67 © 2021 by the authors; licensee Asian Online Journal Publishing Group Asian Journal of Economics and Empirical Research Vol. 8, No. 2, 67-72, 2021 ISSN(E) 2409-2622 / ISSN(P) 2518-010X DOI: 10.20448/journal.501.2021.82.67.72 © 2021 by the authors; licensee Asian Online Journal Publishing Group Examining the Impact of Movements of the Commodity Price on the Value of the Baltic Dry Index during the COVID-19 Pandemic Nikola Radivojevic1 Almir Muhovic2 Milica Josimovic3 Miroslav Pimic4 ( Corresponding Author) 1Academy at Applied Studies Sumadia, Kraguevac, Serbia. Email: radivojevic034@gmail.com Tel: +381600555173 2Institute for Vegetable Crops, Smederevska Palanka, Serbia. 3University of Novi Sad, Technical Faculty “Mihajlo Pupin”, Zrenjanin, Serbia. 4Valjevo City Government, Valjevo, Serbia. Abstract The Baltic Dry Index (BDI) is one of the most well-known indexes, as it is perceived to be a leading indicator of economic activity. Reductions in the movement of people, commodities, and capital during economic crises, such as the financial crisis of 2008 and 2009, as well as the current economic crisis generated by the COVID-19 pandemic, were affected by the reduction of economic activities. The paper aims to examine whether the changes in these raw materials affect the changes in the value of the BDI. For these purposes, the generalized method of moments (GMM) and 2SLS estimators are used. The results show that different raw materials have different impacts on the value of the BDI, which indicates that the individual movements of the value of raw materials which composes the BDI cannot forecast its movement. Keywords: The Baltic Dry Index, COVID-19 pandemic, Economic crises, Commodities, GMM and 2SLS estimators. JEL Classification: C01, C22, C24, G01 Citation | Nikola Radivojevic; Almir Muhovic; Milica Josimovic; Miroslav Pimic (2021). Examining the Impact of Movements of the Commodity Price on the Value of the Baltic Dry Index during the COVID-19 Pandemic. Asian Journal of Economics and Empirical Research, 8(2): 67-72. History: Received: 19 July 2021 Revised: 17 August 2021 Accepted: 6 September 2021 Published: 22 September 2021 Licensed: This work is licensed under a Creative Commons Attribution 3.0 License Publisher: Asian Online Journal Publishing Group Acknowledgement: All authors contributed to the conception and design of the study. Funding: This study received no specific financial support. Competing Interests: The authors declare that they have no conflict of interests. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study followed all ethical practices during writing. Contents 1. Introduction ...................................................................................................................................................................................... 68 2. Literature Review ............................................................................................................................................................................ 68 3. Data and Methodology ................................................................................................................................................................... 69 4. Empirical Analysis and Discussion of Results ........................................................................................................................... 70 5. Conclusion ......................................................................................................................................................................................... 71 References .............................................................................................................................................................................................. 