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Crude Oil Price Analysis and Forecasting based on Empirical Mode Decomposition

Friday 8 March 2013, 3.15PM to 5.00pm

Speaker(s): Dr. Xun Zhang, Chinese Academy of Sciences (Beijing)

This is a joint seminar with Professor Shouyang Wang and Dr. Ai Han also speaking. See department seminars for more details regarding their talks.

Abstract: The importance of understanding the underlying characteristics of international crude oil price movements attracts much attention from academic researchers and business practitioners. Due to the intrinsic complexity of the oil market, however, most of them fail to produce consistently good results. Empirical Mode Decomposition (EMD), recently proposed by Huang et al., appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. Ensemble EMD (EEMD) is a substantial improvement of EMD which can better separate the scales naturally by adding white noise series to the original time series and then treating the ensemble averages as the true intrinsic modes. In this paper, we extend EEMD to crude oil price analysis. First, three crude oil price series with different time ranges and frequencies are decomposed into several independent intrinsic modes, from high to low frequency. Second, the intrinsic modes are composed into a fluctuating process, a slowly varying part and a trend based on fine-to-coarse reconstruction. The economic meanings of the three components are identified as short term fluctuations caused by normal supply-demand disequilibrium or some other market activities, the effect of a shock of a significant event, and a long term trend. Finally, the EEMD is shown to be a vital technique for crude oil price analysis.


Dr. Zhang is an assistant professor at the Academy of Mathematics and Systems Science (AMSS) of Chinese Academy of Sciences (CAS) in Beijing, after obtaining her PhD degree in Management Science and Engineering at AMSS, CAS in 2009. She is also a researcher at Center for Forecasting Science of CAS, which provides quarterly economic projections for China.

Dr. Zhang’s research interests cover a broad set of topics in the art of forecasting and experimental economics. Among her recent areas of interests are data decomposition methods with applications to economic and financial data, analysis and forecasting of China and Asian business cycle, and energy markets modelling based on dynamic stochastic equilibrium models. She has published papers on journals such as energy economics, international journal of information technology and decision making, and Asia-Pacific Journal on Accounting and Economics etc.. She is also (guest) editor for some journals such as Advances in Information Sciences and Service Sciences, Journal of Systems Science & Complexity.

Dr. Zhang was awarded many prizes and awards including the CAS Prize for Chen Jingrun Future Star in 2012, Excellent Ph.D. dissertation of CAS in 2010, CAS Presidential Scholarship (Special Prize) in 2009, The Green Group Award of Computational Finance and Business Intelligence in 2009 and Best Paper Award for the 8th International Conference on Risk Management and Financial Systems Engineering in 2010.

Research areas: Economic and financial forecasting; Energy modeling and forecasting.

Location: Economics Staff Room (EC/202)

Admission: For Staff and PhD students