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Time series in economics and finance

Cipra, Tomas.

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Time series in economics and finance

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Time series in economics and finance/ by Tomas Cipra.
作者:	Cipra, Tomas.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	ix, 410 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Time-series analysis. -
電子資源:	https://doi.org/10.1007/978-3-030-46347-2
ISBN:	9783030463472

Time series in economics and finance
Cipra, Tomas.

Time series in economics and finance[electronic resource] /by Tomas Cipra. - Cham :Springer International Publishing :2020. - ix, 410 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030463472

Standard No.: 10.1007/978-3-030-46347-2doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: HG176.5 / .C577 2020

Dewey Class. No.: 330.015195

Time series in economics and finance
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505 0 $a 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.
520 $a 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.
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