{"title":"基于深度学习模型的包含日内正负跳变的波动率预测","authors":"Yilun Zhang, Yuping Song, Ying Peng, Hanchao Wang","doi":"10.1002/for.3146","DOIUrl":null,"url":null,"abstract":"<p>Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high-frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5-min high-frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short-term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short-term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out-of-sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model\",\"authors\":\"Yilun Zhang, Yuping Song, Ying Peng, Hanchao Wang\",\"doi\":\"10.1002/for.3146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high-frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5-min high-frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short-term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short-term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out-of-sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model.</p>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3146\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.3146","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model
Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high-frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5-min high-frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short-term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short-term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out-of-sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model.
期刊介绍:
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.