LEADER 03430nam 22006255 450 001 9910484538103321 005 20251113180259.0 010 $a3-030-46347-8 024 7 $a10.1007/978-3-030-46347-2 035 $a(CKB)4100000011413952 035 $a(MiAaPQ)EBC6331636 035 $a(DE-He213)978-3-030-46347-2 035 $a(PPN)250216663 035 $a(MiAaPQ)EBC6326509 035 $a(MiAaPQ)EBC29093019 035 $a(EXLCZ)994100000011413952 100 $a20200831d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime Series in Economics and Finance /$fby Tomas Cipra 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (409 pages) $cillustrations 311 08$a3-030-46346-X 320 $aIncludes bibliographical references and index. 327 $a1. 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. 330 $aThis 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. 606 $aStatistics 606 $aEconometrics 606 $aSocial sciences$xMathematics 606 $aFinancial engineering 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aEconometrics 606 $aMathematics in Business, Economics and Finance 606 $aFinancial Engineering 615 0$aStatistics. 615 0$aEconometrics. 615 0$aSocial sciences$xMathematics. 615 0$aFinancial engineering. 615 14$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aEconometrics. 615 24$aMathematics in Business, Economics and Finance. 615 24$aFinancial Engineering. 676 $a330.015195 700 $aCipra$b Tomas$4aut$4http://id.loc.gov/vocabulary/relators/aut$01015116 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484538103321 996 $aTime Series in Economics and Finance$92368793 997 $aUNINA