Exploring the Factors Influencing the Impact of the COVID-19 Pandemic on Global Shipping: A Case Study of the Baltic Dry Index

Abstract

The outbreak of COVID-19 in 2020 resulted in notable disruptions to global shipping and the global economy. As a key indicator influenced by supply and demand conditions in the shipping industry, the Baltic Dry Index (BDI) serves as an early economic indicator for global economic production. Contrary to expectations of decline, the BDI has exhibited a substantial increase. This research paper aims to investigate the impact of the COVID-19 pandemic on global shipping through a comprehensive analysis of the BDI. The study incorporates data spanning from 2019 to 2021, encompassing the pre- and post-pandemic periods. It examines 13 independent variables, including raw material prices (such as iron ore prices), international scrap steel prices, energy prices, stock market indexes, international commodity price volatility (as represented by the Commodity Research Bureau Index), global port calls, and confirmed COVID-19 cases. The primary objective is to explore the factors influencing the BDI and how they were affected by the pandemic. The study employs stepwise regression to select variables and build models before and after the pandemic. The findings of this study elucidate the prominent factors that influence the BDI in different temporal contexts. Before the outbreak, the BDI was notably impacted by variables, including the US Dollar Index (positive relationship), Brent, Port Calls, and CRB Index. However, a discernible shift in the relative significance of these factors has been observed in the post-pandemic period. Specifically, the US Dollar Index now exhibits a negative relationship with the BDI, whereas variables such as Port Calls, Iron Price, Steel Scrap Price, and confirmed COVID-19 cases had attained heightened prominence in shaping the dynamics of the freight index. These findings underscored the dynamic nature of the factors influencing the BDI, particularly in light of the unique circumstances brought about by the COVID-19 pandemic.

1. Introduction

Maritime transport is an essential foundation for global supply chains and economies. According to United Nations Conference on Trade and Development statistics, global shipping and ports accounted for more than 80% of the global merchandise trade volume and more than 70% of the transaction value in 2019 [1]. The price of marine transport varies with the trade volume. The BDI is a metric that reflects the worldwide shipping costs of major goods and materials through significant shipping routes. Its direct correlation with supply and demand conditions makes it an indicator of economic production [2].

COVID-19 exploded in 2020, severely disrupting international trade and causing inefficiencies, delays, and supply chain disruptions on an unprecedented scale [3,4]. Typically, such disruptive circumstances would lead to a downturn in freight activities. However, contrary to expectations, the freight index experienced a substantial increase over time as the epidemic continued to unfold. It is intriguing to investigate the reasons behind the initial decline of the freight index at the onset of the COVID-19 pandemic, followed by a sudden and explosive rise, as illustrated in Figure 1. This notable change has sparked curiosity and garnered the attention of our research inquiry. Hence, this research aims to investigate the key determinants influencing the BDI both prior to and following the outbreak of the COVID-19 epidemic. The findings reveal a shift in the factors impacting the BDI during the COVID-19 pandemic. Specifically, the US Dollar Index, Brent, Port Calls, and CRB Index exhibited greater influence before the epidemic. Conversely, variables such as the US Dollar Index, Port Calls, and Iron Price emerged as more significant factors after the epidemic.

The current manuscript is as follows. The second part of the paper constitutes the literature review, whereas the third part delineates the variables and methodology. This section elucidates the variable selection technique utilized in Stepwise Regression models. The fourth part entails the results section along with a corresponding discussion. Finally, the last section of the paper presents the study’s conclusion.

2. Literature Review

Global trade transportation includes air transport and marine transport. However, the majority of goods are transported via large container ships. The global container freight index is critical, mainly the Baltic Dry Index. BDI indicates shipping costs for transporting commodities, including coal, iron ore, and grain, through the sea. It plays a significant role in the economy by providing insights into the demand for these commodities, primarily in emerging economies, and the potential alterations in worldwide trade patterns. Consequently, this study examines which factors affect the BDI and how the COVID-19 pandemic influenced the BDI.

2.1. The Baltic Dry Index

Grammenos and Arkoulis [5] analyzed data from 1989 to 1998 to investigate the relationship between international maritime transport volumes and global economic risk indicators. Specifically, they examined the BDI, Brent, and US Dollar Index as potential relationship indicators. In this study, the authors established significant connections between the returns of international shipping stocks and global risk factors using a sample of 36 shipping companies listed on ten stock exchanges worldwide. The study’s findings revealed a negative relationship between shipping stocks and both oil prices and shipping stocks. At the same time, there was a positive relationship between the US Dollar Index. Utilizing data from 1989 to 2010, Alizadeh and Muradoglu [6] examined the BDI, West Texas Intermediate (WTI), and ten different stock markets (including petroleum, natural gas, and finance) to investigate the suitability of freight tariffs in explaining short and long-term stock returns. The study found that while long-term movements in freight prices were insufficient in explaining stock returns, short-term movements in freight prices could significantly impact stock returns. Furthermore, the authors observed that data from developed markets such as the United States, Europe, and China had a more pronounced effect on freight rates than data from emerging markets.

Andriosopoulos et al. [7] investigated the shipping stock and freight markets, utilizing data from 2006 to 2012. Specifically, the BDI, Baltic Dirty Tanker Index (BDTI), daily closing price, and the Dow Jones Industrial Average (DJIA) transportation index were employed, drawn from the stock markets of 95 maritime companies worldwide. The models developed for the indexes exhibited significant forecast errors attributable to the differences in risk profiles among the constituent firms of the transportation stock market index. By contrast, the DJIA model was observed to have a smaller error margin. Erdogan et al. [8] conducted a study utilizing daily and weekly data from 1999 to 2012 to examine the correlation between the Dow Jones Industrial Average and the BDI. The results indicated that the two markets exhibited mutual feedback during financial turmoil, strengthening their correlation. Furthermore, the extent of information spillover between the markets was found to vary. Over the long term, changes in the BDI were observed to offer insights into changes in the Dow Jones Industrial Average. These findings had significant implications for market participants seeking to mitigate risks and enhance their understanding of market behavior. As the BDI is a crucial indicator of global trade and economic activity, the paper’s findings may have important implications for more broadly understanding and predicting movements in the BDI and the dry bulk shipping market.