71 mailto:radivojevic034@gmail.com http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ https://www.doi.org/10.20448/journal.501.2021.82.67.72 https://orcid.org/0000-0002-6137-4431 https://orcid.org/0000-0003-1479-2654 https://orcid.org/0000-0003-2180-0237 https://orcid.org/0000-0002-7848-440X https://www.doi.org/10.20448/journal.501.2021.82.67.72 https://orcid.org/0000-0002-6137-4431 https://orcid.org/0000-0003-1479-2654 https://orcid.org/0000-0003-2180-0237 https://orcid.org/0000-0002-7848-440X https://www.doi.org/10.20448/journal.501.2021.82.67.72 https://orcid.org/0000-0002-6137-4431 https://orcid.org/0000-0003-1479-2654 https://orcid.org/0000-0003-2180-0237 https://orcid.org/0000-0002-7848-440X https://www.doi.org/10.20448/journal.501.2021.82.67.72 https://orcid.org/0000-0002-6137-4431 https://orcid.org/0000-0003-1479-2654 https://orcid.org/0000-0003-2180-0237 https://orcid.org/0000-0002-7848-440X https://www.doi.org/10.20448/journal.501.2021.82.67.72 https://orcid.org/0000-0002-6137-4431 https://orcid.org/0000-0003-1479-2654 https://orcid.org/0000-0003-2180-0237 https://orcid.org/0000-0002-7848-440X https://www.doi.org/10.20448/journal.501.2021.82.67.72 https://orcid.org/0000-0002-6137-4431 https://orcid.org/0000-0003-1479-2654 https://orcid.org/0000-0003-2180-0237 https://orcid.org/0000-0002-7848-440X Asian Journal of Economics and Empirical Research, 2021, 8(2): 67-72 68 © 2021 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature This study contributes to the existing literature by revealing that different raw materials have different impacts on the value of the BDI, which indicates that, based on individual movements, the value of raw materials which composes the BDI cannot forecast its movement. 1. Introduction The Baltic Dry Index (BDI) is one of the most well-known indexes, and it is perceived to be a leading indicator of economic activity. The reason for this lies in the fact that it reflects changes in supply and demand for imported raw materials used in manufacturing. The prevailing view in professional and academic circles is that changes in values of this index are a good indicator of future global economic activities (Faqin & Sim, 2013). Although, it should be noted that the BDI is not the only dry bulk index available; it derives its popularity from the fact that is the most comprehensive index (for details see Precision Trading Systems, 2013). Additionally, it derives its popularity from the fact that a real-time indicator is difficult to manipulate, since it is driven by clear forces of supply and demand (Hassan, Sanchez, & Yu, 2011). The BDI is a list of daily weighted average freight prices of shipping raw materials for use in production processes across the globe. It incorporates aspects of future economic activity and thus has the characteristics of a leading economic indicator (Papailias, Thomakos, & Liu, 2017). More precisely, the BDI is a benchmark for the price of transporting major raw materials by sea. In other words, it is an index that measures changes in the cost of transporting various raw materials, which are transported by sea. The creator of the index, the Baltic Stock Exchange, which is based in London, describes it as the index of average prices paid for the transport of dry bulk materials for 26 different shipping routes carrying coal, iron ore, grains, and many other commodities. It consists of three sub-indices – Capesize, Panamax and Supramax – and they measure different sizes of dry bulk carriers or merchant ships. Given the above, it is not surprising that this index is often referred to as a shipping and trade index. The basic characteristic of the index is reflected in high volatility, which many studies have testified. There are several reasons for this. The first is that the supply of large carriers by sea is quite small with long lead times and high production costs, while on the other hand, the demand for the transport of raw materials is determined by the level of economic activity, which is ultimately determined by the price of raw materials and their supply and demand. Hence, the volatility of the raw material market also affects the volatility of this index. Reductions in the movement of people, goods and capital under economic crisis conditions were affected by the reduction in economic activities. It is worth pointing out that the analysis of the basic trend of the BDI movements in the period before the economic crisis shows that the index fell to near record lows just before the credit crisis hit stocks full force (see UNCTAD (2009)). This is a clear signal that the index can be used as a tool for stock market forecasting. The decrease in economic activity affects the increase in price volatility, which is the main characteristic of commodity markets. The reasons for commodity price volatility differ by commodity and may change over the course of time. But, in general, low short-term elasticities of supply and demand cause any shock to production or consumption to translate into significant price fluctuations (Mayer, 2010). The outbreak of COVID- 19 has been accompanied