China-Europe freight transportation under the first wave of COVID-19 pandemic and government restriction measures

Yang, Yang; Liu, Qing; Chang, Chih-Hsin

doi:10.1016/j.retrec.2022.101251

Cited by 7 publications

(4 citation statements)

References 56 publications

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“…We implement the Wald statistic for the QLR test (Quandt 1960) as a modification of the Chow test (Chow 1960) on a dynamic linear model for the exogenous variable to locate the structural break date. The model is a common practice when the dates for the break are unknown (Éric Racicot and Théoret 2016;Seok et al 2023;Yang et al 2023). To reveal the unknown structural breaks, we exclude 15% of observations from each site in the time domain to avoid the short interval for the fitted models in the Chow test.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the resulting SB-GARCH-MIDAS model is useful in cases where the low-frequency variables exhibit a structural break. The models obtain the SB through the Quandt likelihood ratio (QLR) test (Quandt 1960), which is a modification of the Chow test (Chow 1960) and a common practice when the date of the break is not known (Éric Racicot and Théoret 2016;Seok et al 2023;Yang et al 2023). The monetary policy is assumed to follow an autoregressive process, as also done by Vidal-Tomás and Ibañez (2018).…”

Section: Introductionmentioning

confidence: 99%

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Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

Alam,

Amendola,

Candila

et al. 2024

Econometrics

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The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

Alam,

Amendola,

Candila

et al. 2024

Econometrics

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“…Impacted by the COVID‐19, the maritime industry experienced the crazed ascent of CFR in 2020. According to the existing study (Yang et al, 2023), the high infection rate caused by the epidemic has greatly reduced the human resources of the port in 2020. To correlate the weekly changes in the CFR with the huge differences in the population of the destination regions at the early stage of pandemic, we selected the maximum number of new infections per million people per week and the sum of new cases in each month as

new_{italiccases}_{italicijt}

in the destination and departure locations of each trade lane as the core explanatory variable, thereby measuring the severity of the epidemic.…”

Section: Methodsmentioning

confidence: 99%

An empirical study of the impact of COVID‐19 on container freight rates: Evidence from the panel data of China

Chen,

Ding,

Zou

et al. 2024

Transp. j.

Self Cite

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The container shipping industry has undergone significant changes in the post‐COVID‐19 era. This study examines the impact of the pandemic on container freight rates (CFR) through a demand analysis approach, using 11 major international container trade lanes from Shanghai, China as a case study. We analyze the CFR data on both weekly and monthly bases, with a focus on retaining more individual and time information using long panel data. To better reveal the pandemic's impact on CFR, we introduce interactive terms and simultaneous equations in our model. Our empirical analysis shows that the pandemic has not only directly and positively affected CFR, but, more importantly, has also indirectly promoted the growth of container shipping cost by stimulating the demand for containerized goods exported from China. Additionally, the connectivity or capacity of a given trade lane has inhibited the pandemic's effects on CFR to a certain extent. These findings provide insights into the remarkable growth of CFR from the demand side in 2020.

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“…Various studies [12,13] have highlighted the relationship between people's mobility and government measures adopted to control the spread of the COVID-19 pandemic. Mobility restrictions have been implemented differently among country [14][15][16][17]. Schlosser et al [18] stated that travel restriction reduced the incidence of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Impact of the Covid-19 Pandemic on Changing Communication Behavior of Students

Gorzelańczyk

Jurkovič

Szubert

et al. 2023

LOGI – Scientific Journal on Transport and Logistics

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The goal of this article is to assess the views of university students on their mobility in the context of the COVID-19 pandemic. To achieve this goal, a survey was conducted to analyze changes in student mobility patterns before and during the pandemic. The research was conducted in March 2021, during the pandemic, and for its purpose, information was collected on various aspects of student mobility, including the form of study, destination, mode of transportation, and shopping location. The sample group consisted of university students from different backgrounds, and various features, including age, gender, residence, and driver's license status, were considered. In addition, the study examined the impact of COVID-19 on student mobility. The study found that the pandemic has significantly affected student mobility, with a shift towards remote or hybrid learning and preference for cars, bicycles, and walking over public transportation in bigger towns while in smaller towns and rural areas, student mobility behavior has remained relatively unchanged.

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China-Europe freight transportation under the first wave of COVID-19 pandemic and government restriction measures

Cited by 7 publications

References 56 publications

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

An empirical study of the impact of COVID‐19 on container freight rates: Evidence from the panel data of China

Impact of the Covid-19 Pandemic on Changing Communication Behavior of Students

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