The BDI is a widely used measure of shipping costs for bulk commodities. Several studies have examined the relationship between the BDI and other economic indicators. Ruan et al. [4] analyzed the correlation between the BDI and crude oil prices using data from 1988 to 2015. They found that the interaction between the BDI and crude oil prices is significant in the short term but less in the long term. Papailias et al. [9] investigated the cyclical nature of the BDI and its impact on predictive performance using monthly data from 1993 to 2015. They found that the periodicity of BDI was best for 3–5 years, and variables such as Brent, coal prices, and steel prices had significant impacts on the BDI. Lin et al. [10] examined the spillover effects of the BDI on commodity futures, currency, and stock markets using data from 2007 to 2018. They found that the spillover effect of the BDI was time-varying and insignificant, except during the financial crisis of 2009 and the European debt crisis. Gong et al. [11] analyzed the impact of the China–United States trade war on the freight and stock markets using weekly data from 2002 to 2019. They found that the shipping and U.S. stock markets were more likely to crash together than to rise together, and the impact was higher during economic downturns. Additionally, the U.S. stock market had a more substantial influence on and was more sensitive to the shipping market than China.

The BDI is a metric that reflects the worldwide shipping costs of major goods and materials through significant shipping routes. Its direct correlation with supply and demand conditions makes it an indicator of economic production. BDI relates to other economic factors such as crude oil prices [12], global GDP, and stock market indexes. Yang et al. [13] used data from 1999 to 2019 to explore the risk assessment of the BDI, crude oil, and the stock market, finding that the maritime transportation market is the riskiest in the world. Siddiqui and Basu [14] analyzed the interrelationships between four major global tanker routes and found a growing relationship mainly driven by oil and fuel prices. Angelopoulos et al. [15] analyzed the economic relationship between 65 commodities and maritime transportation, finding a close economic relationship from commodity markets to freight markets. Michail [16] quantified the relationship between the world macroeconomic environment and maritime transport demand, finding that changes in the world’s economic environment affected oil demand differently across high-, middle-, and low-income countries. Bildirici et al. [17,18] focused on forecasting the BDI sea freight shipment cost. Bildirici et al. [18] employed LSTAR-GARCH and LSTAR-APGARCH models and examined the relationship between BDI and other variables such as VIX investor sentiment and MSCI global stock market indicator indexes. The research aimed to provide insights and forecasts regarding the BDI’s future movements and its association with these key variables. Finally, Tsioumas et al. [19] used maritime-related literature to construct a composite indicator to understand the relationship between economic conditions and maritime trade.

2.2. COVID-19

The COVID-19 pandemic significantly impacted the BDI, which measures global shipping rates. The closure of factories and reduced demand for goods have led to decreased shipping rates and lower BDI values. As a result, the maritime industry has been affected [3]. The impact of COVID-19 on the BDI has been significant and continues to be a concern for the industry. Ashraf [20] examined the correlation between the number of COVID-19 cases and the stock market performance, finding that stock market returns declined as the number of cases increased. Michail and Melas [21] analyzed the impact of COVID-19 on the maritime industry and found that it had a significant impact on the BDI and the Baltic Clean Tanker Index. Hasan et al. [22] explored the impact of COVID-19 on global economic activity, stock markets, and energy markets, finding that the stock market was more affected than the BDI, but COVID-19 influenced both. Shrestha et al. [23] developed the Pandemic Vulnerability Index to assess global mobility and economic impact, finding that some countries were more vulnerable than others. Chen and Yeh [8] analyzed the performance of the stock, raw material, and energy markets during the global financial crisis of 2008 and the COVID-19 pandemic, finding that quantitative easing policies significantly impacted market performance. Kitamura et al. [24] focused on exploring the impact of the COVID-19 pandemic on the economy, environment, and tourism industry. Xu et al. [25] examined the impact of COVID-19 on cargo throughput in 14 major ports in China and found that it significantly impacted imports and exports, with a greater impact on imports. Overall, these studies provided important insights into how the COVID-19 pandemic affected various aspects of the global economy.

3. Methodology

3.1. Variables

This study utilized a literature review to select the BDI and 13 associated indicators as the primary focus for defining and explaining the variables under examination. The BDI was employed as the dependent variable, whereas other selected indicators were used as independent variables in a prediction model to investigate the impact of the BDI. The dataset encompassed the time period from 3 February 2019 to 3 February 2021, with data collected on a daily basis. The variables related to this study are described as follows:

