[1]柳向东,靳晓洁.市道轮换下的高频数据参数估计[J].深圳大学学报理工版,2018,35(4):432-440.[doi:10.3724/SP.J.1249.2018.04432]
　LIU Xiangdong and JIN Xiaojie.Parameter estimation via regime switching model for high frequency data[J].Journal of Shenzhen University Science and Engineering,2018,35(4):432-440.[doi:10.3724/SP.J.1249.2018.04432]
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市道轮换下的高频数据参数估计()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第35卷
期数:
2018年第4期
页码:
432-440
栏目:
数学与应用数学
出版日期:
2018-07-10

文章信息/Info

Title:
Parameter estimation via regime switching model for high frequency data
作者:
柳向东; 靳晓洁
暨南大学经济学院，广东广州 510632
Author(s):
LIU Xiangdong and JIN Xiaojie
College of Economics, Jinan University, Guangzhou 510632, Guangdong Province, P.R.China
关键词:
应用统计数学; 高频数据; 市道轮换模型; 滤波算法; Kim平滑算法; 参数估计
Keywords:
application of statistical mathematics;  high frequency data;  regime switching;  filtering algorithm;  Kim smoothing algorithm;  parameter estimation
分类号:
O 211.9；F 830
DOI:
10.3724/SP.J.1249.2018.04432
文献标志码:
A
摘要:
在对高频金融数据的研究分析中，引入市道轮换模型，结合自回归模型和波动率替代模型，对高频数据进行建模分析，并运用滤波算法以及Kim平滑算法等进行参数估计和预测. 利用2017-01-03—2017-08-02上证综指每5 min的收盘价格，用Matlab实现马氏市道轮换自回归模型的预测和推测，并构建波动率替代模型. 结果表明，马氏市道轮换高频数据模型是一种具有模型创新并且理论性强的分析方式.
Abstract:
To analyze high-frequency financial data, we use the regime switching method combined with the autoregressive model and volatility substitution model. We make the parameter estimation and predict the high-frequency data by filtering algorithm and Kim smoothing algorithm. Using the 2017-01-03 to 2017-08-02 Shanghai Composite Index every five minutes closing price, we achieve regime-switching autoregressive model of prediction and speculation, and construct of the volatility replacement model. The results show that the Markov high frequency data model is a model with innovative and theoretical analysis.

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更新日期/Last Update: 2018-06-20