欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
在考虑当前预期和波动性条件下，为了有效地捕获极端条件下收益率时间序列动态特征，提高VaR的度量精度，建立了基于高频数据的条件极值VaR模型。应用智能优化算法对条件极值分布的时变参数进行估计，考察了在不同样本容量分块下的条件极值VaR，并对VaR计算结果的精度进行了Kupiec-LR检验和动态分位数检验。研究结果表明，基于高频数据的条件极值分布较好地拟合了极端条件下的收益率特征，与McNeil提出的传统条件极值VaR相比，应用高频数据建立在条件广义极值分布基础上的条件极值VaR的Kupiec检验DQ检验值都较为理想，表明该模型能够捕捉到我国市场风险特征，提高极端情况下风险测度能力。
英文摘要：
Considering the factors of anticipation and volatility, to catch the character of return series in extreme condition and improve VaR precision, a model of conditional extreme value VaR is established. The time-varying parameters of conditional generalized extreme value distribution is estimated using intelli- gence optimization algorithm, calculating the diversified extreme value VaR at the different block and checking the results by invoking Kupiec-LR and dynamic quentile test. The anmysis on models and VaR shows that conditional generalized extreme value distribution is better fitted with the feature of return series in extreme condition. Comparing our model with McNeil＇s, Kupiec-LR and dynamic quantile test of conditional extreme value VaR using high frequency return perform well, which has the implication that our model can catch the risk character of Chinese stock markets and improve estimation in extreme condition.
同期刊论文项目
同项目期刊论文
位置：