• Baltic Dry Index (BDI): The BDI, a composite of the Capesize, Panamax, and Supramax Timecharter Averages, is a shipping freight-cost index of dry bulk commodities issued daily by the London-based Baltic Exchange. It mainly transports staple raw materials and industrial raw materials such as steel, grain, coal, etc. There is an inextricable relationship between the BDI, the global economic outlook, and raw material prices, so the BDI is commonly perceived as a leading economic indicator [18,19,20].
• Brent: Brent is an international crude oil evaluation and observation system. It is considered a light, sweet crude oil (low-sulfur crude oil) and is used to measure the level of oil prices. Brent is the most used and referenced oil price figure.
• Standard and Poor’s 500 (S&P 500): The S&P 500 is one of the top 500 most traded stocks in the U.S. Compared to the Dow Jones Industrial Average (DJIA), The S&P 500 includes more companies, so it reflects a broader range of market changes and the fundamental importance of a company’s stock in the stock market [26].
• Volatility Index (VIX): The VIX is the ticker symbol for the Chicago Board Options Exchange’s CBOE Volatility Index, a popular measure of the stock market’s expectation of volatility based on S&P 500 index options [19].
• Shanghai Index: The Shanghai Index is a Capitalization-weighted Index that reflects statistical indicators of the overall trend in listed stocks on the Shanghai Stock Exchange. It is a basis for observing the China stock market and market boom.
• Bunker Index: The Bunker Index uses the average price of Bunker Index 180 CST and Bunker Index 380 CST published by Bunker Research. It is an average of bunker prices at ports worldwide, such as Singapore and other international commercial ports.
• Global Steel Price (Steel Price): Global steel transaction price is mainly provided by the Shanghai Futures Exchange (SHFE) and the London Metal Exchange (LME).
• Iron Price (Iron Price): London Metal Exchange (LME) mainly provides global iron ore transaction prices.
• Steel Scrap Price (Steel Scrap): London Metal Exchange (LME) mainly provides global steel scrap transaction prices.
• Commodity Research Bureau Index (CRB Index): The CRB Index was compiled by the U.S. Commodity Research Bureau and appeared in 1957. This particular commodity index encompasses a total of 19 commodities, with an allocation breakdown as follows: 39% is assigned to energy contracts, 41% to agriculture, 7% to precious metals, and 13% to industrial metals. The CRB Index is a critical reference indicator of international commodity price volatility.
• London Metal Exchange Index (LME Index): The LME Index is six metals from the London Metal Exchange, with the following weights: aluminum (42.8%), copper (31.2%), zinc (14.8%), lead (8.2%), nickel (2%), and tin (1%).
• U.S. Dollar Index: The U.S. Dollar Index measures the value of the U.S. dollar relative to a basket of foreign currencies, often referred to as a basket of U.S. trade partners’ currencies. It is a weighted geometric mean of the dollar’s value relative to six main currencies (Euro (EUR), Japanese yen (JPY), Pound sterling (GBP), Canadian dollar (CAD), Swedish krona (SEK), and Swiss franc (CHF)). Also, the indexes started in 1973 with a base of 100. It means that if the U.S. Dollar Index, the U.S. dollar is gaining value. As most of the significant international commodities are denominated in U.S. dollars, the rise and fall of the U.S. dollar is one of the indicators of the global economy and trade.
• Port Calls (Port Calls): The Global Port Calls is a global port index composed of 82 international ports worldwide, covering more than 60% of global port trade. It is an important indicator of global trade [11].
• COVID-19 global confirmed cases (Coronavirus): The collection of Coronavirus data in this study relies on the officially announced data provided by the World Health Organization (WHO). The WHO initiated the systematic recording of confirmed cases globally from 3rd February 2020 onward.
• The definitions of variables, data sources, and summary statistics are displayed in

  Table 1. Definition of variables and data sources.

  Variables	Measures	Unit	Frequency	Source
  Baltic Dry Index (BDI)	$y_{BDI}$	GBP	Daily	Baltic Exchange
  Brent	$x_{Brent}$	USD/TNK	Daily	ICEFE 1
  Standard and Poor’s 500	$x_{SP500}$	USD	Daily	NYSE 2
  Volatility Index (VIX)	$x_{VIX}$	USD	Daily	NYSE
  Shanghai Index	$x_{Shanghai}$	CNY	Daily	SSE 3
  Bunker Index	$x_{Bucker\_Index}$	USD/TNE	Daily	Bunker 4
  Steel Price	$x_{Steel\_Price}$	USD/TNE	Daily	SHFE 5, LME 6
  Iron Price	$x_{Iron\_Price}$	USD	Daily	LME
  Steel Scrap Price	$x_{Steel\_Scrap}$	USD/TNE	Daily	LME
  CRB Index	$x_{CRB}$	USD	Daily	NYMEX 7
  LME Index	$x_{LME}$	USD	Daily	LME
  US Dollar Index	$x_{US\_Dollar}$	Based period on 1973	Daily	NYCY 8
  Port Calls	$x_{Port\_Calls}$	Based period on 2008	Monthly	RWI/ISL 9
  COVID-19 Cases	$x_{Coronavirvus}$	Daily confirmed cases	Daily	WHO 10

  ¹ ICEFE is the abbreviation of the Intercontinental Exchange, Inc of Europe. ² NYSE is the abbreviation of the New York Stock Exchange. ³ SSE is the abbreviation of the Shanghai Stock Exchange. ⁴ Bunker is the abbreviation of Bunker Research. ⁵ SHFE is the abbreviation of the Shanghai Stock Exchange. ⁶ LME is the abbreviation of the London Metal Exchange. ⁷ NYMEX is the abbreviation of the New York Mercantile Exchange. ⁸ NYCY is the abbreviation of the New York Cotton Exchange. ⁹ RWI/ISL is the abbreviation of the Rheinisch-Westfälisches Institut für Wirtschaftsforschung/Institute of Shipping Economics and Logistics. ¹⁰ WHO is the abbreviation of the World Health Organization.

  ,

  Table 2. Summary statistics before COVID-19.

  Variables	Mean	SD	Maximum	Minimum	Skewness	Kurtosis
  BDI	1328.057	541.539	2518	576	0.3743474	1.942579
  Brent	64.17816	3.955042	74.57	54.91	0.4582497	2.704772
  Standard and Poor’s 500	2934.177	169.847	3329.62	2447.89	0.0234382	3.113209
  Volatility Index (VIX)	15.22576	2.633363	25.45	11.54	1.054102	3.853339
  Shanghai Index	4134.172	18,843.84	29,7871	2464.36	15.55455	242.9643
  Bunker Index	431.2061	35.674	497.5	364.5	−0.1590372	1.789265
  Steel Price	3780.494	191.129	4192	2760	−0.3101305	5.389448
  Iron Price	93.21204	11.38113	123.19	71.06	0.8880165	3.950634
  Steel Scrap Price	288.5123	25.40274	330	235	−0.5680389	2.573212
  CRB Index	179.3323	4.673222	189.66	167.89	−0.2639299	2.534157
  LME Index	2852.202	94.02627	3058	2717.8	0.8535983	2.59956
  US Dollar Index	97.14896	0.8823592	99.021	94.79	−0.3253769	2.661322
  Port Calls	115.909	4.534883	121.3	100.2	−2.183438	8.569458

  and

  Table 3. Summary statistics after COVID-19.

