Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Abstract

Dealing with structural breaks is an essential step in most empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy; however, its impact on stock markets is still unclear. Most markets seem to have recovered while the pandemic is ongoing, suggesting that the relationship between stock returns and COVID-19 has been subject to structural break. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. Motivated by this last observation, the present article develops a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a structural change test, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation is the quantitative easing programs announced by central banks all over the world in the second half of March.

Keywords:

• Change-point
• Common correlated effects
• COVID-19
• Cross-section dependence
• Panel data
• Structural change

Supplementary Materials

This supplement provides (a) the proofs of the results reported in Sections 3 and 4 of the main article, (ii) some additional theoretical results that are commented on but not reported in the main article, and (iii) a Monte Carlo study.

Funding

Westerlund would also like to thank the Knut and Alice Wallenberg Foundation for financial support through a Wallenberg Academy Fellowship.

Acknowledgments

We thank the Editor Professor Christian B. Hansen, an Associate Editor and three referees for helpful comments and suggestions. A previous version of this article was presented in seminars at the University of Duisburg-Essen and the Free University of Bozen-Bolzano, and at the 14th and 15th International Conferences on Computational and Financial Econometrics, the International Association for Applied Econometrics 2021 Annual Conference, the 26th International Panel Data Conference, the 2021 Latin American Meeting of the Econometric Society and the 2021 European Winter Meeting of the Econometric Society. The authors would like to thank all seminar and conference participants, and in particular Jan Ditzen, Christoph Hanck, Yannick Hoga, Sebastian Otten, Thilo Reinschlüssel and Martin Weidner for many valuable comments and suggestions.

Notes

1 See Chudik et al. (2011) for a detailed treatment of the concepts of weak and strong cross-section dependence.

2 Strictly speaking, the assumption that $e_{i,t}$ and $x_{i,t}$ load on the same set of factors is not necessary. Factors that are unique to either $e_{i,t}$ or $x_{i,t}$ can be accommodated by imposing zero restrictions on γ_i and Γ_i. This means that there might be factors in $e_{i,t}$ that are not captured by our CCE approach, and the theory provided here does not consider this possibility. However, this does not mean that our toolbox cannot handle unattended factors. In fact, intuition suggest that the approach should work well as long as there are no unattended factors in $x_{i,t}$, so that the regressors are conditionally exogenous given the factors, and our unreported Monte Carlo evidence supports this.

3 The two-sided Brownian motion B(v) satisfies $B(0)=0$, and $B(v)=B_1(v)$ for v > 0 and $B(v)=B_2(v)$ for v < 0, where $B_1(v)$ and $B_2(v)$ are two independent standard Brownian motions.

4 By comparison, the lowest global GDP growth rate during the 2007–2009 global financial crisis was –1.7% in 2009.

5 Not all news were about the economy and many were just rumors, but they still attracted considerable attention and were therefore important in setting the public sentiment at the time. For example, on February 17, a run on toilet paper in Hong Kong was mentioned for the first time, and became a highly contagious story. Some people in locked-down China reportedly were reduced to searching for minnows and ragworms to eat. In Italy, there were stories of medical workers in overwhelmed hospitals being forced to choose which patients would receive treatment (Shiller 2020).

6 Many studies focus on single countries. There are also those that focus on the volatility of stock returns, as opposed to stock returns themselves. These are not reviewed here.

7 We experimented using excess returns. However, because the results were qualitatively the same, and since the previous literature focuses almost exclusively on raw returns, here we only report the results based on using raw returns as the dependent variable.

8 While the COVID-19 variables are clearly exogenous, the controls are not. Because of this we tried lagging the controls, which reduces the risk of reversed causality. The results were, however, unaffected by this.

9 The included countries are Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Croatia, Cyprus, Czech Republic, Denmark, Egypt, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kenya, Kuwait, Latvia, Luxembourg, Malaysia, Mexico, Morocco, the Netherlands, New Zealand, Norway, Oman, Pakistan, Peru, the Philippines, Poland, Portugal, Romania, Russia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Sri Lanka, Sweden, Switzerland, Thailand, Tunisia, Turkey, and the United Kingdom.

10 After the break date was estimated, the Stata command xtdcce2 by Ditzen (2018) was used to obtain the regression results.

11 We also note that our estimated breakpoint does not coincide with the sample splits considered by Capelle-Blancard and Desroziers (2020), Mamaysky (2020), and Ramelli and Wagner (2020).

Additional information

Funding

Westerlund would also like to thank the Knut and Alice Wallenberg Foundation for financial support through a Wallenberg Academy Fellowship.