terms of load capacity,has the weakest long-range correlation in freight rate time series.It is also verified that switching points exist in the DFA linear fitting of all the three indexes,at which a would experience evident shift.Long-range correlation tends to be stronger with shorter time range,indicating that long memory feature of freight rate market is quite different for short-term and long-term investors.Overall,switching point analysis is of great significance because of the existence of diverse trading horizons.With the help of long memory analysis in this paper,investors and policy makers can make better predictions of volatility behaviors of dry bulk carrier freight rate market,as well as adjust their trading strategies timely. Acknowledgements This work is sponsored by "Shanghai Jiao Tong University,State Key Laboratory of Ocean Engineering", MOE (Ministry of Education in China)Project of Humanities and Social Sciences (Project No.12YJCGJW001).The authors want to thank the reviewers for very valuable comments. References Aggarwal,R.,Inclan,C.,Leal,R.,1999.Volatility in emerging stock markets.Journal of Financial and Quantitative Analysis3401),33-55. Arouri,M.E.H.,Lahiani,A.,Levy,A.,Nguyen,D.K.,2012.Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models.Energy Economics 34(1),283-293. Baillie,R.T..Han,Y.W.,Myers,RJ.,Song,J.,2007.Long memory models for daily and high frequency commodity futures returns.Journal of Futures Markets 27(7),643-668. Baillie,R.T.,Morana,C.,2009.Modelling long memory and structural breaks in conditional variances:An adaptive FIGARCH approach.Journal of Economic Dynamics and Control 33(8),1577-1592. Barkoulas,J.,Labys,W.C.,Onochie,J.,1998.Fractional dynamics in international commodity prices.Journal of Futures Markets17(2),161-169. Charfeddine,L.,Ajmi,A.N.,2013.The Tunisian stock market index volatility:Long memory vs.switching regime. Emerging Markets Review 16,170-182. Chen,Z.,Daigler,R.T.,Parhizgari,A.M.,2006.Persistence of volatility in futures markets.Journal of Futures Markets 26(6).571-594. Christensen,B.J.,Nielson,M.O.,Zhu,J.,2010.Long memory in stock market volatility and the volatility-in-mean effect:the FIEGARCH-M model.Empir.Finance 17,460-470. Crato,N.,Ray,B.K.,2000.Memory in returns and volatilities of futures'contracts.Journal of Futures Markets 20(6), 525-543. Elder,J.,Jin,H.J.,2007.Long memory in commodity futures volatility:A wavelet perspective.Journal of Futures Markets27(5),411-437. Elder,J.,Serletis,A.,2008.Long memory in energy futures prices.Review of Financial Economics 17(2),146-155. Fernandez,A.,Arago,V.,2003.European volatility transmission with structural changes in variance.Working Paper presented at the XI Foro de Finanzas,Alicante(Spain). Fernandez,V.,2010.Commodity futures and market efficiency:A fractional integrated approach.Resources Policy 35(4), 276-282 Inclan,C.,Tiao,G.C.,1994.Use of cumulative sums of squares for retrospective detection of changes of variance. 1212 terms of load capacity, has the weakest long-range correlation in freight rate time series. It is also verified that switching points exist in the DFA linear fitting of all the three indexes, at which  would experience evident shift. Long-range correlation tends to be stronger with shorter time range, indicating that long memory feature of freight rate market is quite different for short-term and long-term investors. Overall, switching point analysis is of great significance because of the existence of diverse trading horizons. With the help of long memory analysis in this paper, investors and policy makers can make better predictions of volatility behaviors of dry bulk carrier freight rate market, as well as adjust their trading strategies timely. Acknowledgements This work is sponsored by “Shanghai Jiao Tong University, State Key Laboratory of Ocean Engineering”, MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No.12YJCGJW001). The authors want to thank the reviewers for very valuable comments. References Aggarwal, R., Inclan, C., Leal, R., 1999. Volatility in emerging stock markets. Journal of Financial and Quantitative Analysis 34(01), 33-55. Arouri, M.E.H., Lahiani, A., Lévy, A., Nguyen, D.K., 2012. Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models. Energy Economics 34(1), 283-293. Baillie, R.T., Han, Y.W., Myers, R.J., Song, J., 2007. Long memory models for daily and high frequency commodity futures returns. Journal of Futures Markets 27(7), 643-668. Baillie, R.T., Morana, C., 2009. Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach. Journal of Economic Dynamics and Control 33(8), 1577-1592. Barkoulas, J., Labys, W.C., Onochie, J., 1998. Fractional dynamics in international commodity prices. Journal of Futures Markets 17(2), 161-169. Charfeddine, L., Ajmi, A.N., 2013. The Tunisian stock market index volatility: Long memory vs. switching regime. Emerging Markets Review 16, 170-182. Chen, Z., Daigler, R.T., Parhizgari, A.M., 2006. Persistence of volatility in futures markets. Journal of Futures Markets 26(6), 571-594. Christensen, B.J., Nielson, M.O., Zhu, J., 2010. Long memory in stock market volatility and the volatility-in-mean effect: the FIEGARCH-M model. Empir.Finance 17, 460–470. Crato, N., Ray, B.K., 2000. Memory in returns and volatilities of futures' contracts. Journal of Futures Markets 20(6), 525-543. Elder, J., Jin, H.J., 2007. Long memory in commodity futures volatility: A wavelet perspective. Journal of Futures Markets 27(5), 411-437. Elder, J., Serletis, A., 2008. Long memory in energy futures prices. Review of Financial Economics 17(2), 146-155. Fernández, A., Aragó, V., 2003. European volatility transmission with structural changes in variance. Working Paper presented at the XI Foro de Finanzas, Alicante (Spain). Fernandez, V., 2010. Commodity futures and market efficiency: A fractional integrated approach. Resources Policy 35(4), 276-282. Inclan, C., Tiao, G.C., 1994. Use of cumulative sums of squares for retrospective detection of changes of variance