  Variables	Mean	SD	Maximum	Minimum	Skewness	Kurtosis
  BDI	1124.847	462.7662	1956	393	−0.1560041	1.655002
  Brent	42.95685	9.445393	61.47	19.33	−0.3685739	2.786709
  Standard and Poor’s 500	3272.699	367.6489	3915.59	2237.4	−0.4738171	2.779977
  Volatility Index (VIX)	30.13737	11.91423	82.69	13.68	2.015219	7.590668
  Shanghai Index	3172.149	263.7619	3655.09	2660.17	−0.1759481	1.649951
  Bunker Index	313.8511	48.5005	407	200	−0.208855	2.575449
  Steel Price	3736.702	277.8824	4546	3294	0.624765	2.564149
  Iron Price	114.1662	25.90873	170.03	78.33	0.6338015	2.486254
  Steel Scrap Price	301.9438	66.67363	483.5	48	1.111886	4.356229
  CRB Index	148.3337	17.35605	184.22	106.29	−0.0716374	2.427222
  LME Index	2867.957	377.9657	3643	2231.9	0.2701468	2.028614
  US Dollar Index	95.15678	3.618556	103.605	82.259	0.0314093	2.376818
  Port Calls	114.2779	8.434657	125.7	94.1	−0.888869	3.145588
  COVID-19 Cases	290,188.6	260,299	1,497,272	−530,881	0.5370899	4.071896

  , respectively.

3.2. Stationarity Check

Time series models are predicated on the assumption that the underlying data exhibit stationarity. Stationarity refers to the condition wherein the statistical characteristics of the data, including mean, variance, and covariance, remain constant over time. Hence, assessing the stationarity of time series variables assumes a vital role in constructing robust and meaningful models for the analysis of time series data. The utilization of stationarity tests, such as the Augmented Dickey–Fuller (ADF) test, furnishes statistical evidence for the validation or rejection of the stationarity hypothesis. These tests serve the purpose of identifying suitable transformations or differencing procedures required to achieve stationarity in the data. In the context of this study, the ADF test was employed to evaluate the stationarity of the variables under investigation.

Table 4. Augmented Dickey–Fuller (ADF) Unit Root Test before COVID-19.

Variables	Level		1st Difference
	t-Statistics	p-Value	t-Statistics	p-Value
BDI	−0.609	0.8689	−4.338	0.0004 ***
Brent	−3.974	0.0016 ***
Standard and Poor’s 500	−1.803	0.3788	−12.239	0.000 ***
Volatility Index (VIX)	−6.048	0.000 ***
Shanghai Index	−13.707	0.000 ***
Bunker Index	−1.121	0.7068	−18.774	0.000 ***
Steel Price	−4.166	0.0008 ***
Iron Price	−1.887	0.3384	−6.667	0.000 ***
Steel Scrap Price	−0.850	0.8041	−7.797	0.000 ***
CRB Index	−2.824	0.0550 *
LME Index	−1.026	0.7436	−12.245	0.000 ***
US Dollar Index	−2.635	0.0938 *
Port Calls	−2.569	0.0996 *

Unit root test results. Table 4 presents the unit root tests for all the variables (null hypothesis: there is a unit root). The reported numbers represent test statistics. ***, and * indicate the rejection of the null hypothesis at the 1% and 10% level of significance, respectively.

displays the outcomes of the ADF unit root tests conducted before the COVID-19 pandemic. Conversely,

Table 5. Augmented Dickey–Fuller (ADF) Unit Root Test after COVID-19.

Variables	Level		1st Difference
	t-Statistics	p-Value	t-Statistics	p-Value
BDI	−2.541	0.1058	−5.599	0.000 ***
Brent	−1.047	0.7356	−9.556	0.000 ***
Standard and Poor’s 500	−1.129	0.7033	−14.660	0.000 ***
Volatility Index (VIX)	−3.109	0.0259 **
Shanghai Index	−1.821	0.3703	−8.511	0.000 ***
Bunker Index	0.571	0.9869	−23.107	0.000 ***
Steel Price	−0.356	0.9172	−13.904	0.000 ***
Iron Price	0.967	0.9939	−11.886	0.000 ***
Steel Scrap Price	−3.792	0.0030 ***
CRB Index	−1.546	0.5109	−6.503	0.000 ***
LME Index	1.870	0.9985	−11.241	0.000 ***
US Dollar Index	−2.838	0.0936 *
Port Calls	−0.431	0.9047	−11.744	0.000 ***
COVID-19 Cases	−4.506	0.0002 ***

Unit root test results. Table 5 presents the unit root tests for all the variables (null hypothesis: there is a unit root). The reported numbers represent test statistics. ***, ** and * indicate the rejection of the null hypothesis at the 1%, 5% and 10% level of significance, respectively.

presents the results after the COVID-19 pandemic.

3.3. Variables Selection Method—Stepwise Regression

Stepwise regression is a method used in multiple regression to select independent variables for inclusion in the regression equation. This approach reduces computation time by adding or removing variables individually during each model iteration, enabling the best parameter evaluation for the regression model. The criterion for adding or removing variables is based on the F-statistic, which determines whether the $x_2$ of the added variable has a significant effect on the reduction in the error sum of squares. In this study, we utilize the stepwise regression model to identify the variables that affect BDI (before and after COVID-19) and select variables with significant effects for description. F-statistic:

$$F=\frac{\frac{SSE\left(x_1\right)-SSE(x_1,x_2)}{1}}{\frac{SSE(x_1,x_2)}{n-p-1}}$$

(1)

The F-statistic molecular degrees of freedom is the number of independent variables added to the model, and the denominator degrees of freedom is n − p − 1.

