分布不确定下的风险对冲策略及其效用

发布时间：2018-03-07 09:07

本文选题：风险对冲　切入点：期望效用　出处：《中国管理科学》2017年01期 　论文类型：期刊论文

【摘要】：为避免参数法和半参数法的事前模型设定偏误和参数估计误差,本文引入无需做事前分布假设的非参数核估计法对VaR和CVaR进行估计,并基于VaR和CVaR的核估计量构建风险对冲模型,实现风险估计和风险对冲同时进行。在此基础上,本文进一步在核估计框架内引入期望效用理论比较最小方差、最小VaR和最小CVaR对冲策略的对冲效率,以期解决传统文献将风险下降比率作为风险对冲效率指标,却因风险度量指标不同而导致比较结果不一致的问题。最后,将核估计框架下的风险对冲模型和期望效用理论运用到沪深300股指期货现货的风险对冲问题,实证结果表明:最小CVaR对冲策略的对冲效率优于最小方差和最小VaR对冲策略,且四种效用函数给出的比较结果一致。
[Abstract]:In order to avoid the prior model setting error and parameter estimation error of parameter method and semi-parameter method, the nonparametric kernel estimation method is introduced to estimate VaR and CVaR without prior distribution hypothesis. Based on the kernel estimator of VaR and CVaR, the risk hedging model is constructed to realize both risk estimation and risk hedging. On this basis, the expected utility theory is further introduced into the framework of kernel estimation to compare the minimum variance. The hedge efficiency of the minimum VaR and minimum CVaR hedging strategies, in order to solve the problem that the risk reduction ratio is regarded as the risk hedging efficiency index in the traditional literature, but the results of the comparison are inconsistent because of the difference of the risk measurement index. The paper applies the risk hedging model and expected utility theory under the framework of kernel estimation to the hedging problem of Shanghai and Shenzhen 300 stock index futures. The empirical results show that the hedging efficiency of minimum CVaR hedging strategy is better than that of minimum variance and minimum VaR hedging strategy. The comparison results of the four utility functions are consistent.
【作者单位】：广东财经大学金融学院;中山大学管理学院;
【基金】：国家自然科学基金资助项目(71231008,71603058,71573056) 教育部人文社会科学研究项目(16YJC790033) 中国博士后科学基金资助项目(2014M562246) 广东省自然科学基金资助项目(2014A030312003,2016A030313656) 广东省哲学社会科学规划项目(GD15YYJ06,GD15XYJ03) 广州市哲学社会科学规划项目(15Q20) 广州市社会科学界联合会2016年“羊城青年学人”研究项目(16QNXR08)
【分类号】：F224;F724.5

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