by widespread declines in global commodity prices (Bank, 2020). In such conditions, it is expected that these changes affect the decrease in the value of the BDI. In order to examine whether the changes in these raw materials affect the values of the BDI and to what extent, the paper analyzes the impact of changes in the value of basic raw materials on the change in the value of the BDI. In other words, the aim is to examine the relationship between the BDI and the major raw materials, whose freight prices enter the calculation of the BDI. The paper is structured as follows: The first part of the paper provides the introduction to the study; the second part gives an overview of the most significant and relevant empirical research and presents the results of previous research in this field; the third part of the paper provides a description of the analyzed data and the methodology used; in the fourth section, the results are presented, analyzed and discussed; and the final part of the paper summarizes the findings and outlines the conclusions. 2. Literature Review There is an abundance of literature on the BDI that can be classified into two groups. The first group consists of papers that focus on the development of more reliable procedures and models to predict changes in freight rates. The development of various models is caused by fact that risk and uncertainty in the shipping market have increased dramatically. Interestingly, maritime freight rates fell by 50% on average, while global trade increased by 400% from 1870 to 1913 (Jacks & Pendakur, 2010). Driehuis (1970) was the first to design a good model for forecasting freight rates. Similar attempts were made by Marlow & Gardner (1980), and Beenstock & Vergottis (1989a) and Beenstock & Vergottis (1989b). Cullinane, Mason, & Cape (1999) applied the Box–Jenkins approach to forecast the movements of the Baltic Freight Index (BFI). More recently, Makridakis, Merikas, Merika, Tsionas, & Izzeldin (2020) presented a new model to predict changes in freight rates and apply it to the BDI. The findings they reported show that the new model was very successful in forecasting the BDI movement. Thalassinos, Hanias, Curtis, & Thalassinos (2013) used the false nearest neighbors (FNN) method to forecast the BDI, and Tsioumas, Papadimitriou, Smirlis, & Zahran (2017) used a multivariate vector autoregressive model with exogenous variables (VARX). Zeng, Qu, Ng, & Zhao (2016) developed a new forecasting approach in literature known as empirical mode decomposition (EMD). This approach is based on artificial neural networks (ANN). Geman & Smith (2012) investigated the BDI and suggested several diffusion models that are able to capture the unique features of its trajectories, such as large swings and high volatility. The second group includes papers that examine the relationship between the BDI and various microeconomic and macroeconomic indicators and commodities. Tsioumas & Papadimitriou (2015) investigated the lead–lag relationship between China’s steel output and various Baltic Exchange indices and concluded that there is a significant causality effect of Chinese steel production on the dry bulk freight market. Similar results were Asian Journal of Economics and Empirical Research, 2021, 8(2): 67-72 69 © 2021 by the authors; licensee Asian Online Journal Publishing Group presented by Tsioumas & Papadimitriou (2016). Their results provide evidence in favor of an existing significant causality between certain commodity prices and the freight rates of bulk carriers. A significant part of these papers focuses on testing the hypothesis that the BDI functions as a signal that promptly responds to crisis effects. These papers focus on analyzing the BDI as a supply and demand signal for the stock market. Faqin & Sim (2013) examined the link between the BDI as a proxy for trade and income improvements for the 48 least developed countries. Since trade is endogenous in the determination of income levels, they used the BDI as a proxy for trade and developed a new measure of trade cost as an external source of variation in trade, which, in turn, is used to construct the within-country estimate of the causal effect that trade has on the income of the least developed countries. They found that a reduction in the BDI has a positive effect on the income of least developed countries through the trade channel; a 1% expansion in trade raises the GDP per capita by