4. Results

4.1. Correlation Analysis

This study aimed to examine the correlation between the BDI and 13 related indicators, including Brent, Standard and Poor’s 500 (S&P 500), Volatility Index (VIX), Shanghai Index, Bunker Index, Global Steel Price, Global Iron Ore Price, Global Steel Scrap Price, Commodity Research Bureau Index (CRB Index), London Metal Exchange Index (LME Index), US Dollar Index, Global Port Calls, and COVID-19 global confirmed cases, before and after the COVID-19 outbreak. The dataset included daily observations from 3 February 2019 to 3 February 2021, totaling 732 data points, consisting of the BDI, 13 raw materials, energy, and stock market indicators. After eliminating non-matching missing data, the resulting time series comprised 453 observations. The present study examined the Pearson Product-Moment Correlation Coefficient (PPMCC) between the BDI and 13 related indicators. The results, as presented in

Table 6. Correlation coefficients before COVID-19.

Variables	BDI	Brent	S&P 500	VIX	Shanghai	Buke	STLPrice	IronPrice	STLScrap	CRB	LME	USDollar	PortCalls
BDI	1.000	−0.627 **	0.172 **	0.262 **	−0.265 **	−0.289 **	−0.135 *	0.302 **	−0.720 **	−0.668 **	−0.652 **	0.598 **	0.556 **
Brent		10.000	−0.160 *	−0.308 **	0.446 **	0.582 **	0.522 **	−0.022	0.552 **	0.807 **	0.627 **	−0.369 **	−0.180 **
S&P 500			10.000	−0.507 **	0.165 *	−0.615 **	−0.292 **	0.004	−0.383 **	0.175 **	−0.320 **	0.195 **	0.079
VIX				10.000	−0.377 **	0.066	−0.033	0.026	−0.040	−0.655 **	−0.357 **	0.225 **	0.202 **
Shanghai					10.000	0.230 **	0.154 **	−0.024	0.131 *	0.543 **	0.474 **	−0.270 **	0.126
Bunker						10.000	0.583 **	0.271 **	0.428 **	0.315 **	0.569 **	−0.288 **	0.011
STLPrice							10.000	0.490 **	0.474 **	0.259 **	0.239 **	−0.140 *	0.176 **
IronPrice								10.000	0.119	−0.114	−0.283 **	−0.181 **	0.492 **
STLScrap									10.000	0.478 **	0.585 **	−0.654 **	−0.350 **
CRB										10.000	0.695 **	−0.518 **	−0.369 **
LME											10.000	−0.508 **	−0.429 **
USDollar												10.000	0.312 **
PortCalls													10.000

** p < 0.01, * p < 0.05.

and

Table 7. Correlation coefficients after the COVID-19.

Variables	BDI	Brent	S&P 500	VIX	Shanghai	Bunker	STLPrice	IronPrice	STLScrap	CRB	LME	USDollar	PortCalls	COVID
BDI	1.000	0.369 **	0.604 **	−0.346 **	0.808 **	0.323 **	0.610 **	0.704 **	0.462 **	0.323 **	0.673 **	−0.708 **	0.787 **	0.550 **
Brent		10.000	0.798 **	−0.681 **	0.631 **	0.933 **	0.493 **	0.575 **	0.633 **	0.946 **	0.699 **	−0.610 **	0.105	0.428 **
S&P 500			10.000	−0.782 **	0.870 **	0.717 **	0.778 **	0.832 **	0.752 **	0.758 **	0.928 **	−0.877 **	0.515 **	0.744 **
VIX				10.000	−0.542 **	−0.480 **	−0.400 **	−0.397 **	−0.372 **	−0.562 **	−0.544 **	0.522 **	0.158 *	−0.379 **
Shanghai					10.000	0.589 **	0.803 **	0.881 **	0.727 **	0.634 **	0.924 **	−0.917 **	0.762 **	0.757 **
Bunker						10.000	0.458 **	0.564 **	0.649 **	0.951 **	0.684 **	−0.589 **	0.094	0.400 **
STLPrice							10.000	0.859 **	0.831 **	0.493 **	0.877 **	−0.831 **	0.636 **	0.839 **
IronPrice								10.000	0.905 **	0.605 **	0.935 **	−0.897 **	0.708 **	0.840 **
STLScrap									10.000	0.678 **	0.858 **	−0.760 **	0.448 **	0.770 **
CRB										10.000	0.734 **	−0.637 **	0.145 *	0.459 **
LME											10.000	−0.935 **	0.667 **	0.857 **
USDollar												10.000	−0.739 **	−0.793 **
PortCalls													10.000	0.672 **
COVID														10.000

** p < 0.01, * p < 0.05.

, indicated a negative correlation between the BDI and Brent, Shanghai Index, Bunker Index, Global Steel Price, Global Iron Ore Price, Global Steel Scrap Price, VIX, and LME Index. However, a positive correlation was observed between the BDI and US Dollar Index and Global Port Calls before the outbreak of COVID-19 in 2019.

Interestingly, after the outbreak of COVID-19 in 2020, the results showed a positive correlation between the BDI and Brent, S&P 500, Shanghai Index, Bunker Index, Global Steel Price, Global Iron Ore Price, Global Steel Scrap Price, VIX, LME Index, Global Port Calls, and COVID-19 global confirmed cases. Specifically, a highly positive correlation is observed between the BDI and Shanghai Index, Global Iron Ore Price, and Global Port Calls. Furthermore, the study found a highly positive correlation between the COVID-19 global confirmed cases and Shanghai Index, Global Steel Price, Global Iron Ore Price, Global Steel Scrap Price, LME Index, and Global Port Calls. These findings highlighted the significant impact of the pandemic on the shipping industry.

