A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting

2012 ◽  
Vol 29 (6) ◽  
pp. 2245-2253 ◽  
Author(s):  
Li Liu ◽  
Jieqiu Wan
Keyword(s):  
High Frequency ◽  
Price Volatility ◽  
Fuel Oil ◽  
High Frequency Data ◽  
Frequency Data ◽  
Oil Futures ◽  
Dependence Modeling ◽  
Modeling And Forecasting ◽  
Futures Price ◽  
Fuel Oil Futures

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.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

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