A Comparative Study of Prediction Models of High-Frequency Fluctuation of China's Fuel Oil Futures

2016 ◽  
Author(s):  
Guest Editor Jianping Du
Keyword(s):  
Comparative Study ◽  
High Frequency ◽  
Prediction Models ◽  
Fuel Oil ◽  
Frequency Fluctuation ◽  
Oil Futures ◽  
Fuel Oil Futures ◽  
High Frequency Fluctuation

A Comparative Study of VAR Measure Using Different Frequency Data of China’s Fuel Oil Futures Market

2011 ◽  
Vol 361-363 ◽  
pp. 1887-1891
Author(s):  
Feng Wang
Keyword(s):  
High Frequency ◽  
Value At Risk ◽  
Low Frequency ◽  
Futures Market ◽  
Fuel Oil ◽  
High Frequency Data ◽  
Frequency Data ◽  
Oil Futures ◽  
Ultra High Frequency ◽  
Fuel Oil Futures

By using datas of Chinese fuel oil futures market, this pater establishes VAR model based on low frequency, high frequency and ultra-high frequency data, to measure the value at risk, and compares the prediction accuracy of different frequency. The research results show that the high frequency and ultra-high frequency data have better accuracy in the VAR measuring, as they contain more intraday information and can reflect the futures market microstructure better.

Bursting high-frequency fluctuation observed in biased plasma in TU-Heliac

2014 ◽  
Vol 54 (11) ◽  
pp. 114013
Author(s):  
S. Koike ◽  
S. Kitajima ◽  
A. Okamoto ◽  
K. Ishii ◽  
Y. Sato ◽  
...  
Keyword(s):  
High Frequency ◽  
Frequency Fluctuation ◽  
High Frequency Fluctuation

scholarly journals An empirical analysis of the price discovery function of Shanghai fuel oil futures market

2007 ◽  
Vol 4 (3) ◽  
pp. 97-102 ◽  
Author(s):  
Zhen Wang ◽  
Zhenhai Liu ◽  
Chao Chen
Keyword(s):  
Empirical Analysis ◽  
Price Discovery ◽  
Futures Market ◽  
Fuel Oil ◽  
Oil Futures ◽  
Fuel Oil Futures

scholarly journals An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China

2017 ◽  
Vol 63 (No. 3) ◽  
pp. 136-148 ◽  
Author(s):  
Xiong Tao ◽  
Li Chongguang ◽  
Bao Yukun
Keyword(s):  
High Frequency ◽  
Hybrid Approach ◽  
Low Frequency ◽  
Frequency Fluctuation ◽  
Price Forecasting ◽  
Short Term ◽  
Price Series ◽  
Short Term Forecasting ◽  
High Frequency Fluctuation ◽  
Improved Eemd

Short-term forecasting of hog price, which forms the basis for the decision making, is challenging and of great interest for hog producers and market participants. This study develops improved ensemble empirical mode decomposition (EEMD)-based hybrid approach for the short-term hog price forecasting. Specifically, the EEMD is first used to decompose the original hog price series into several intrinsic-mode functions (IMF) and one residue. The fine-to-coarse reconstruction algorithm is then applied to compose the obtained IMFs and residue into the high-frequency fluctuation, the low-frequency fluctuation, and the trend terms which can highlight new features of the hog price fluctuations. Afterwards, the extreme learning machine (ELM) is employed to model the low-frequency fluctuation, while the autoregressive integrated moving average (ARIMA) and the polynomial function are used to fit the high-frequency fluctuation and trend term, respectively, in a multistep-ahead fashion. The commonly used iterated prediction strategy is adopted for the implementation of the multistep-ahead forecasting. The monthly hog price series from January 2000 to May 2015 in China is employed to evaluate the forecasting performance of the proposed approach with the selected counterparts. The numerical results indicate that the improved EEMD-based hybrid approach is a promising alternative for the short-term hog price forecasting.  

scholarly journals Nonlinear Coupling of Drift Waves and High Frequency Fluctuation on PANTA

2017 ◽  
Vol 12 (0) ◽  
pp. 1201034-1201034
Author(s):  
Kazunobu HASAMADA ◽  
Yusuke KOSUGA ◽  
Fumiyoshi KIN ◽  
Sigeru INAGAKI ◽  
Yoshihiko NAGASHIMA ◽  
...  
Keyword(s):  
High Frequency ◽  
Frequency Fluctuation ◽  
Drift Waves ◽  
Nonlinear Coupling ◽  
High Frequency Fluctuation
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