B&E | Understanding the Relationship between Baltic Dry Index and Economic Growth

B&E | Understanding the Relationship between Baltic Dry Index and Economic Growth

By Melike E. Bildiricia, Fazıl Kayıkçı and Işıl Şahin Onat

Since its establishment, the Baltic Dry Index has become one of the foremost indicators on the cost of shipping and an important barometer on the volume of worldwide trade and manufacturing activity. Global factors also play important role in supply and demand of BDI index. BDI and global markets have common economical and financial movement due to market supply and demand which is as a result of turmoil’s and crisis. After economic recessions and during economic growth, demand of raw materials increase as production and investments are also increase, as a result transportation volume grows accordingly. 


Image Attribute: Bloomberg Terminal Screenshot of BDI Index for IOE1 Commodity 
Jan 30, 2014 - Feb 02, 2015

The Baltic Exchange has a long history going back to 1744. In 1985, the Baltic Exchange developed the Baltic Dry Index (BDI) as a general indicator, consisting mainly of raw commodities such as grain, coal, iron ore, copper and other primary materials. Since its establishment, the BDI has become one of the foremost indicators on the cost of shipping and an important barometer on the volume of worldwide trade and manufacturing activity (Faqin Lina Nicholas C.S. Sim, 2013; 59:1-18. )

Investors are always looking for practical economic indicators that they can use to help them make informed investing decisions. Recently, Baltic Dry Index can be sources of economic indicator on a global scale. In addition to that the BDI depends on volatile of crude oil prices and port and docking fees which makes BDI to be sensitive for global demand and manufactured goods (Economic SYNOPSES, Federal Reserve Banks of St. Louis). Oomen (2012) mentioned that Baltic Dry Index (BDI) which is a source of measurement to determine cost of raw materials around the world such as iron, coal, cement, grain. Average of price of 23 different shipping routes around the world compiles daily to form the Baltic Dry Index. Economic indicators such as unemployment rate, inflation and oil prices that can be manipulated or influenced by governments and speculators, however, Baltic Dry Index is difficult to manipulate because it is driven by clear forces of supply and demand. One of the reasons for BDI to be difficult to manipulate and influence is number of ships around the world is limited with up to a certain extend therefore in order to manipulate and increase the supply, more ships need to be built which will be very costly.

After economic recessions and during economic growth, demand of raw materials increase as production and investments are also increase, as a result transportation volume grows accordingly. On the other hand, during economic slowdowns, demand of raw material decreases which creates utilized capacity. Global factors also play important role in supply and demand of BDI index. BDI and global markets have common economical and financial movement due to market supply and demand which is as a result of turmoil’s and crisis. Iron ore, coal, phosphate, grain and alumina are main goods of dry bulk transportation. These goods are mostly dynamics of construction and energy sector. Moreover, freight rate is determined by raw material demand as transportation need continues to remain the same.

The volatility of the bulk shipping market has gained wide attention, and much research regarding this volatility has been undertaken. In the past decades, econometric and statistical methods, such as VAR, GARCH and VECM models, have been widely used in shipping market analysis and forecasting. For example, Kavussanos and Alizadeh-M (2001) analysed seasonal volatility considering ship type, lease term, market environment, etc. Veenstra and Franses (1997) found that cointegration relations exist between several freight rate time series. Duru and Yoshida (2011) studied the lag and price elasticity of the bulk shipping market through the long-term freight index. The results indicate that the log-linear model is not a good method for bulk shipping market forecasting because of the spurious regression. Byoung-wook (2011) decomposed the bulk shipping market freight time series into a longterm trend component and a temporary particular component with a random model.

Bashi (2011) investigated the importance of the BDI growth rate as a predictor that stems from two findings. First, the BDI growth rate exhibits a positive and statistically significant relation to subsequent global stock returns, commodity returns, and industrial production growth. Second, the predictability is corroborated in statistical terms, in-sample and out-of-sample, as well as through metrics of economic significance, and in the presence of some alternative predictors. Movements in the BDI growth rate, thus, capture variation across the real and financial sectors, and the association appears stable across a multitude of economies. (Bashi et.all:2011)


Studies that focus on BDI have mostly used VAR-VECM models. However, MS-VAR model have been used in this study because of the nonlinear structure of the economic time series, especially GDP which has been used as the measure of economic performance fluctuates as the business cycles. In these models in perspectives of business cycles, the parameters are assumed to be constant over the sample period which means the relationship between GDP and BDI is stable. 

But the world has experienced many significant crises during the past decades. For this reason, the relationship between BDI and economic growth must be analyzed in perspectives of the business cycles because countries and the world experienced many significant crisis. If in time series analysis, phase of the business cycle must be taken into account, the estimated parameters would be incorrect and misleading. One way to overcome these problems is to divide the sample into sub-samples, based on the structural breaks; however, in most cases the exact date of these changes are not known and the researcher must determine it endogenously based on the data. But there is no guarantee that the relationship between GDP and BDI in the same date as the break dates of the variables itself (Falahi:2011;4165-4170; Bildirici:2012;179-205).

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About The Author:


Melike E. Bildiricia  , Fazıl Kayıkçı   , Işıl Şahin Onat ,Yıldız Technical University, Social Science Institute, Istanbul, 34349,Turkey.

Publication Details:

4th International Conference on Leadership, Technology, Innovation and Business Management Baltic Dry Index as a Major Economic Policy Indicator: The relationship with Economic Growth ISSN 1877-0428 / doi: 10.1016/j.sbspro.2015.11.389.

© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the International Conference on Leadership, Technology, Innovation and Business Management.

AIDN0020120160001 /INDRASTRA /ISSN 2381-3652
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