approximately 0.5% on average. This estimate is much larger than what was previously found in the literature and its quantitative significance emphasizes the importance of trade for the economic development of low income countries (Faqin & Sim, 2013). Papailias et al. (2017) found that variations in the BDI are strongly associated with fluctuations in commodity markets, such as coal, steel, iron, corn, and wheat markets. They also showed that it is possible, by applying trigonometric regression, to improve forecasting in the BDI movements, and thus movements of commodity markets. Similar studies were conducted by Adland & Cullinane (2005); Koekebakker, Adland, & Sødal (2006) and Batchelor, Alizadeh, & Visvikis (2007). They studied the BDI series as a whole rather than analyzing the spot or forward rates separately. Lin & Sim (2013) examined the relationship between the BDI and trade in Sub-Saharan countries, and they also investigated the impact of the BDI on the transitory negative income shocks. They found that there is a strong relationship between the BDI and trade and the impact of the BDI on the transitory negative income shocks. The seasonal properties and forecasting in the dry bulk shipping sector were the subject of research conducted by Cullinane et al. (1999); Kavussanos & Alizadeh-M (2001) and Kavussanos & Alizadeh-M (2002). The results of these studies imply that a considerable proportion of the BDI variations can be predicted by a combination of explanatory factors and the cyclical pattern that exists in the series. Kavussanos & Nomikos (1999) and Kavussanos & Visvikis (2004) studied the relationship between freight futures and spot prices using the VAR and VEC models. Kavussanos & Visvikis (2004) and Kavussanos, Visvikis, & Menachof (2004) utilized a cointegration analysis to examine the predictability of the forward freight agreements (FFA) in the Panamax freight market. Jurun, Ratković, & Moro (2015) examined the relationship between the BDI as the key indicator of economic and business activities, and the business results of shipping companies. They studied the relationship between the BDI and performance excellence of the representative shipping companies. The results of their study show that there is a strong relationship between the average annual BDI values as an indicator of the cyclical nature of the maritime market and results of the representative shipping companies. Jurun et al. (2015) used the adjusted Altman Z-score to measure business results. The conclusion of their study is that the BDI serves as a good signal for the buying or selling of certain shares. In other words, it provides a reliable basis for making decisions. They came to this conclusion based on the discovery a high correlation exists between short-term (quarterly) average BDI values and a company’s business excellence. All of the abovementioned papers suggest possible endogeneity problems. 3. Data and Methodology As already mentioned, the aim of the paper is to examine the relationship between the BDI and major raw materials, such as corn, coal crude oil, iron ore, soybeans, copper, tin, wheat, aluminum, zinc, nickel, gold, rice and lead, whose freight prices enter the BDI. The raw materials were selected from the aforementioned empirical studies that confirm the link between the BDI and the major raw materials. Data were collected from the official Federal Reserve Bank of St. Louis and Bloomberg websites for the period between November 1999 and September 2020. For purpose of this study, monthly data was used. This study used a multiple linear regression model on a set of explanatory variables as mentioned above. The econometric model is expressed below, with the BDI as the dependent variable. (1) Where BDI is the Baltic Dry Index; Cor is corn price; Coa is coal price; Cru is crude oil price; Iro is iron ore; Soy represents soybeans; Lea is lead, Cop represents copper, Tin represents tin; Whe represents wheat, Alu is aluminum, Zin is zinc, Nil is nickel, Gol represents gold, and Ric represents rice; is the model error. According to Wooldridge (2003) and Radivojevic & Jovovic (2017), the ordinary least squares (OLS) model represents the most efficient estimator. However, it is true only if all the assumptions on which it is based are met. Otherwise, it will generate biased/unbiased and consistent/inconsistent estimates (some of these combinations) depending on which assumptions are not met. From