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The Asian Steel Index (ASI) and Baltic Capsize Index (BCI)are important indices of the tramp shipping industry, where the BCI index reveals both the current tramp maritime transport fare and market status, while the ASI index is a composite index determined by the trade prices of steel. This paper first reviewed the literature of ASI and BCI indices to illustrate the current composition of the ASI index, as well as the BCI index in the present tramp maritime industry. A time-series analysis was then followed to construct the optimum model of ASI and BCI indices, and the results could be used as references for the maritime industry. According to the formulation of the multivariate time series models, the optimal mode was found to be VARMA (2,4), which meant that both the ASI and BCI indices would be affected by previous two series of data and have the error correction effect of 4 series. The results of this study confirm that ASI and BCI indices have impacts on each other, especially for the maritime zones of the BCI index, which are consistent with the status of China, a big country with high demands for raw steel material. Furthermore, this model allows the user to discover the impact multiplier of both indices. The construction of the model is hoped to help provide a reference for academics and businesses in the shipping industry.

Shipping businesses can be divided into liner shipping and tramp shipping. Tramp shipping mainly focuses on delivering bulk cargo that are primarily composed of consumer goods and raw industrial materials like iron ore and coal, with no fixed schedules or timetables. The market of bulk cargo tramp shipping industry is close to a perfect competition market [

The Baltic Freight Index (BFI), created in 1985, was the key bulk index used by the bulk cargo businesses to grasp the changes in freight rates for the bulk cargo market. It was, however, replaced by Baltic Dry Index (BDI) in 1999. Due to the differences in factors such as carrying capacity and characteristics of the maritime trade, the dry bulk shipping market is currently separated into four markets and each builds its own index, that is, Baltic Capsize Index (BCI), Baltic Panamax Index (BPI), Baltic Supermax Index (BSI) and Baltic Handy Size Index (BHSI). Each index represents different types of ships, including the market reference tariffs which are designated when signing for the shipping contracts with different routes. The economy of the bulk cargo shipping industry, there- fore, can be revealed from the changes in BDI [

Steel is the main raw material required for the construction and heavy industries, and demand rises during times of economic growth, both globally and in particular regions, as reflected by fluctuations in steel price [

The prices of coal and iron ore are highly dependent on their relationship of supply and demand. Due to the rising demand of the market, China became a great importer of iron ore and coal [

This study aims to explore the relationship between BCI index and ASI index through conducting empirical research using BCI index and ASI index as variables to determine the direction of causality, their roles in influencing each other and in which way, whether the relationship is one-way or two-way, and whether there is a long-term link between the two indices. The results from the study could be used for investors as a reference to improve the effect of hedging in times of fluctuating BCI and ASI indices, and benefit the investors in making the most favorable decisions.

The demand for steel has been growing since 2000 and had brought up the demand of other raw materials along with it, especially for iron ore, coal and scrap metal. The close relationship between BCI and steel price can be seen from the BCI index of the routes and cargo. The vessels in BCI market are 172,000 DWT, not over 10 years of age and 190,000 cbm grain maximum. They principally transport iron ores and coal with a capacity of 60%, and cannot pass through the Panama Canal. The trialed route on March 1^{st}, 1993 was formed by seven voyage charters and four time charters. Since the release of BCI in April 26^{th}, 1999, it has been amended and adjusted many times, and the existing routes and weighting of BCI freight index are shown in

Affected by the rapid recovery of the global economy, the demand for steel consumption surged. China’s crude steel output continued to grow and caused the steel factories in various regions of China to rapidly increase iron ore imports [

The global steel price index was lower than 80 points in 2001, and many businesses went bankrupt due to mismanagement. Some parts of the steel industry preferred to go through industrial restructuring and failed due to the poor market sentiment. With economic recovery, steel price has risen dramatically. The global steel price index had changed by 4.27 times from the lowest point of 68.5 on 11^{th} January 2002 to the highest point of 292.8 on 8^{th} August 2008. This change is especially clear in Asia [

Number | Ship Tonnages | Cargo/Time Charter | Shipping Route | Weights |
---|---|---|---|---|

C2 | 160,000 | Iron ore | Tubarao, Brazil, South America to Rotterdam, Netherlands, Europe | 10% |

C3 | 150,000 | Iron ore | Tubarao, Brazil, South America to Beilun, Ningbo, China/Baoshan, Shanghai, China | 15% |

C4 | 150,000 | Coal | Richard’s Bay, South Africa to Rotterdam, Netherlands, Europe | 5% |

C5 | 150,000 | Iron ore | West Australia to Beilun, Ningbo, China/Baoshan, Shanghai, China | 15% |

C7 | 150,000 | Coal | Bolivar, Columbia, South America to Rotterdam, Netherland, Europe | 5% |

