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How has COVID-19 impacted meat processors' stock returns? The authors evaluate the effects of supply chain disruptions (e.g. lockdowns and COVID-19 incidences among workers) on stock market prices of meat processors during the COVID-19 pandemic.

This study uses an event study approach to examine the disruptions from COVID-19 through events such as plant shutdowns, the pandemic announcement, lockdown dates and the first case of COVID-19 outbreaks in meat processing plants. The dataset includes S&P 500, Google Trends, financial beta and data collected for 14 US publicly traded meat processing companies.

Results show that nationwide events (e.g. announcement of the pandemic) had no statistically significant impact on average abnormal returns of meat processing companies. Individually, however, firms experienced negative abnormal returns. COVID-19-related events in individual meat processing companies had a temporary negative abnormal return in the days prior to the event.

This study has two main contributions. First, the authors estimate the effect of COVID-19 on the returns of meat processors. Second, the authors use Google Trends to estimate the expected stock markets returns of meat processing companies. This study provides insight to investors on the behavior of industry returns from events such as outbreaks that affect human health.

This paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.

Autoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.

There was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.

This volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.

To the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.