the literature review on this subject, it was observed that researchers have concluded that this topic might run the risk of endogeneity. The problem of the possible endogeneity of one or more independent variables may be solved using the two-stage least squares (2SLS) method but the instrumental variables must not be weak. For that reason, the 2SLS method has been employed in this study. Mladenovic & Pavlovic (2003) warned that the 2SLS usually generates biased and consistent estimates. In addition, we have used the generalized method of moments (GMM). Unlike other estimators, the main advantage of the GMM is that it can be used even when the assumptions of other estimators are not satisfied. Generally speaking, the GMM can be viewed as a generalization of many other methods, and as a result, it is less likely to be misspecified (Chaussé, 2010). The GMM generates correct standard errors and p-values, provided that the specified moment conditions are valid. It is based on the simple idea that the estimations of parameters are done by solving a set of moment conditions. For the purpose of this study, a one-step1 IV-GMM was used. Since the GMM depends only on moment conditions, it is a reliable estimation procedure for many models in economics and 1 Of the available two-step and one-step GMMs, the one-step GMM estimator was chosen as it tends to be less biased in smaller sample sizes (see Arellano & Bond (1991)). Asian Journal of Economics and Empirical Research, 2021, 8(2): 67-72 70 © 2021 by the authors; licensee Asian Online Journal Publishing Group finance, especially for models which suffer from endogeneity problems because it provides the efficient estimations of instrumental variables under “orthogonality conditions”, with the instrumental variables and the error term being orthogonal in the expectation sense (Radivojević et al., 2019). 4. Empirical Analysis and Discussion of Results Table 1 shows the results of the descriptive statistics of the data set. As can be seen from Table 1, the BDI ranges from 306.9 to 10843.65, which indicates a very high disparity between the minimum and maximum index values. The very high value of the standard deviation of the BDI testifies to a large fluctuation in the value of this index, and this a similar case for the values of all commodities. The excess kurtosis ranges from 4.84 in the case of the BDI index to -1.09 in the case of copper. This indicates that the BDI index has significant leptokurtosis. The skewness of all the commodities and the index is different from zero, which indicates that they have asymmetric distribution. Table 1. Descriptive statistics of selected variables. BDI Cor Coa Cru Iro Soy Cop Lea Mean 2268.77 164.72 73.21 60.62 67.44 336.30 5332.50 1621.46 Standard Deviation 2027.69 63.74 33.92 26.22 48.01 115.60 2353.37 753.00 Excess Kurtosis 4.84 0.13 0.02 -0.70 -0.61 -0.74 -1.09 -0.86 Skewness 2.14 0.89 0.52 0.43 0.63 0.30 -0.35 -0.23 Minimum 306.90 75.06 24.00 16.55 11.93 158.61 1377.38 412.61 Maximum 10843.65 333.00 195.19 133.88 187.18 622.91 9880.94 3722.61 No. obs. 251 251 251 251 251 251 251 251 Tin Whe Alu Zin Nic Gol Ric Mean 14726.08 184.20 1902.17 1944.61 15341.74 958.33 391.55 Standard Deviation 7022.54 64.56 414.70 798.11 7751.67 482.84 153.35 Excess Kurtosis -1.07 0.08 -0.07 -0.23 4.30 -1.31 1.04 Skewness -0.10 0.82 0.77 0.39 1.74 -0.06 0.69 Minimum 3698.37 90.44 1283.53 748.81 4830.78 260.75 162.10 Maximum 32347.69 403.81 3067.46 4381.45 51783.33 1971.17 1015.21 No. obs. 251 251 251 251 251 251 251 To identify a potential multicollinearity problem, the next step included an analysis of the matrix correlation; the results are presented in Table 2. Table 2. Matrix correlation. BDI Cor Coa Cru Iro Soy Cop Tin Whe Alu Zin Nic Gol Ric Lea BDI 1.00 Cor -0.03 1.00 Coa 0.18 0.76 1.00 Cru 0.29 0.80 0.79 1.00 Iro -0.23 0.82 0.71 0.70 1.00 Soy 0.01 0.93 0.77 0.81 0.80 1.00 Cop 0.15 0.80 0.81 0.85 0.79 0.78 1.00 Tin -0.09 0.83 0.85 0.75 0.86 0.87 0.88 1.00 Whe 0.27 0.86 0.71 0.84 0.64 0.85 0.78 0.72 1.00 Alu 0.55 0.46 0.63 0.69 0.33 0.38 0.76 0.48 0.57 1.00 Zin 0.15 0.35 0.49 0.45 0.37 0.34 0.76 0.58 0.35 0.73 1.00 Nic 0.58 0.40 0.40 0.58 0.30 0.33 0.66 0.37 0.52 0.84 0.64 1.00 Gol -0.31 0.78 0.67 0.56 0.86 0.79 0.78 0.88 0.59 0.26 0.51 0.19 1.00 Ric 0.13 0.79 0.81 0.75 0.73 0.81 0.75 0.76 0.72 0.48 0.35 0.35 0.72 1.00 Lea 0.17 0.70 0.78 0.72 0.70 0.76 0.90 0.88 0.71 0.62 0.75 0.55 0.78 0.69 1.00 As can be seen from Table 