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Based On Var Shanghai Fuel Oil Futures Market Risk Analysis

Posted on:2012-05-05Degree:MasterType:Thesis
Country:ChinaCandidate:X Z WuFull Text:PDF
GTID:2219330371951380Subject:Finance
Abstract/Summary:PDF Full Text Request
With the imbalance growth of the world economic, the structure of energy demand are to be adjusted inevitably, the supply and demand of oil are out of balance in the international market. Besides, other factors, such as war, which make the international oil price fluctuates frequently, impact on China's oil markets tremendously. Shanghai fuel oil futures were traded at the Shanghai Futures Exchange official in 2004.After six years of history, fuel oil futures gradually mature. At present, we study it mainly on qualitative and normative analysis, and the quantitative method was not relatively rich enough, especially on the futures market risk measure. This article uses the JP Morgan's VaR method to measure the risk of fuel oil futures market quantitatively, which support experience and reference.This paper mainly studies using the method of VaR to measure Shanghai fuel futures risk. After introducing the related research achievements, the paper introduces the related theory of futures market risk, especially introduces the principle of the method of the VaR. Then, in the part of empirical research, it first analysis the sequence of yield of the Shanghai basically, and it obtains the sample series is not the normal distribution, and through the ADF test, it proves the sample series is the non-stationary series. On the analysis of the relevant features of the series, we combine the characteristics of the GARCH family, and we establish the EGARCH-M model to measure the VaR of the Shanghai fuel futures market. Final, we use the Kupiec method to test the calculated results were verified, the result shows that the established model was effective, it suggested that using the VaR methods to control the risk of Shanghai fuel futures market is feasible.
Keywords/Search Tags:Fuel oil futures, Variance - covariance method, Monte Carlo simulation, Historical simulation, Kupiec test
PDF Full Text Request
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