4.2. Results before COVID-19

4.2.1. Result of the BDI Stepwise Regression before COVID-19

Table 8. Result of the BDI stepwise regression before COVID-19.

Source	SS	df	MS	Number of obs = 188 F(9, 178) = 50.25 Prob > F = 0.000 R-squared = 0.7176 Adj R-squared = 0.7033 Root MSE = 296.99
Model	39,888,983.5	9	4,432,109.28
Residual	15,700,284.4	178	88,203.845
Total	55,589,267.9	187	297,268.812

presents the results of the BDI stepwise regression conducted in 2019, before the onset of the COVID-19 pandemic. The regression employed night independent variables, including the CRB Index, US Dollar Index, Port Calls, Brent, Bunker Index, LME Index, Steel Price, VIX, and SP 500. The significance level was α = 0.05. The F-statistic was 50.25. The p-value was 0.000 < α = 0.05, rejecting H₀, indicating that the relationship was significant, and the parameters of the variables were not zero. This meant the relationship between Y_BDI and the independent variables of the CRB Index, US Dollar Index, Port Calls, Brent, Bunker Index, LME Index, Steel Price, VIX, and SP 500. Therefore, the regression model for 2019 was a good fit, with the coefficient of determination R² = 0.7176 and R²_adjusted = 0.7033, indicating that the independent variables had good explanatory power for the dependent variables.

The complete result of the BDI stepwise regression model is shown in Table 8, and the estimated stepwise regression model was as follows

$$\begin{gathered} Y_{BDI}\hspace{0.17em}=\hspace{0.17em}-\mathrm{16,872.95}+\hspace{0.17em}220.8933·X_{U{S}_{Dollar}}- \hspace{0.17em}60.25977·X_{Brent}+\hspace{0.17em} 37.43986·X_{Por{t}_{Calls}}-33.36271 \\ ·X_{VIX}-24.3696·X_{CR{B}_{Index}}\hspace{0.17em}\hspace{0.17em} +9.572698·{∆X}_{Bunke{r}_{Index}} \hspace{0.17em}+3.528453·{∆X}_{LME} \\ -2.087742·{∆X}_{SP500}+\hspace{0.17em}0.3043951·X_{Steel\_Price}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em} \end{gathered}$$

(2)

When constructing a linear regression model, it is imperative to evaluate the presence of multicollinearity among the independent variables. Multicollinearity refers to the condition where the independent variables are highly correlated with each other. A widely adopted criterion for detecting multicollinearity is the Variance Inflation Factor (VIF), with a VIF value exceeding 10 typically considered indicative of multicollinearity.

In the current study, the VIF values of the independent variables, including CRB Index, US Dollar Index, Port Calls, Brent, Bunker Index, LME Index, Steel Price, VIX, and SP 500, were examined. It was found that all of these variables exhibited VIF values below 10, as presented in

Table 9. Model summary before COVID-19.

∆BDI	Estimation	SE	T-Value	p-Value	[95% Conf. Interval]		VIF
CRB Index	−24.3696	12.89839	−1.89	0.060	−49.82303	1.083832	7.51
US Dollar	220.8933	35.06297	6.3	0.000	151.7007	290.0859	4.83
Port Calls	37.43986	5.449355	6.87	0.000	26.6862	48.19351	2.53
Brent	−60.25977	12.36883	−4.87	0.000	−84.66818	−35.85136	2.03
∆Bunker Index	9.572698	3.061649	3.13	0.002	3.530899	15.6145	1.67
∆LME Index	3.528453	1.178964	2.99	0.003	1.201909	5.854997	1.36
Steel Price	0.3043951	0.1461573	2.08	0.039	0.015971	0.5928192	1.32
VIX	−33.36271	13.99597	−2.38	0.018	−60.98209	−5.743338	1.31
∆SP 500	−2.087742	1.204358	−1.73	0.085	−4.4644	0.288915	1.03
Intercept	−16872.95	5051.762	−3.34	0.001	−26842	−6903.895	2.62

The symbol “∆” represents the first difference of the variables.

. This outcome demonstrated the absence of any collinearity issues among the variables in the 2019 BDI forecast model. In summary, the analysis confirmed that there was no evidence of multicollinearity among the independent variables utilized in the constructed BDI forecast model of 2019.

4.2.2. Test for Autocorrelation

Autocorrelation refers to the correlation between the residuals of a regression model at different time lags, indicating a departure from the assumption of independent and identically distributed errors. To assess the presence of autocorrelation, the Breusch–Godfrey LM test was employed.

Table 10. Breusch–Godfrey serial correlation LM test.

lags(p)	chi2	df	Prob > chi2
1	2.806	1	0.0939
H0: no serial correlation

presents the results of the Breusch–Godfrey Serial Correlation LM Test, where it is observed that the test accepts the null hypothesis of no serial correlation. This provides evidence suggesting the absence of significant autocorrelation in the model.

4.2.3. Heteroscedasticity Test

The outcomes of the heteroscedasticity test are presented in

Table 11. Breusch–Pagan/Cook–Weisberg test for heteroskedasticity.

Breusch–Pagan/Cook–Weisberg Test for Heteroskedasticity
Assumption: Normal error terms
Variable: Fitted values of BDI
chi2(1) = 1.70
Prob > chi2 = 0.1924

, which showcases a Chi-square coefficient of 1.70 and a corresponding p-value of 0.1924, surpassing the significance level of 0.05. Consequently, the absence of statistical significance leads to the acceptance of the null hypothesis, thereby indicating a constant variance. As a result, it can be concluded that there is no presence of heteroscedasticity in the model.

