{"title":"基于ARIMA-GARCH模型的欧洲股市价格预测分析","authors":"Alžběta Zíková, Jitka Veselá","doi":"10.54694/stat.2023.4","DOIUrl":null,"url":null,"abstract":"The achievement of profits when trading on the stock markets is conditioned by a quality analytical forecast of the development of stock prices in the coming period. This research attempts to compare the results of the ARIMA model and the ARIMA-GARCH model to forecast the development of stock prices on a sample of selected stocks from the Czech, German, Austrian, Polish and British markets. The 4 most liquid titles from each of the above-mentioned markets were selected for the sample of analyzed stocks. Available daily closing stock price data, mostly from the period 2000–2022, were used for the analysis. Research has shown that for most of the analyzed titles, it is more appropriate to use the ARIMA-GARCH model, which better captures variability for this data than just the ARIMA model. The quality of the selected model is evaluated by autocorrelation, heteroskedasticity tests, and Theil´s inequality coefficient.","PeriodicalId":43106,"journal":{"name":"Statistika-Statistics and Economy Journal","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting Analysis of Stock Prices on European Markets Using the ARIMA-GARCH Model\",\"authors\":\"Alžběta Zíková, Jitka Veselá\",\"doi\":\"10.54694/stat.2023.4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The achievement of profits when trading on the stock markets is conditioned by a quality analytical forecast of the development of stock prices in the coming period. This research attempts to compare the results of the ARIMA model and the ARIMA-GARCH model to forecast the development of stock prices on a sample of selected stocks from the Czech, German, Austrian, Polish and British markets. The 4 most liquid titles from each of the above-mentioned markets were selected for the sample of analyzed stocks. Available daily closing stock price data, mostly from the period 2000–2022, were used for the analysis. Research has shown that for most of the analyzed titles, it is more appropriate to use the ARIMA-GARCH model, which better captures variability for this data than just the ARIMA model. The quality of the selected model is evaluated by autocorrelation, heteroskedasticity tests, and Theil´s inequality coefficient.\",\"PeriodicalId\":43106,\"journal\":{\"name\":\"Statistika-Statistics and Economy Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2023-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistika-Statistics and Economy Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54694/stat.2023.4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistika-Statistics and Economy Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54694/stat.2023.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
Forecasting Analysis of Stock Prices on European Markets Using the ARIMA-GARCH Model
The achievement of profits when trading on the stock markets is conditioned by a quality analytical forecast of the development of stock prices in the coming period. This research attempts to compare the results of the ARIMA model and the ARIMA-GARCH model to forecast the development of stock prices on a sample of selected stocks from the Czech, German, Austrian, Polish and British markets. The 4 most liquid titles from each of the above-mentioned markets were selected for the sample of analyzed stocks. Available daily closing stock price data, mostly from the period 2000–2022, were used for the analysis. Research has shown that for most of the analyzed titles, it is more appropriate to use the ARIMA-GARCH model, which better captures variability for this data than just the ARIMA model. The quality of the selected model is evaluated by autocorrelation, heteroskedasticity tests, and Theil´s inequality coefficient.
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