The Model On Management Of Value At Risk In Futures Merchandise

As a modern investment means, future transaction plays important role in economic development day by day. Therefore emerged risk becomes the question that investors have to face. How to reduce the risk of future transaction as much as possible, making the most profit, has been a research contents worthing wide attention.The main work includes following several parts:Firstly, taking conditional value at risk(CVaR) as risk measure index, measuring value at risk of futures. According to the requirements of CVaR risk measuring to volatility, using GARCH model, solving agglomerative effect,Fat-Tail effect and time-varying variance effect of futures volatility. Through adding GED distribution, improving accuracy of prediction results effectively. SV model has good ability to characterize financial return distribution, the paper adopt maximum likelihood method of markov chain-monte carlo simulation, estimating parameters of SV model, and calculate the CVaR value of Shanghai copper, the results showed that SV model can reflect risk level of Shanghai copper effectively.Secondly, calculate margin using VaR model. Differently, adopt Monte Carlo simulation method to solve the question that VaR model underestimate price volatility in extreme situation, using EGARCH model estimate volatility and using t-distribution instead of normal distribution, improving the accuracy of VaR in the method of Monte Carlo. According to the empirical results of margin level of Shanghai copper, the method can meet the demand of copper future risk management, margin level reflects market risk situation, reducing the cost of future transaction.Thirdly, separately construct GARCH-X and DC-MSV model to forecast dynamic hedging ratio. Through introducing error correction term, considering the effect of spot price fluctuation to future price, building dynamic hedging model based on bivariate GARCH model. DC-MSV reveals the valid information of the factors which drive the price fluctuation of assets, and fully estimates the cross correlativity of price fluctuation of assets, the time-varying hedging ratio can reflect fluctuation of asset price more precise.