CAJ | 학술논문

为了解决厚尾分布不拥有完整的中心矩集合而无法进行矩估计的问题,在金融领域引入近年来在水文领域发展较为迅速的L-矩理论。在考虑当前预期和波动性条件下,基于L-矩理论分别考察了广义帕累托分布对高频收益超额数的静态尾部拟合和动态尾部拟合,应用条件VaR以及Kupiec-LR检验对拟合的结果进行了检验。研究结果表明,L-矩理论可以很好地解决厚尾分布的矩估计问题;VaR以及Kupiec-LR检验表明,基于L-矩的广义帕累托分布较好地拟合了极端条件下的收益率尾部,可以捕获极端条件下收益率时间序列动态特征。
위료해결후미분포불옹유완정적중심구집합이무법진행구고계적문제,재금융영역인입근년래재수문영역발전교위신속적L-구이론。재고필당전예기화파동성조건하,기우L-구이론분별고찰료엄의파루탁분포대고빈수익초액수적정태미부의합화동태미부의합,응용조건VaR이급Kupiec-LR검험대의합적결과진행료검험。연구결과표명,L-구이론가이흔호지해결후미분포적구고계문제;VaR이급Kupiec-LR검험표명,기우L-구적엄의파루탁분포교호지의합료겁단조건하적수익솔미부,가이포획겁단조건하수익솔시간서렬동태특정。
To solve moment estimation problems in heavy-tailed distribution which do not possess set of finite central moments,we introduce the theory of L-moments which is developed in hydrology. Considering the factors of anticipation and volatility and fitting the tail with Generalized Pareto Distribution using high frequency excess return in the static and dynamic condition,we check the results by invoking Kupiec-LR and dynamic quantile test. The analysis of models and VaR shows that problems of moment estimation in heavy-tailed distribution can be solved with L-moments. Generalized Pareto Distribution is better fitted with the feature of return series in extreme condition,which has the implication that our model can catch the dynamic character of return series in extreme conditions.