C8 | 172,000 | Time charter | Hand over the ship within the area of Gibraltar, Europe; transatlantic round-trip routes; charting period around 30 - 45 days; return the ship within the area of Gibraltar. | 10% |

C9 | 172,000 | Time charter | Hand over the ship within the area of Antwerp, Rotterdam, Amsterdam, Europe (ARA) or when passing Passero; return the ship within the area of China/Japan; charting period around 65 days. | 5% |

C10 | 172,000 | Time charter | Hand over the ship within the area of China/Japan; transpacific round-trip route; charting period around 30 - 40 days; return the ship within the area of port where handed over. | 20% |

C11 | 172,000 | Time charter | Hand over the ship within the area of Antwerp, Rotterdam, Amsterdam, Europe (ARA) or when passing Passero; return the ship within the area of China/Japan; charting period around 65 days. | 5% |

C12 | 150,000 | Coal | Rotterdam, Netherlands, Europe to Gladstone, east coast of Australia | 10% |

Source: Baltic exchange (2011) [

The price of coal has also undergone tremendous growth since 2000 from US $47 per ton in 2003 to US $300 per ton in 2008 [

In 2003, China’s economic growth rate rose to 9%. To support this wave of infrastructural needs, China had to import more raw material and energy to satisfy these demands. This surge of demand in raw material continued until the 2008 Olympics, on top of further demands for iron ore and coal from the automotive and other industries have received heavy investments. The demand for iron ore and coal from China caused the ASI steel price index jumped up to 100 - 200 points from fluctuating below 100 points from after 2003. This meant that the steel market had been driven to a new market level by the rise of the Chinese economy, and that the price of steel in China had increasingly important influence on the global steel prices. Bin [

Broadly speaking, the Chinese steel price has greater influential power on the global steel price, thus allowing it to become a key factor for predicting the global steel price. The global steel price, however, has less significant influence on the Chinese steel price. This is due to the global steel price indices can be branched into North American steel price index, European steel price index and the Asian steel price index, meaning that there is only an one-way relationship between the two. As for the relationship between the Chinese steel price and the Taiwanese steel price, the study by Yau et al. [

Time series indicates a set of observations of the emergence of patterns in a chronological order, such as a continuous collection of data on seismic activity, rainfall, annual birth rate, and monthly sales of products [

Time-series analysis is mainly used to help the researchers understand the causal relationships between different variables in a system. Time-series studies generally try to assess and predict the relationships between indices. As for the indices, the trends in their movement could change depending on current affairs and the economic climate. For this study, the influential relationship between ASI and BCI index is the main focus of the research.

Considering the series through the use of the Autoregressive Moving Average Model (ARMA) (Box and Jenkins, 1970), the series would likely have stable and random behaviour, thus promoting it into the VARMA model. The univariate variable used in the ARMA

The vectors can be expressed as:

This time series can now be termed as K by K degree vectors.

Using the Baltic series univariate as

In the formula above,

B is the backward shift operator,

C is the constant.

The matrix of the VARMA model can be re-written as the following formula:

B is the matrix polynomial of the backward shift operator,

In addition, we need to assume that the root of the polynomial determinants

Assuming the final model is VARMA (1,1),

The parameters

The Vector Autoregressive Moving Average Model (VARMA) takes all the related series to construct a dynamic model and analyze the results. Because the main aim of the time-series analysis is to understand the causal relationships between the variables, adding another related variable could make up the lack of explanation power with the pervious singular variable. VARMA can gain more understanding of the relationships between series, such as series are correlated, or a particular series could cause other series to develop, or discover their feedback relationships. Combining the ASI and BCI indices in the model reveals more information about the related series, and allows more efficiency in building the dynamic relationship between ASI and BCI indices, as well as improving the accuracy of the predictions [

This study collected data of the two variables, BCI and ASI, on a weekly basis from July 2007 to December 2011, with a total of 495 datasets. From observing the standard deviation of the two series, the degree of differences of the two variables can be assessed. BCI had a larger degree of dispersion, while ASI was less dispersed. The variable with the greater dispersion has more intense return volatility in comparison. The comparison of

This article uses Augmented Dickey Fuller (ADF) unit root test to determine whether BCI and ASI are stationary or non-stationary series. Although many economy data such as product prices and currency supply are considered to be non-stationary series [

H_{0}: the non-stationary series has a unit root

H_{1}: the series is in stationary

Firstly, all the variables are made into their natural logarithm and the ADF unit root test is conducted.

For the VARMA, the determination of the model orders is the most complicated procedure. The graphics of SCAN (

The aspect of model estimation is done using the Maximum Likelihood Method (MLE), and the results of VARMA (2,4) of BCI and ASI matrix are shown below.

