Abstract: BDI index forecasting is of great practical significance for the management and decision-making of shipping market. In this study, we construct a O-U stochastic forecasting model of BDI index based on A new perspective from periodicity, mean reversion and jump features in this paper, The main innovation points are: Firstly, The periodicity, mean reversion and jump features of BDI index are analyzed and included in the BDI Index Stochastic forecasting model, which effectively improves the accuracy of the BDI index forecasting theory. Secondly, the BDI index stochastic forecasting model based on periodicity, mean reversion and jump features is set up. Simultaneously, the periodic parameters are estimated by the Fourier series function, the mean reverting parameters are estimated by means of the first order autoregressive estimation, and the jump parameters are estimated by means of Gamma distribution and double exponential distribution, which solves the problem of more parameters extremely difficult to calculate. Thirdly, the daily BDI data in 2013 to 2015 are presented to test the proposed model. The BDI index was fitted, and in the first half of 2016 is forecasting by Monte Carlo method. The results show that the proposed method has higher accuracy.

Key words: BDI index, cycle, mean reversion, jump, O-U stochastic process, forecasting