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.
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.
