UID:
almahu_9948691270102882
Umfang:
IX, 410 p. 94 illus., 15 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2020.
ISBN:
9783030463472
Inhalt:
This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers decomposition methods, autocorrelation methods for univariate time series, volatility and duration modeling for financial time series, and multivariate time series methods, such as cointegration and recursive state space modeling. It also includes numerous practical examples to demonstrate the theory using real-world data, as well as exercises at the end of each chapter to aid understanding. This book serves as a reference text for researchers, students and practitioners interested in time series, and can also be used for university courses on econometrics or computational finance.
Anmerkung:
1. Introduction -- I. Subject of Time Series -- 2. Random Processes -- II. Decomposition of Economic Time Series -- 3. Trend -- 4. Seasonality and Periodicity -- 5. Residual Component -- III. Autocorrelation Methods for Univariate Time Series -- 6. Box-Jenkins Methodology -- 7. Autocorrelation Methods in Regression Models -- IV. Financial Time Series -- 8. Volatility of Financial Time Series -- 9. Other Methods for Financial Time Series -- 10. Models of Development of Financial Assets -- 11. Value at Risk -- V. Multivariate Time Series -- 12. Methods for Multivariate Time Series -- 13. Multivariate Volatility Modeling -- 14. State Space Models of Time Series -- References -- Index.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783030463465
Weitere Ausg.:
Printed edition: ISBN 9783030463489
Weitere Ausg.:
Printed edition: ISBN 9783030463496
Sprache:
Englisch
Fachgebiete:
Mathematik
DOI:
10.1007/978-3-030-46347-2
URL:
https://doi.org/10.1007/978-3-030-46347-2
