| As an important component of the world shipping market, international dry bulkshipping market is a derived demand of international trade; the main goods ittransports are iron ore, grain, coal and other dry bulk. In recent years, with theoccurrence of the international financial crisis and the European debt crisis, the worldeconomy will be in a precarious situation for a long term. The changes of theinternational trade environment exacerbates the international dry bulk shippingmarket’s uncertainty, making the dry bulk freight cargo shipping market fluctuationsmore frequently. Therefore, the study of the fluctuations law of international bulkshipping price index can provide a theoretical basis for the shipping marketparticipants’ decisions.Through the review of the international dry bulk shipping market, finding thatthe law on the BDI index fluctuation studies mostly concentrated in the assumption ofa normal distribution effectively market theory, the method is suitable for a uniform,continuous changes in the market to make effective explanations. For the shippingmarket there will be volatility in some period, so it loses versatility. This paperintroduces the fractal theory to study the BDI index fractal characteristics. Bycalculating the fractal characteristic parameters, the paper studies the fractal structureof the BDI index. The main contents are:Firstly, considering the interference of external factors, the article uses wavelettransform theory to process the signal, and then extracts the essential characteristics ofthe freight index to improve the research accuracy. Through several experiments, thepaper uses db4wavelet function BDI index series for4layers decomposition, andthen reconstructed as needed. The program environment is Matlab7.0.Secondly, thefourth chapter of the paper studies the fractal characteristics of the BDI. Using R/Sanalysis method, the paper studies on BDI daily return series, weekly return seriesand monthly return series.By V statistic chart,the paper studies the BDI series long term memory; through different time scales H values, the paper studies thefluctuations self similarity of BDI index. Finally, through fractal dimension, the paperanalysis the fluctuations complexity of the BDI daily, weekly and monthly returnseries. The single fractal studies the long term memory and fractal dimension of timeseries. It just explores the long term statistical behavior of a process, lacking thedescription at a given moment of time series. Therefore, combining the conclusions ofsingle fractal and the causes of multi-fractal, the article uses MF-DFA analysismethod to calculate the generalized Hurst exponent and mass index of BDI weeklyreturn series to verify BDI weekly return series’ multi-fractal structure. Finally, westudy the BDI index future trends by analyzing the Multi-spectral of BDI weeklyreturn series.By studying the fractal characteristics of BDI index, the paper indicates that theBDI index has a long memory and fractal dimension, which lays foundation for theBDI index prediction. Through the study of multi-fractal characteristics, the articleindicates that the BDI has continual property and a rising trend in the future. All ofthese provide a theoretical basis for the shipping market participants’ decisions. |
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