摘要
本文通过引入一类自适应权重函数,提出了一种新的数据融合方法,来提炼高频数据中的信息,并将所得信息与低频数据结合在一起,构建了一种能充分利用混频交易信息的波动模型:混频GARCH模型。针对模型参数估计问题,本文给出了参数的估计方法,分析了估计量的理论性质,得到了相应的中心极限定理并用模拟数据检验了估计量的数据表现。文中模型有如下优势:首先,与传统融合数据方法依据数据的先后顺序分配权重不同,新权函数的自变量能描述高频交易的特征,这使得基于新权函数的数据融合方法将按照交易特征来分配权重,该分配方式能依据交易特征的变化自动调整不同交易日内权重的分配,从而让每个高频交易所分配的权重与其产生的冲击效果相一致,因此新模型利用高频数据的方式更恰当;其次,新建的模型能利用同一交易过程中多种高频数据,其数据利用程度更加充分。这些优势使得混频GARCH模型具有更好的预测表现,实证结果也证明了这一点。将多种模型同时用于预测实际数据的波动率,结果表明,混频GARCH模型能更加准确又稳健地预测出波动率。新模型的提出,扩充了利用混频数据分析波动率及其相关问题的方法。
In this paper,a new way for extracting useful information from high-frequency data is constructed through introducing an adaptive weight function,and a new volatility GARCH model with the ability to use mixed frequency data is proposed by combining the low-frequency data with the information extracted from high-frequency data.The parameter estimation method for the suggested model is proposed and the asymptotic property of the suggested estimator is discussed in detail to obtain the corresponding central limiting theory.Simulation studies are employed to test the performance of the constructed estimator.The proposed model’s advantages are as follows:First,the constructed model is different from the traditional ones to integrate data in which the distribution of weight depends on the order of data.Due to the fact that the independent variable of the suggested weight function can describe the character of a high-frequency transaction,the constructed model based on the new weight function will assign weight according to the character of the transaction.This assignment of weight can automatically regulate its formation according to the variation of transaction character in different trading days and the assigned weight for each high-frequency transaction is consistent with its impact on future volatility.Hence,the proposed model can exploit high-frequency data more properly.Second,more than one high-frequency data series produced in the same transaction is employed in the proposed model,which means that our model makes full use of existing data resources.These advantages of the mixed frequency GARCH model lead to better performance in predicting volatility,which has been confirmed by empirical results in the paper.Various models are used simultaneously to predict the volatility of real transaction data.The results show that the proposed model can predict volatility more accurately and robustly.The new model will enrich the technique of using mixed frequency data to analyze volatility and the related issues.
作者
王江涛
黄立玮
崔翔宇
Wang Jiangtao;Huang Liwei;Cui Xiangyu
出处
《统计研究》
CSSCI
北大核心
2021年第5期97-108,共12页
Statistical Research
基金
国家社会科学基金项目“混频因素协同变动下系统性金融风险的度量、预警及防范研究”(19BTJ015)
关键词
混频数据
自适应权重
波动率
渐近性质
Mixed Frequency Data
Adaptive Weight
Volatility
Asymptotic Property
