{"title":"基于扩展卡尔曼滤波器的股票市场价格分析——以美国和中国为例","authors":"Ö. Alp, Levent Özbek, Bilge Canbaloglu","doi":"10.1080/10293523.2023.2179160","DOIUrl":null,"url":null,"abstract":"ABSTRACT This study decomposes the trend-cycle components of the stock market indices of the United States and China in a time series framework over the period of 1980–2021, and 1992–2021 years, respectively. Using the extended Kalman filter (EKF) method, the changing dynamics of stock market prices can be analysed more effectively since stock market prices can have a nonlinear pattern, and the EKF allows estimated system parameters to change over time under the nonlinear state-space model. As the impacts of shocks to trend and cycle on the stock market can be observed more efficiently due to flexible time-varying parameter estimation, the EKF offers more reasonable results than other decomposition tools. The empirical findings of this study prove that the EKF extracts the trend and cycle components by giving quite consistent forecasts for stock market prices in both advanced and emerging market countries.","PeriodicalId":44496,"journal":{"name":"Investment Analysts Journal","volume":"52 1","pages":"67 - 82"},"PeriodicalIF":1.2000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An analysis of stock market prices by using extended Kalman filter: The US and China cases\",\"authors\":\"Ö. Alp, Levent Özbek, Bilge Canbaloglu\",\"doi\":\"10.1080/10293523.2023.2179160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT This study decomposes the trend-cycle components of the stock market indices of the United States and China in a time series framework over the period of 1980–2021, and 1992–2021 years, respectively. Using the extended Kalman filter (EKF) method, the changing dynamics of stock market prices can be analysed more effectively since stock market prices can have a nonlinear pattern, and the EKF allows estimated system parameters to change over time under the nonlinear state-space model. As the impacts of shocks to trend and cycle on the stock market can be observed more efficiently due to flexible time-varying parameter estimation, the EKF offers more reasonable results than other decomposition tools. The empirical findings of this study prove that the EKF extracts the trend and cycle components by giving quite consistent forecasts for stock market prices in both advanced and emerging market countries.\",\"PeriodicalId\":44496,\"journal\":{\"name\":\"Investment Analysts Journal\",\"volume\":\"52 1\",\"pages\":\"67 - 82\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Investment Analysts Journal\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1080/10293523.2023.2179160\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Investment Analysts Journal","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1080/10293523.2023.2179160","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
An analysis of stock market prices by using extended Kalman filter: The US and China cases
ABSTRACT This study decomposes the trend-cycle components of the stock market indices of the United States and China in a time series framework over the period of 1980–2021, and 1992–2021 years, respectively. Using the extended Kalman filter (EKF) method, the changing dynamics of stock market prices can be analysed more effectively since stock market prices can have a nonlinear pattern, and the EKF allows estimated system parameters to change over time under the nonlinear state-space model. As the impacts of shocks to trend and cycle on the stock market can be observed more efficiently due to flexible time-varying parameter estimation, the EKF offers more reasonable results than other decomposition tools. The empirical findings of this study prove that the EKF extracts the trend and cycle components by giving quite consistent forecasts for stock market prices in both advanced and emerging market countries.
期刊介绍:
The Investment Analysts Journal is an international, peer-reviewed journal, publishing high-quality, original research three times a year. The journal publishes significant new research in finance and investments and seeks to establish a balance between theoretical and empirical studies. Papers written in any areas of finance, investment, accounting and economics will be considered for publication. All contributions are welcome but are subject to an objective selection procedure to ensure that published articles answer the criteria of scientific objectivity, importance and replicability. Readability and good writing style are important. No articles which have been published or are under review elsewhere will be considered. All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. All peer review is double blind and submission is via email. Accepted papers will then pass through originality checking software. The editors reserve the right to make the final decision with respect to publication.