1 Long Memory Analysis of Bulk Freight Rate under Structural Breaks Si-Yu Dai 1 , Ya-Dong Zeng 1 , Fei-Er Chen 1 1 State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai Abstract: Analysis of freight rate volatility characters under structural breaks is of great importance after year 2008. In this paper, the presence of structural break points in bulk freight rate index time series is investigated by employing the Iterated Cumulative Sum of Squares (ICSS) algorithm. Furthermore, long memory features comparison of different vessel sizes, Supramax, Panamax and Capesize bulk carriers is also accomplished, and emperical results show that dry bulk carriers with smaller ship size are inclined to have stronger long-range correlation. Results of different types of Detrended Fluctuation Analysis (DFA) Method are compared, and influence of seasonality is taken into consideration. For policy makers and investors with different trading horizons, switching point is an important indicator of different long memory characters of freight rate index time series in different time ranges. Hence switching points in DFA results are picked out and conclusions of long memory feature are achieved. Key words: Freight rate; Long memory; Structural break; Detrended fluctuation analysis. 1 Introduction Freight rate market is a vital component of shipping industry, of which the distinctive characteristic is dynamic and volatile (Zhang et al., 2014). Freight rate volatility represents the fluctuation or dispersion of the freight rate in shipping market. It has been validated by large amount of researches that freight rate volatility can be rather large, while its fluctuation trend can be estimated according to its time-varying feature (Jing, 2008; Kavussanos, 1996, 2003). Recent studies suggested that shocks on volatility have long-lasting effects. This phenomenon is known in the empirical finance literature as long-range dependence behavior or long memory behavior (Charfeddine and Ajmi, 2013). When the effects of volatility shocks decay slowly, long memory in volatility would occur. Significance is attached to the long memory feature, mainly for the sake of its accordance with the presence of nonlinear dependence between observations. Although long memory features have been discussed in many fields, including hydrology, Internet traffic, commodity market and energy future market (Barkoulas et al., 1998; Chen et al., 2006; Crato and Ray, 2000; Elder and Jin, 2007; Ohanissian et al., 2008; Panas, 2001; Wang and Wu, 2012), few researches focus on the long memory feature in freight rate market. As claimed by Yalama and Celik (2013), different financial markets and different sampling periods may present different characteristics of long-range correlation. Therefore, the long memory study focused on freight rate market is indispensable. In this paper, the Detrended Fluctuation Analysis (DFA) method is employed to study the long memory feature of freight rate volatility and the robustness of long-memory inference on daily freight rate index prediction. Compared with other methods which can be used in long memory analysis, such as Rescaled Range Method (R/S) and spectrum analysis method employed in the study of Kai et al.(Kai et al., 2008), an indisputable advantage of DFA is that it can detect the long-range correlations embedded in a non-stationary time series, without being influenced by spurious detection of apparent long-range correlations caused by