2, there is a strong correlation (above 0.800) between certain commodities. For this reason, eight variables were excluded from further analysis (Cor, Coa, Cru, Soy, Cop, Tin, Gol and Alu). As two different estimation methods are used, two different sets of results are illustrated in Table 3. The 2SLS results in Table 3 show that there is: 1) a positive and significant relationship between the BDI and Nic – every 1% increase in Nic causes an increase of 0.217% in the BDI; 2) a positive and significant correlation between Ric and the BDI – for every 1% increase in the value of rice, the NPL’s rate will rise by approximately 4.17%; 3) a positive and significant relationship between Lea and the BDI – every 1% rise in Lea leads to a 1.61% increase in its BDI value. These results are in line with those found by Papailias et al. (2017), Tsioumas & Papadimitriou (2015) and Tsioumas & Papadimitriou (2016). The results obtained from the 2SLS also show that there is: 1) a negative and significant relationship between Iro, as one of the main commodities for international trade, and the BDI index – for every rise of 1% in Iro, the BDI value will decrease by 35.95%; and 2) a negative and significant correlation between Zin and the BDI – every 1% increase in Zin will decrease the BDI by 1.501%. The one-step GMM method provided similar results: 1) there is a positive and significant relationship between the BDI and Nic – every increase in Nic of 1% causes an increase of 0.207% in the BDI; 2) a positive and significant correlation between Ric and the BDI – for every 1% increase in the value of rice, the NPL’s rate will rise by approximately 3.8%; 3) a positive and significant relationship between Lea and the BDI – every 1% rise in Lea corresponds to a 1.45% increase in the value of the BDI. Asian Journal of Economics and Empirical Research, 2021, 8(2): 67-72 71 © 2021 by the authors; licensee Asian Online Journal Publishing Group Table 3. 2SLS and one-step GMM method results. 2SLS Regressor Coefficient Std. Error t-ratio p-value Const 525.294 289.874 1.812 0.071 * Iro −35.952 2.378 −15.12 <0.0001 *** Whe −2.633 2.246 −1.173 0.242 Zin −1.501 0.179 −8.386 <0.0001 *** Nic 0.217 0.014 15.610 <0.0001 *** Ric 4.178 0.791 5.280 <0.0001 *** Lea 1.610 0.240 6.695 <0.0001 *** R-squared = 0.71 Adjusted R-squared = 0.70 Hausman test: Chi-square(1) = 4.41037; p-value = 0.035 Weak instrument test - F-statistic (1,243) = 1110.79 One-step GMM Regressor Coefficient Std. Error z-ratio p-value Const 300.320 381.020 0.790 0.431 Iro −36.24 3.550 -10.090 <0.0001 *** Whe 0.450 2.819 0.160 0.872 Zin -1.385 0.183 -7.560 <0.0001 *** Nic 0.207 0.021 9.570 <0.0001 *** Ric 3.800 1.344 2.830 <0.005 *** Lea 1.451 0.295 4.930 <0.0001 *** Note: ***, **, * indicate significance at the 1%, 5% and 10% levels, respectively. The results obtained from the one-step GMM show that there is: 1) a negative and significant relationship between Iro and the BDI index – for every rise of 1% in Iro, the value of the BDI will decrease by 36.24%; and 2) a negative and significant correlation between Zin and the BDI – every increase in Zin of 1% will decrease the BDI value by 1.385%. Both methods suggest that there is no significant relationship between Whe and the BDI index. 5. Conclusion This paper examined the relationship between the BDI and major raw materials, whose freight enters the calculation of the value of the BDI index. The aim of the paper was to examine whether the changes in the value of these raw materials affect the changes in the value of the BDI, and to what extent they affect it. For purpose of the study, a multiple linear regression model was used. To estimate the model parameters, the 2SLS and GMM estimators were used. The survey covers the period from the day the indexes were created to the present day. This period includes two major economic crises: the great economic crisis of 2008 and the current crisis caused by the Covid-19 pandemic. The findings of this research suggest that iron ore has a crucial deterministic role for the BDI, unveiling that the value of this raw material is oppositely linked to the value of the index. Also, the findings of this research suggest that there is a negative and significant correlation between zinc and the BDI. The results of the paper imply that there is a positive and significant relationship between the index and lead, nickel and rice. 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