previous cases of pandemics and epidemics on the T&L stocks, the behavior of these stocks during COVID-19 pandemic is modest owing to the positive nexus between equity market volatility due to infectious diseases (EMV-ID) (our proxy for pandemics and epidemics) and the T&L returns during the COVID-19 period. The authors maintain that investors in this market need not panic as the market tends to be resilient to pandemics over time albeit with a lower resilience during daily trading. The results leading to this conclusion are robust to alternative measures of the COVID-19 pandemic.Originality/valueThe peculiarity of this paper on T&L stocks is premised on the introduction of the new datasets for infectious diseases, and the need to include the COVID-19 pandemic given its peculiarity. Essentially, we utilize the Baker et al. (2020) dataset which captures all the pandemics including COVID-19 and a complementary dataset on the COVID-19 pandemic using an alternative approach.