Original scientific paper
https://doi.org/10.1080/1331677X.2021.1910533
Causal estimation of COVID-19 and SARS on China’s stock market: evidence from a time series counterfactual prediction
Yun Feng
Xin Li
Abstract
This investigation infers the time evolution causal effect of
COVID-19 and SARS on China’s stock market based on predicting
the counterfactual market response using a diffusion-regression
state-space model. The results show that SARS caused an average
negative impact of 5.4% on stock prices. In comparison, COVID-19
had a negative impact of 5.3%. Furthermore, considering China’s
growing worldwide influence, this study carefully reselects the
covariates and finds that the negative impact of COVID-19 on
stock prices has conservatively increased to 10%, far stronger
than the impact of SARS. The results show that the quantitative
estimation of the causal effect of emergencies such as COVID-19
must be based on reliable counterfactual inference; only relying
on statistical correlation measures may lead to biased estimation.
The analysis of the time evolution characteristics of the causal
effect shows that the negative impact caused by COVID-19 began
to weaken within three days, while the impact of SARS lasted longer. The results show that the Chinese government’s strict lockdown achieved the effect of stopping losses in time, and this
finding helps to provide a positive demonstration for worldwide
epidemic response strategies.
Keywords
Stock market; COVID-19; SARS; causal inference; counterfactual predicting
Hrčak ID:
302233
URI
Publication date:
31.3.2023.
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