Chen Huan Shieh


This paper investigates the dynamic responses of stock return of container shipping companies to the global container freight indices during the Coronavirus pandemic period. The new econometric approach Dynamic Common Correlated Effects (DCCE) has been used to measure cointegrating relations among cross-sectional units. This procedure provides significant robust outcomes in the presence of cross-sectional dependence. A statistically significant and positive result has been observed between stock returns and container freight indices. The newly developed tests for a structural break were also implemented for our macro panel data. Our results are robust to structural break under different measures of container freight indices.

Keywords:  COVID-19, Stock return.

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Akhtaruzzaman, Md&Boubaker, Sabri&Sensoy, Ahmet. (2021). Financial contagion during COVID–19 crisis. Finance Research Letters. 38. 101604. 10.1016/j.frl.2020.101604.

Bai, JS & Ng, Serena. (2004). A PANIC attack on unit roots and cointegration. Econometrica: Journal of the Econometric Society. 72. 1127-1177.

Bai, B. Y. J., and P. Perron. 1998. Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica, 66(1): 47–78.

Baltagi, Badi&Pesaran, Hashem. (2007). Heterogeneity and cross-section dependence in panel data models: Theory and applications - Introduction. Journal of Applied Econometrics. 22. 229-232.

Barbieri, Laura. 2009. Panel Unit Root Tests under Cross-sectional Dependence: An Overview. Journal of Statistics: Advances in Theory and Applications. 1.

Breitung, Joerg (2015). The Analysis of Macroeconomic Panel Data. in The Oxford Handbook of Panel Data, edited by Badi H. Baltagi, chapter 15.

Breitung, J., and S. Das (2005). Panel unit root tests under cross-sectional dependence. StatisticaNeerlandica 59: 414-433.

Chudik, A., and M. H. Pesaran (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors.188(2): 393–420.

De Hoyos, Rafael &Sarafidis, Vasilis. (2006). Testing for Cross-Sectional Dependence in Panel-Data Models. Stata Journal. 6. 482-496.

Ditzen, Jan (2018). Estimating dynamic common-correlated effects in Stata. Stata Journal, StataCorp LP, vol. 18(3), pages 585-617, September.

Ditzen, J, Y. Karavias and J. Westerlund. (2021), xtbreak: Estimating and testing for structural breaks in Stata

Dobnik, Frauke, 2011.Energy Consumption and Economic Growth Revisited: Structural Breaks and Cross-section Dependence, Ruhr Economic Papers 303, RWI - Leibniz-InstitutfürWirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

Eberhardt, Markus. (2012). Estimating Panel Time-Series Models with Heterogeneous Slopes. Stata Journal. 12. 61-71.

Eberhardt, M., and F. Teal. (2010). Productivity analysis in global manufacturing production. Discussion Paper 515, Department of Economics, University of Oxford. http://www.economics.ox.ac.uk/research/WP/pdf/paper515.pdf

Fernandes, Nuno (2020), Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. IESE Business School Working Paper No. WP-1240-E, Available at https://ssrn.com/abstract=3557504or http://dx.doi.org/10.2139/ssrn.3557504

Frees, E. W. 1995. Assessing cross-sectional correlation in panel data. Journal of Econometrics 69: 393–414.

Friedman, M. 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32: 675–701.

International Transport Forum. (2020). COVID-19 and Transport; A Compendium. International Transport Forum, OECD

Kao, Chihwa& Chiang, Min-Hsien. (2000). On the estimation and inference of a cointegrated regression in panel data. In Advances in Econometrics: Vol. 15—Nonstationary Panels, Panel Cointegration, and Dynamic Panels, ed. B. H. Baltagi, 179–222. New York: Elsevier.

Kapetanios, George &Pesaran, Hashem & Yamagata, Takashi. (2006). Panels with Nonstationary Multifactor Error Structures. Journal of Econometrics. 160(2), pages 326-348.

Mazur, Mieszko& Dang, Man & Vega, Miguel. (2020). COVID-19 and the march 2020 stock market crash. Evidence from S&P1500. Finance Research Letters. 38. 101690. 10.1016/j.frl.2020.101690.

Moon, H.R.Hyungsik Roger &Perron, Benoit (2004). Testing for a unit root in panels with dynamic factors. Journal of Econometrics, Elsevier, vol. 122(1), pages 81-126.

Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967-1012.

Pesaran, M. H. (2007), A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics 22: 265-312.

Reese, Simon &Westerlund, Joakim. (2016). Panicca: Panic on Cross-Section Averages. Journal of Applied Econometrics. 31(6):961-981

Simon Grima&LetifeÖzdemir&ErcanÖzen& Inna Romānova, 2021. The Interactions between COVID-19 Cases in the USA, the VIX Index, and Major Stock Markets. International Journal of Financial Studies, MDPI, Open Access Journal, vol. 9(2), pages 1-19.

Szczygielski, Jan &Murefu, Princess &Charteris, Ailie&Brzeszczyński, Janusz. (2021). The only certainty is uncertainty: An analysis of the impact of COVID-19 uncertainty on regional stock markets. Finance Research Letters. 10.1016/j.frl.2021.101945.

UNCTAD. (2021) Container shipping in times of COVID-19: Why freight rates have surged and implications for policymakers - Policy Brief 84 No. (UNCTAD/PRESS/PB/2021/2)

Xu, Libo (2021), Stock Return and the COVID-19 pandemic: Evidence from Canada and the U.S. Finance Research Letters. 38. 101872. https://doi.org/10.1016/j.frl.2020.

Zhang, Xingping& Zhang, Haonan& Yuan, Jiahai. (2019). Economic growth, energy consumption, and carbon emission nexus: fresh evidence from developing countries. Environmental Science and Pollution Research. 26. 10.1007/s11356-019-05878-5.


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