摘要
异质自回归(heterogeneous autoregressive,HAR)及其拓展模型(统称为HAR-类模型)能够刻画不同类型(期限)交易者的异质性对金融市场未来价格波动的“贡献”程度,在实证研究中备受推崇,并在预测金融市场波动率中取得了较好的效果.研究发现,HAR-类模型虽然能够在一定程度上刻画金融市场中非常重要的长记忆特征,但刻画能力明显比自回归分整移动平均(ARFIMA)模型差.HAR-类模型的主要优势在于对异质性的刻画,而ARFIMA模型的主要优势在于对长记忆性的准确刻画.因此,基于这两个模型各自的优势提出了新的模型:异质自回归分整移动平均(HARFIMA)模型,并对新模型进行了拓展建模,提出HARFIMA-类模型.将HARFIMA-类模型运用于对标普500和上证综指的已实现波动率(RV)的建模和预测发现,HARFIMA-类模型能够更加准确地刻画金融市场的长记忆性,更重要的是样本外的预测能力明显优于其他模型,并且预测结果相当稳健.
HAR-type models can be used to describe the proportion of contributions by different class of traders(Heterogeneous).They are widely used in empirical study and their performance is good in financial market volatility forecasting.The empirical results show the HAR-type models can be used to partly capture the long memory which is very important for financial market,but the performance is very limited.However,ARFIMA models are very good at describing long memory.Therefore,based on the advantages of above mentioned two models,a new model:HARFIMA is proposed and developed further into an HARFIMA-type model.Then the HARFIMA-type model is used to forecast the realized volatility(RV)of S&P 500 and SSEC.The empirical results show that our HARFIMA-type model can be used to capture long memory more accurately,and that the out-sample forecasting performance is better than other models.This conclusion is also robust.
作者
陈王
魏宇
马锋
梅德祥
CHEN Wang;WEI Yu;MA Feng;MEI De-xiang(College of Finance and Economics,Yangtze Normal University,Chongqing 408100,China;School of Finance,Yunnan University of Finance and Economics,Kunming 650221,China;School of Economics&Management,Southwest Jiaotong University,Chengdu 610031,China;School of Finance,Chongqing Technology and Business University,Chongqing 400067,China)
出处
《管理科学学报》
CSSCI
CSCD
北大核心
2020年第11期103-116,共14页
Journal of Management Sciences in China
基金
国家自然科学基金资助项目(71901041,71671145,71971191,71701170)
教育部人文社会科学规划青年基金资助项目(17YJC790105)
云南省高校科技创新团队资助项目(201914)
云南省科技计划基础研究重点资助项目(202001AS070018).
