This paper investigates methods of value-at-risk (VaR) estimation using extreme value theory (EVT). It compares two different estimation methods, 'two-step subsample bootstrap' based on moment estimation and m...This paper investigates methods of value-at-risk (VaR) estimation using extreme value theory (EVT). It compares two different estimation methods, 'two-step subsample bootstrap' based on moment estimation and maximum likelihood estimation (MLE), according to their theoretical bases and computation procedures. Then, the estimation results are analyzed together with those of normal method and empirical method. The empirical research of foreign exchange data shows that the EVT methods have good characters in estimating VaR under extreme conditions and 'two-step subsample bootstrap' method is preferable to MLE.展开更多
针对以相关谱最大值作为统计量对线性调频/二相编码(LFM/BPSK,Linear Frequency Modulation/Binary Phase Shift Keying)混合调制信号盲处理结果进行可信性检验时,存在概率密度函数复杂,难以得到似然比检验闭合表达式的问题,提出了一种...针对以相关谱最大值作为统计量对线性调频/二相编码(LFM/BPSK,Linear Frequency Modulation/Binary Phase Shift Keying)混合调制信号盲处理结果进行可信性检验时,存在概率密度函数复杂,难以得到似然比检验闭合表达式的问题,提出了一种基于极值分布理论(EVT,Extreme Value Theory)的简化处理算法.利用相关谱最大值的极限分布替代其精确分布,基于纽曼-皮尔逊(NP,Neyman-Pearson)准则得到简化的似然比检验,给出了相应判决式及其判决门限的解析表达式.文中给出了不同假设下相关谱最大值的极限分布形式.计算机仿真结果表明:本算法与已有的恒虚警方法相当,但优于基于分组极值模型及超阈值模型的两种分布拟合检验法,且具有较低的计算复杂度.展开更多
基金the National Natural Science Foundation of China (No. 79970041).
文摘This paper investigates methods of value-at-risk (VaR) estimation using extreme value theory (EVT). It compares two different estimation methods, 'two-step subsample bootstrap' based on moment estimation and maximum likelihood estimation (MLE), according to their theoretical bases and computation procedures. Then, the estimation results are analyzed together with those of normal method and empirical method. The empirical research of foreign exchange data shows that the EVT methods have good characters in estimating VaR under extreme conditions and 'two-step subsample bootstrap' method is preferable to MLE.
文摘针对以相关谱最大值作为统计量对线性调频/二相编码(LFM/BPSK,Linear Frequency Modulation/Binary Phase Shift Keying)混合调制信号盲处理结果进行可信性检验时,存在概率密度函数复杂,难以得到似然比检验闭合表达式的问题,提出了一种基于极值分布理论(EVT,Extreme Value Theory)的简化处理算法.利用相关谱最大值的极限分布替代其精确分布,基于纽曼-皮尔逊(NP,Neyman-Pearson)准则得到简化的似然比检验,给出了相应判决式及其判决门限的解析表达式.文中给出了不同假设下相关谱最大值的极限分布形式.计算机仿真结果表明:本算法与已有的恒虚警方法相当,但优于基于分组极值模型及超阈值模型的两种分布拟合检验法,且具有较低的计算复杂度.
