期货交易风险管理决策模型研究
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摘要
期货交易作为一种现代投资手段,对经济的发展日益起着重要的作用。随之产生的风险成为投资者不得不面临的问题。如何尽可能地降低期货交易产生的风险,以获取最大的利润,成为值得广泛关注的研究内容。
文章的主要工作表现在以下几个方面:
第一,以条件风险价值(CVaR)为风险计量指标,测定期货风险的价值。其一,根据CVaR风险评估对波动性的要求,采用GARCH模型,解决期货合约波动率的聚集效应、厚尾效应和时变方差效应,同时,通过加入GED分布,有效地提高了预测结果的准确度。其二,SV模型对金融市场收益率分布也有着较好的刻画能力,本研究采用马尔科夫链蒙特卡洛模拟的极大似然法,估计SV模型的参数,并计算了沪铜的CVaR值,结果表明,SV模拟能够很好地反映沪铜的风险水平。
第二,采用VaR技术计算保证金。所不同的是,用蒙特卡罗模拟解决传统VaR模型对价格波动极端状况时的低估问题,同时用EGARCH模型估计波动性和用t分布代替正态分布,提高蒙特卡洛模拟法计算VaR的准确性。根据对上海期货交易所铜期货保证金水平的实证结果,模拟的保证金算法能够适应铜期货合约风险管理的需求,保证金水平反映了市场风险状况,有效的降低了投资者交易成本。
第三,分别构建了GARCH-X模型和DC-MSV模型来预测动态套期保值比率。GARCH-X套期保值模型在二元GARCH模型中添加了误差矫正项,考虑了短期收益率的波动对条件方差和协方差的影响,更好的预测了收益率波动的变化,提高了套期保值的准确性。DC-MSV模型反映了推动资产价格波动因素的有效信息,更加完整的估计资产收益的交叉相关性,得到的最小方差时变套期比更加精确的反映资产价格的波动变化。
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.
文章的主要工作表现在以下几个方面:
第一,以条件风险价值(CVaR)为风险计量指标,测定期货风险的价值。其一,根据CVaR风险评估对波动性的要求,采用GARCH模型,解决期货合约波动率的聚集效应、厚尾效应和时变方差效应,同时,通过加入GED分布,有效地提高了预测结果的准确度。其二,SV模型对金融市场收益率分布也有着较好的刻画能力,本研究采用马尔科夫链蒙特卡洛模拟的极大似然法,估计SV模型的参数,并计算了沪铜的CVaR值,结果表明,SV模拟能够很好地反映沪铜的风险水平。
第二,采用VaR技术计算保证金。所不同的是,用蒙特卡罗模拟解决传统VaR模型对价格波动极端状况时的低估问题,同时用EGARCH模型估计波动性和用t分布代替正态分布,提高蒙特卡洛模拟法计算VaR的准确性。根据对上海期货交易所铜期货保证金水平的实证结果,模拟的保证金算法能够适应铜期货合约风险管理的需求,保证金水平反映了市场风险状况,有效的降低了投资者交易成本。
第三,分别构建了GARCH-X模型和DC-MSV模型来预测动态套期保值比率。GARCH-X套期保值模型在二元GARCH模型中添加了误差矫正项,考虑了短期收益率的波动对条件方差和协方差的影响,更好的预测了收益率波动的变化,提高了套期保值的准确性。DC-MSV模型反映了推动资产价格波动因素的有效信息,更加完整的估计资产收益的交叉相关性,得到的最小方差时变套期比更加精确的反映资产价格的波动变化。
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.
引文
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