基于支持向量机的干散货航运市场运价预警

为分析预测干散货航运市场运价波动的警情,建立基于支持向量机的运价预警模型,并构造相应的算法.选择波罗的海海岬型指数(Baltic Capesige Index BCI)、波罗的海巴拿马指数(Baltic Panamax Index BPI)、波罗的海灵便型指数(Baltic Supramax Index BSI)、波罗的海小灵便型指数(Baltic Handsige Index BHSI)等四个干散货运价指数作为警兆指标,结合航运专家知识经验,确定干散货航运市场运价的实际警度.依据训练样本数据,利用支持向量机的学习功能,通过编制MATLAB软件程序,获得市场运价警度的分类超平面及预测警度区间,并进行内插和外推检验.检验结果表明此方法对于干散货航运市场运价预警有很好的适用性.     

基于GARCH模型的波罗的海干散货运价指数预测

研究波罗的海干散货运价指数(BDI),对其日收益率序列建立模型,并比较不同分布的各模型优劣。结果表明,基于不同分布的GARCH模型对BDI的静态预测误差均很小,利用GARCH模型对短期运价波动的预测精度较高。     

国际干散货运输市场月评

<正>(2019年11月)本期国际干散货运输市场运价分船型表现各异,海岬型船航运市场运价波动剧烈,巴拿马型船和超灵便型船航运市场运价先抑后扬。三大船型运价本期平均值水平均低于上期,其中:海岬型船运价高于上年同期,巴拿马型船和超灵便型船运价低于上年同期。11月29日,上海航运交易所发布的远东干散货指数(FDI)综合指数、运价指数和租金指数分别报收于1 101.94点、     

波罗的海运价指数相关性分析

航运运价的波动与船舶供给变化有关。当船舶供给发生变化时,会引起市场运价的变动;同样,运价的波动也会影响船舶供给。本文以波罗的海干散货运价指数(BFI)为研究对象,从供求关系决定价格的角度分析船舶运力与航运运价指数的相关关系,研究船舶运力供给各指标和运价指数的相关程度。     

波罗的海干散货运价指数演变机理

利用基础分析法研究了波罗的海干散货运价指数(BDI)的演变机理,期望在研究航运期权定价模型、利用运费期权规避航运风险方面有一定的借鉴和指导作用。     

波罗的海干散货运价指数演变机壁

利用基础分析法研究了波罗的海干散货运价指数(BDI)的演变机理，期望在研究航运期权定价模型、利用运费期权规避航运风险方面有一定的借鉴和指导作用。     

基于BP神经网络的干散货航运市场运价预警

为分析预测干散货航运市场运价波动的警情,采用BP神经网络的学习算法,建立干散货航运市场运价预警模型。选择干散货船队运力供给、干散货船队运力需求、OECD工业生产指数、原油价格和FFA合约价等5项指标作为警兆指标,运用因子分析法并结合航运专家知识经验,确定干散货航运市场运价的实际警情。通过编制MATLAB软件程序,对实例样本进行训练和检测,表明此方法对干散货航运市场运价预警有很好的适用性。     

沿海运力持续紧张运价淡季创新高

在众多利好因素推动下，9月份波罗的海干散货综合运价指数（BDI）连破8000点，9000点两个千点关口，9月28日报收9474点，创造了单月上涨1772点新记录，三大船型运价再创历史新高，这在世界航运史上绝无仅有。     

跌跌不休,运价继续走低——2011年11月国内外航运市场简评

1国际市场:中小船型领跌,BDI指数小幅下滑11月30日,波罗的海航运交易所干散货综合运价指数(BDI)报收于1846点,较上月下降6.1%,其中:海岬型船3298点,上升5.3%;巴拿马型船1727点,下降10.6%;大灵便型船1359,下降11.2%。国际铁矿石价格持续下滑刺激需求增长,11月     

航运指数:裂变与轮回

近几年,波罗的海干散货综合运价指数(BDI)上蹿下跳,让众多船东、贸易商、金融机构如坐过山车般心惊肉跳。对中国相关方而言,曾经被誉为航运市场晴雨表的BDI,以及由此而生的运价指数衍生品,一夜之间变得陌生而捉摸不透。那么,航运指数究竟为何物?其背后又蕴藏着哪些奥秘?     

An analysis of freight rate volatility in dry bulk shipping markets

The world bulk shipping market has been in a peak period since 2003, and this has lasted an incredibly long time considering that the markets are much more complex than before. This paper investigates the characteristics of volatility in dry bulk freight rates of different vessel sizes (capesize, panamax and handysize). The daily returns of freight rate indices of three different types of bulk vessel in the sample period have been examined. The sample period ran from 1 March 1999 to 23 December 2005, and applying the GARCH (generalized auto regressive conditional heteroskedasticity) model showed that the shocks will not decrease but have the tendency to strengthen for all the daily return series. Further, external shocks on the market have a different magnitude of influence on volatility in different types of vessels due to their distinct flexibility. To examine the asymmetric characters of daily return volatility in different bulk shipping sectors and different market conditions, the sample was divided into two periods: one is from 1 March 1999 to 31 December 2002, the other is from 1 January 2003 to 23 December 2005; the EGARCH (exponential generalized auto regressive conditional heteroskedasticity) model was then applied to investigate the asymmetric impact between past innovations and current volatility. The results show that the asymmetric characters are distinct for different vessel size segments and different market conditions. The reasons for the results are discussed and it is considered that the main reasons may be the different flexibility and different commodity transport on different routes. The results from this investigation will be useful for the operators and investors in the dry bulk shipping market to increase profitability and reduce investment risk.     