4.3. Results after COVID-19

4.3.1. Result of the BDI Stepwise Regression after COVID-19

Table 12. Result of the BDI stepwise regression after COVID-19.

Source	SS	df	MS	Number of obs = 174 F(5, 168) = 44.12 Prob > F = 0.000 R-squared = 0.5677 Adj R-squared = 0.5548 Root MSE = 311.37
Model	21,386,889.1	5	4,277,377.82
Residual	16,287,463.9	168	96,949.1901
Total	37,674,353	173	217,770.827

displays the results of the stepwise regression analysis for the BDI in 2020. The model includes nine independent variables: US Dollar Index, Iron Price, Steel Scrap Price, Port Calls, and COVID-19, entering the model in that order. The model had a significant level of 0.05 (α = 0.05), and the F-statistic was 44.12 > F_0.05 = 2.796, rejecting H₀. To put it another way, the relationship between Y_BDI and the independent variables of raw materials, energy, and stock market indicators was significant. The p-value was 0.000 < α = 0.05, rejecting H₀, which meant that the relationship was significant, and the parameters of the variables were not zero. The coefficients of determination for the regression model were calculated as R² = 0.5677 and R²_adjusted = 0.5548, signifying a moderate to substantial level of goodness of fit.

Table 11 shows the complete results of the BDI stepwise regression model, with the following equation predicting the BDI:

$$\begin{gathered} ∆Y_{BDI}\hspace{0.17em}=\mathrm{10,593.28}\hspace{0.17em}-95.4527·x_{U{S}_{Dollar}}\hspace{0.17em}+78.76864·{∆X}_{Globa{l_{Calls}}_{}}-36.935·∆X_{Iro{n}_{Price}}\hspace{0.17em}-\hspace{0.17em}1.58·X_{Stee{l}_{Scrap}} \\ +0.000359·X_{Coronavirus} \end{gathered}$$

(3)

The VIF value of this model is shown in

Table 13. Model summary before COVID-19.

∆BDI	Estimation	SE	T-Value	p-Value	[95% Conf. Interval]		VIF
US Dollar	−95.4527	11.32449	−8.43	0.000	−117.809	−73.0961	3.05
∆Iron Price	−36.935	16.96914	−2.18	0.031	−70.4352	−3.43477	1.05
∆Port Calls	78.76864	33.53117	2.35	0.020	12.5719	144.9654	1.03
Steel Scrap Price	−1.58	0.629486	−2.51	0.013	−2.82272	−0.33728	2.84
COVID-19	0.000359	0.000164	2.19	0.030	3.57 × 10−5	0.000683	3.06
Intercept	10,593.28	1187.047	8.92	0	8249.826	12,936.73

The symbol “∆” represents the first difference of the variables.

. The VIF values of the independent variables in the present study, namely, the US Dollar Index, Iron Price, Steel Scrap Price, Port Calls, and COVID-19, were found to be 3.05, 1.05, 2.84, 1.03, and 3.06, respectively. Notably, all of these VIF values were below the threshold of 10, which suggested a relatively low degree of multicollinearity among the predictor variables. This implied that the variables exhibited a limited level of intercorrelation, thereby indicating that they contributed unique information to the regression model without significant redundancy.

4.3.2. Test for Autocorrelation

According to the results presented in

Table 14. Breusch-Godfrey serial correlation LM test.

lags(p)	chi2	df	Prob > chi2
1	9.621	1	0.0654
H0: no serial correlation

, the Breusch–Godfrey LM test for serial correlation yielded a probability value of 0.65, which exceeded the predetermined significance level of 5%. Consequently, based on the findings reported in Table 14, we accepted the null hypothesis, indicating the absence of serial correlation.

4.3.3. Heteroscedasticity Test

The Breusch–Pagan/Cook–Weisberg test was employed to assess heteroscedasticity, as presented in

Table 15. Breusch–Pagan/Cook–Weisberg test for heteroskedasticity.

Breusch–Pagan/Cook–Weisberg Test for Heteroskedasticity
Assumption: Normal error terms
Variable: Fitted values of BDI
chi2(1) = 0.11
Prob > chi2 = 0.7442

. The null hypothesis was the constant variance that was not rejected because the p-value was 0.7442 > 0.05. So, the variance was constant, and there was no heteroskedasticity in the model.

5. Discussion

5.1. Discussion of Findings

The emergence of the COVID-19 pandemic in 2020 unleashed a wave of disruptions that reverberated throughout the global economy. In conventional circumstances, such disruptive events would be expected to induce a downturn in freight activities. Paradoxically, however, the freight index exhibited a notable increase over time as the pandemic unfolded. This anomalous trend warranted further investigation into the underlying factors contributing to the initial decline of the freight index at the outset of the COVID-19 pandemic, followed by its subsequent rapid and substantial ascent.

Based on our research findings, before the onset of the COVID-19 outbreak in 2019, the freight index demonstrated susceptibility to the influence of nine discernible variables. In descending order of significance, these variables encompassed the US Dollar Index, Brent, Port Calls, CRB Index, VIX, Bunker Index, LME Index, SP 500 index, and Steel Price. A comprehensive synthesis of these influential factors revealed that the US dollar, oil prices, the level of maritime trade and transportation, and international commodity prices assumed the role of key drivers exerting influence on the freight index.

After the outbreak of the COVID-19 pandemic in 2020, the global shipping industry underwent a profound metamorphosis in its role within the international maritime transportation system. The implementation of extensive port closures and restrictive measures designed to mitigate the spread of the virus engendered significant disruptions in global maritime logistics operations, resulting in container strandings and a marked escalation in shipping prices. In this context, the freight index underwent a reconfiguration. It was influenced by five variables, arranged in the following order of importance: the US Dollar Index, Port Calls, Iron Price, Steel Scrap Price, and COVID-19 global confirmed cases. Among these variables, the US Dollar Index and maritime trade and transportation level, measured explicitly by global port calls, emerged as the primary determinants shaping the freight index. Furthermore, the price of iron and Steel Scrap Price assumed augmented significance in their impact on the index. These observed dynamics underscored the criticality of the fluctuations in the US Dollar Index, the level of maritime trade and transportation, and the volatility in iron prices as formative factors molding the freight index during the COVID-19 pandemic.

