Baltic Freight Index Risk Measure

The word “Marine economy”, has been increasingly eye-catching in recentyears, which has not only become a new economic growth engine in the world,the highlights of the “12th five-year plan” in China. As the intersection ofthe financial sector and shipping, shipping finance plays an important role ineconomic construction and society development. Our country in the internationalshipping industry plays an increasingly important role, however, risk managementfor shipping is still a large gap between the international community.State-owned enterprises suffer huge losses in the trade, which can explain thereason. Therefore, developing shipping finance, risk management is the key,especially the fluctuations have been more severe.This paper attempts to contrast VaR method with CVaR method in the shippingfinance market of the risk measurement applications, to establish a moreeffective risk management system. VaR method is one of the most popular methodsin risk management.Given confidence level, VaR method is a measure of a portfolioof financial assets in the future will be maximum loss values in the expectedholding period. Since VaR is not a coherent risk measure, the scholars developCVaR. CVaR meet the risk measure’s needs and considers the losses exceeded VaR’s risk,which strongly recommended by the academia. Doing VaR and CVaR’s researchin shipping index will enhance our international competitiveness,making sensein the application of the shipping market.International dry bulk shipping market is affected by many factors, such asdemand, supply, costs and significant short-term political and economic eventsas well as natural disasters. In addition, the dry bulk shipping market is almosta perfectly competitive market, which is more sensitive to a variety of factors.As reflecting the overall level of international dry bulk freight, the Baltic DryIndex has been highly concerned by the shipping community, known as thebarometer in the international dry bulk shipping market. It is constituted bythe Baltic Sea Index, the Baltic Panamax Index and the Baltic Capesize Index.The BHMI’s cargo is phosphate, potash, wood, cement; the BPI is grain and otherbulk materials; the BCI’s cargo is coking coal, coal, iron ore, phosphate rock,bauxite and other industrial raw materials.Firstly, we select BCI, BPI, BDI as the research sample data, and then through basic statistical analysis, ultimately taking BDI index for the study. After usingEVIEWS software to analysis the statistical characteristics of the sample data andthe ARCH test, we establish a GARCH (1,1) model to obtain return series andvolatility sequence. Secondly, by comparing the distribution based on differentpros and cons of each model, we identify the most suitable model. Finally, theresearch shows, GARCH-T (1,1) model, matching the various constraints, is the bestindicator of a fat tail of BDI yields. Afterwards, let’s compare VaR with CVaRtheory, including the calculation of them and their advantages and disadvantages.Based on GARCH model, we conclude that CVaR is better than VaR about coveragingloss.The innovation of this paper is that we put general distribution and GARCHModels into CVaR model to measure the shipping market risk, with qualitativeanalysis in the shipping market risk measurement and research. The empiricalresults shows that the shipping market is vulnerable to the external news, showingrelatively huge fluctuations in market characteristics, establishing a higherrequirements of risk management system to our shipping finance market.