CAJ | 학술논문

为了考察多个市场或多个金融资产之间的高阶矩风险度量问题，有效地捕获收益率时间序列高阶矩动态特征，在考虑当前预期和波动性条件下，推导了高阶中心矩和协矩之间的关系，提出了能够有效解决维数灾祸问题的多维条件高阶矩模型．在多维SU分布基础上，采用动态条件相关性（DCC）和自回归条件密度技术，通过智能优化算法对条件高阶矩模型的时变参数进行估计．实证研究结果表明，多维条件高阶矩模型较好的拟合了收益率时间序列高阶矩动态特征，与之前的高阶矩模型相比，能够有效解决高阶矩模型的维数灾祸问题，表明该模型能够捕捉到我国多个市场之间高阶矩风险特征，提高多维条件高阶矩模型测度能力．
위료고찰다개시장혹다개금융자산지간적고계구풍험도량문제，유효지포획수익솔시간서렬고계구동태특정，재고필당전예기화파동성조건하，추도료고계중심구화협구지간적관계，제출료능구유효해결유수재화문제적다유조건고계구모형．재다유SU분포기출상，채용동태조건상관성（DCC）화자회귀조건밀도기술，통과지능우화산법대조건고계구모형적시변삼수진행고계．실증연구결과표명，다유조건고계구모형교호적의합료수익솔시간서렬고계구동태특정，여지전적고계구모형상비，능구유효해결고계구모형적유수재화문제，표명해모형능구포착도아국다개시장지간고계구풍험특정，제고다유조건고계구모형측도능력．
Considering factors of anticipation and volatility, to measure the dynamic character of higher moments risk and investigate impacts of the risk on multi-financial markets or assets, a model of multivariate conditional higher order moments, which can solve the problem of ＇dimension disaster＇, was proposed with the determination of the formulas between moments and co-moments. Time-varying parameters of higher order moments were estimated using Dynamic Conditional Correlation, Autoregressive conditional density and intelligence optimization algorithm on distribution. The analysis on models shows that model. of multivariate conditional higher order moments is better fitted with the feature of higher order moments of return series. Comparing our model with others, the model perform well in solving the problem of ＇dimension disaster＇, which has implication that our model can catch the risk character of Chinese multi-markets and improve estimation in multivariate conditional higher order moments.