5.2. Academic Implications

The research paper has several academic implications:

• Understanding the Impact of COVID-19 on Global Shipping: The paper aims to investigate the impact of the COVID-19 pandemic on global shipping by analyzing the BDI as an economic indicator. This contributes to the academic understanding of how global shipping has been affected by the pandemic and provides insights into the dynamics of the shipping industry during such crises.
• Identification of Factors Influencing the BDI: The study examines various independent variables, including Brent, Standard and Poor’s 500, Volatility Index (VIX), Shanghai Index, Bunker Index, Steel Price, Iron Price, Steel Scrap Price, CRB Index, LME Index, US Dollar Index, Port Calls, and COVID-19 Cases, to understand their influences on the BDI. By employing stepwise regression analysis, the research identifies the key factors that shape the BDI in different temporal contexts. This contributes to the academic knowledge on the determinants of the BDI and its relationship with global economic factors.
• Comparative Analysis of Pre- and Post-Pandemic Influences on the BDI: The present study illuminates the alterations in the relative significance of factors influencing the BDI before and after the advent of the COVID-19 outbreak. It reveals a discernible shift in the salience of variables, such as the US Dollar Index, which transitions from a positive association with the BDI to a negative one. Furthermore, the oil price indicators (Brent and Bunker index) and the CRB index exhibit a transition in importance toward the iron price determinants (Iron Price and Steel Scrap Price). These insights provide valuable observations about the evolving dynamics within the shipping industry and the repercussions of the pandemic on these dynamic characteristics.
• Dynamic Nature of BDI Factors: The study underscores the dynamic nature of the factors influencing the BDI, especially in the context of the COVID-19 pandemic. It reveals that certain variables exhibited varying degrees of significance in different temporal contexts, indicating the adaptability and responsiveness of the shipping industry to changing circumstances.

5.3. Practical Implications

The practical implications of this research guide decision-making, economic analysis, risk management, and industry collaboration within the shipping sector as follows:

• Decision-Making for Shipping Industry Professionals: The research provides insights into the factors influencing the BDI in different temporal contexts, including pre- and post-pandemic periods. Professionals in the shipping industry, such as shipping companies, port authorities, and logistics managers, can utilize these findings to make informed decisions. They can consider the shifting significance of variables like the US Dollar Index, Port Calls, Iron Price, Brent, Port Calls, and CRB Index to adjust their strategies, optimize operations, and mitigate risks.
• Economic Analysis and Forecasting: The research establishes the BDI as an early economic indicator for global economic production, influenced by supply and demand conditions in the shipping industry. Economists, analysts, and financial institutions can utilize this knowledge to incorporate the BDI into their economic analyses and forecasting models. The BDI can serve as an additional tool for assessing the health and performance of the global economy, especially during periods of economic disruptions, such as the COVID-19 pandemic.
• Risk Management and Adaptability: The study reveals the dynamic nature of the factors influencing the BDI and the shipping industry as a whole. This understanding can assist stakeholders in risk management and adaptability planning. Shipping companies, investors, and other relevant entities can consider the identified variables and their changing significances to develop strategies that enable them to navigate uncertain times effectively and respond to evolving market conditions.
• Industry Collaboration and Resilience: The research highlights the challenges faced by the shipping industry during the COVID-19 pandemic, including disruptions in logistics operations and surging shipping prices. These insights can foster collaboration among industry stakeholders to address common issues and enhance the resilience of the global shipping ecosystem. By working together and leveraging the knowledge gained from this research, industry players can identify opportunities for innovation, optimize supply chains, and ensure the smooth flow of goods and commodities.

6. Conclusions and Limitations

6.1. Conclusions

In conclusion, this study reveals notable shifts in the factors influencing the BDI during the COVID-19 pandemic. Before the outbreak, variables such as the US Dollar Index (positive relationship), Brent, Port Calls, CRB Index, Bunker Index, LME Index, Steel Price, VIX, and SP 500 significantly influenced the BDI. However, a discernible change in the relative importance of these variables has been observed in the post-pandemic period. Specifically, the US Dollar Index now exhibits a negative relationship with the BDI, whereas variables such as Port Calls, Iron Price, Steel Scrap Price, and confirmed COVID-19 cases have significantly increased influence. These findings suggest that the dynamics shaping the BDI have evolved in response to the unique circumstances brought about by the COVID-19 pandemic. This research paper contributes to the academic understanding of the COVID-19 pandemic’s impact on global shipping and the factors influencing the BDI. The study offers valuable insights for decision-making and economic analysis in the shipping industry, facilitating informed decision-making, economic analyses, and the formulation of effective policies.

6.2. Limitations

The study employed a stepwise regression approach using the BDI and COVID-19 data to conduct an in-depth analysis aiming to comprehend the reasons behind the maritime transport sector’s earnings rebound amidst the widespread downturn experienced by most industries during the epidemic. However, it is essential to acknowledge the limitations of this study. Firstly, the research scope was confined to the specific regression model employed, thereby warranting further exploration of alternative prediction models, such as neural networks, artificial intelligence, or data mining techniques, to enhance the accuracy of freight index forecasts. Additionally, the dynamic nature of the economy, akin to the unpredictable movements of a walrus, entails diverse external factors, including natural disasters, conflicts, strategic decisions, and environmental considerations that influence index changes. Consequently, incorporating multiple techniques is imperative to improve the predictive precision of the target index.