Variables | Intercept | Intercept and Trend |
---|---|---|

BCI | −2.61 | −2.52 |

ASI | −2.84 | −3.57 |

Note: The critical values of the ADF unit root test at 1%, 5% and 10% significant level are presented as ^{*}, ^{**} and ^{***}, respectively.

Variables | Intercept | Intercept and Trend |
---|---|---|

△BCI | −20.83^{*} | −20.83^{*} |

△ASI | −4.98^{*} | −4.99^{*} |

Note: The critical values of the ADF unit root test at 1%, 5% and 10% significant level are presented as ^{*}, ^{**} and ^{***}, respectively.

Q | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|---|

0 | X | O | O | X | X | X | O |

1 | X | O | O | O | O | X | O |

2 | X | O | O | O | O | O | O |

3 | X | O | O | O | O | O | O |

4 | X | X | X | X | O | O | O |

5 | O | O | O | O | O | O | O |

6 | O | O | O | O | O | O | O |

Note: The suitable orders of the model are shown as “O”, the unsuitable orders of the model are shown as “X”.

The expansion of the ASI and BCI index matrices are shown below:

The formula above demonstrates that there is a correlation between the ASI index and BCI index. It exhibits that the current ASI is affected by the data of its past two periods as well as the second period BCI index. By adding the error correction effect of 4, such as the level of significance of 1%, we are able to discriminate between ASI and BCI indices and discover that ASI is more affected by the BCI index than being affected by itself. The current BCI is affected by its second period data. In this paper, there is empirical proof that ASI and BCI do have influence over each other. Examining the expanded matrix of the BCI and ASI indices at the 1% significant level, it is clear that the BCI index leads the ASI index, thus the causal relationship shows that the BCI index is the cause and the ASI is the effect.

The VARMA model was used by this study to understand the structure of the causal relationship between the ASI index and the BCI index. The results could be used to construct BCI freight cost interpretation and explanation for maritime shipping businesses and the model can be eventually used to estimate future tariffs. Overall, the results of this study are as follows.

1) According the hypothesis stated for this study, the results of the structural formula for the ASI index and BCI index showed that the ASI index is two periods ahead the BCI index and is leading towards a long-term equilibrium, making the BCI index the leading indicators of the ASI index.

2) From the analysis of the VARMA model at the 1% significant level, the ASI index had no significant relationship with some of the BCI parameters. Therefore, the BCI index is only affected by the unilateral feedback relationship caused by its lagging second period.

3) The VARMA theory demonstrated that the BCI index is the leading indicator of the ASI. Therefore, for the shipping companies or related investors, this study recommends using the BCI index to predict and interpret future steel rates. It is hoped that the results of this study would be able to provide investors and liner shipping companies a grasp of the relative relationship between the ASI index and the BCI index. Furthermore, when the liner shipping market is affected by seasonal fluctuations or poor market conditions, the BCI index could be used to predict the conditions of the shipping costs to develop investment strategies and avoid potential risks for shipping companies.

Whether the focus is on the theory or in practice, the use of the ASI index and BCI index for interpreting the freight costs is an important guide worthy of attention for the maritime shipping industry. The study also faced several limitations during the research process, but working with appropriate assumptions, the VARMA model could still be operated and produce useful reference values of the BCI index for the shipping companies. The limitations and difficulties faced by the study during its operation, as well as its corresponding solutions, are summarized as follows.

a) In reality, the composition of the shipping freight is not limited to the ASI index and the BCI index, but this study had only set ASI index and BIC index as the reference indices for the research. Other influential elements, such as oil prices, shipping company unions, and economic crisis could also influence the shipping freights. Presently, Asia is still the biggest importer of iron ores. With factors such as China being the second biggest economy in the world, the reference value of ASI would greatly increase in the future.

b) The data type used in this study for correlation analysis and estimations using the VARMA model has not considered the effects of seasonality. In order to overcome this problem, future research may consider the changes brought by seasonality, and the inclusion of this factor could improve the accuracy and rationality of the VARMA model.

c) Existing literature on the ASI index and the BCI index data base is still rare. Therefore, we would recommend associated academics and experts to participate in related research to improve the references and basis for future use.

d) This research only used two indices as research variables and used their causal behaviour to create estimations. Future research on related topics could also include other different indices for more dynamic assessments, and use their varied effects to explore and search for the common principles.

The results of this research is hoped to help investors and shipping companies to grasp the different possible outcomes and be able to be ahead in the shipping market, recognize the market freight cost fluctuations in advance, and develop investment strategies to avoid potential risks.

The author gratefully acknowledges the helpful comments and suggestions of the reviewers, which have improved the presentation. This research work was partially supported by the Ministry of Science and Technology of Taiwan, under the project number of 102-2410-H-309-013-.