Return lead–lag and volatility transmission in shipping freight markets

This study investigates the return lead–lag and volatility transmission between dry bulk shipping and container shipping freight markets over the period before, during and after the 2008 financial tsunami. Both cointegration analysis and the Granger causality test are applied to explore the lead–lag relationship between the Baltic dry index (BDI) and the China containerized freight index (CCFI). Besides, in the study we employed GARCH–BEKK model, which allows for transmission in freight volatility. On the whole, the empirical results show that the BDI reflects the economic climate earlier than the CCFI during the financial tsunami, whereas the CCFI leads the BDI after the financial tsunami. The price formation hypothesis could well explain the relationship. Moreover, volatility spillovers are found in most subperiods. The dynamics of the conditional volatilities differ, but causality links in the variance are found to be strong and bidirectional in normal periods, and unidirectional during the financial tsunami. Therefore, the occurrence of the financial tsunami could be regarded as an interference factor.     

The study on the compilation of the China container freight index

Although there exists many kinds of shipping freight indexes, the compilation of a container freight index is still not shown. As such, this paper has taken a further study on the compilation of the China container freight index, which includes selection of calculation formula, determination of freight type, the choice of samples of shipping lines and rules of revision of the index.     

The study on the compilation of the China container freight index

Although there exists many kinds of shipping freight indexes, the compilation of a container freight index is still not shown. As such, this paper has taken a further study on the compilation of the China container freight index, which includes selection of calculation formula, determination of freight type, the choice of samples of shipping lines and rules of revision of the index.     

Shipping finance: time to follow a new track?

In this paper we introduce, for the first time, a methodology from the most recent literature of finance to reveal the duration of shipping cycles and then show the benefit of the use of this information to make more successful shipping loans. This is so as banks are willing to finance, during boom periods, shipping loans for new buildings but by this way 'create' oversupply and thus depress the freight market by their own actions. The information about cycles, especially their forecasting, is mostly important as shipping loans are based on project financing/cash flow financing, which means that ship revenue is of utmost importance. The Rescaled Range Analysis is applied here to 379 monthly freight trips—made stationary—between 1971 and 2002 (July), due to Hurst 1 and elaborated and popularized by Mandelbrot 2. The most important effect, however, is that shipping freight series exhibit non-normality and long-run dependence rendering the use of random walk models such as GARCH (Generalized Autoregressive Conditional Heteroscedasticity) problematic. Thus an adequate literature review is carried out with criticism against the models used. The cycles have been calculated as equal to 4.5 years and 2.25 years. This is almost compatible with the most recent paper of Stopford 3. The Hurst exponent was found equal to 0.93, alternating over the periods examined (0.65, 0.73, 0.62, 0.59 and 0.55) and indicating long-term persistence but seriously away from normal/random walk domain. Most studies have said the same using the Jarque--Bera test for normality but provided no alternative.     

典型航运公司应收运费核算系统的设计

作为业务复杂度高的集装箱运输,正确收取应收运费对航运公司而言有着重要的意义.基于某典型航运公司的业务特点,在分析现有应收运费核算模式局限性的基础上,通过研究运价协议构成和应收核算模式,实现了电子运价本、运价核算的自动化和差错更正流程,并建立了应收运费核算系统.系统成功实施后,显著提高了应收运费的准确性,保障了航运公司的应得利益.     

An approach for Baltic Dry Index analysis based on empirical mode decomposition

The bulk shipping market is seasonal, cyclical and highly volatile. Due to the nonstationary and nonlinear nature of price series and the complexity of influencing factors, it is difficult to analyse the fluctuations in the bulk shipping market. In this study, a method based on empirical mode decomposition (EMD) is proposed to investigate the volatility of the Baltic Dry Index (BDI). In this method, the original freight price series is decomposed into several independent intrinsic modes, using EMD first. Then, the intrinsic modes are composed into three components: short-term fluctuations caused by normal market activities, the effect of extreme events and a long-term trend. Numerical experiments indicate that the proposed method can effectively reveal the characteristics of bulk freight price series with different economic meanings and decrease error accumulation. Meanwhile, by decomposition of intrinsic modes, the complexity of the model formulation can be controlled and the operability of the model can be improved.     

Strategic groups in Taiwanese liner shipping

Liner shipping is normally viewed as being oligopolistic in nature with firms competing on the basis of service offered. Since shipping services are easily copied by competitors, if shipping firms want to gain a competitive advantage, it is essential to identify the competitors' strategies. This paper uses the analytical concepts of strategic groups theory to explore the strategic differences in the Taiwanese (the Republic of China, ROC) shipping industry. Cluster analysis is used to classify shipping companies, shipping agencies and ocean freight forwarders into four strategic groups on the basis of the key strategic factors obtained from the factor analysis. The results of multivariate analysis of variance (MANOVA), analysis of variance (ANOVA) and the Scheffe test showed that strategies among the strategic groups are significantly different.     

Are there bubbles in the shipping freight market?

ABSTRACT

This paper addresses the reconsideration of price bubbles specific to the shipping freight market based on the method of the Generalized Supremum Augmented Dickey–Fuller (GSADF). This approach offers the opportunity to recognize multiple bubbles and set their corresponding original and final dates. Empirical results reveal that four bubbles existed in the shipping freight market between October 1988 and February 2018 in which freight deviated from fundamental values. Strong demand (especially in China), the supply capacity, crude oil prices and U.S. dollar fluctuations are potential explanations for the first three bubbles. The global financial crisis that burst in 2008 is the major factor results in the last bubble. Hence, we must distinguish the potential reasons of bubbles in different periods and take measures such as promoting economic multipolarization, strengthening the bargaining power of China, building an effective information transfer system, employing financial derivatives and accelerating the consolidation of the shipping industry to alleviate the negative influences on global seaborne trade.