Modeling volatility of Baltic dry bulk freight index
Qingdao（2008）
摘要
The paper is to investigate features of fluctuation of international dry bulk shipping market using Baltic dry bulk freight index. After fundamental statistical analysis on data, R/S and GPH tests are employed to model long memory of volatility of the indices, which interprets the existence of long memory and then leverage effect in the market subdivided by ship types including Handymax, Panamax, and Capesize. Moreover, VaR (value-at-risk) of spot freight rates in the dry bulk shipping market with EGARCH (Exponential ARCH) VaR model is used to further the study on volatility of the indices because of the daily return series with factors of volatility clustering and significant ARCH (Autoregressive Conditional Heteroskedastic) and the distribution with characteristics of fat tail. The empirical results suggest the operators and investors in the dry bulk shipping market to increase operational profits and reduce investment risks according to the long memory and the VaR.
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关键词
autoregressive processes,freight handling,investment,logistics,risk analysis,series (mathematics),ships,statistical analysis,transportation,arch,baltic dry bulk freight index volatility modeling,var model,autoregressive conditional heteroskedastic,daily return series,international dry bulk shipping market fluctuation,investment risk,value-at-risk,volatility clustering,correlation,indexation,series mathematics,autoregressive conditional heteroskedasticity,fat tail,indexes,fluctuations,reactive power,time series analysis,value at risk,long memory,profitability
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