06130oam 2200709 a 450 991081619100332120231220231048.01-280-75948-897866136780101-118-30294-X1-118-03246-21-118-03071-0(CKB)2560000000050713(EBL)699152(OCoLC)705354491(SSID)ssj0000482407(PQKBManifestationID)12231697(PQKBTitleCode)TC0000482407(PQKBWorkID)10526485(PQKB)11357369(MiAaPQ)EBC699152(Au-PeEL)EBL699152(CaPaEBR)ebr10444387(CaONFJC)MIL367801(EXLCZ)99256000000005071320100423d2010 uy 0engur|n|---|||||txtrdacontentcrdamediacrrdacarrierTime series applications to finance with R and S-Plus /Ngai Hang Chan2nd ed.Hoboken, N.J. :Wiley,2010.©20101 online resource (xxiii, 296 pages) illustrations0-470-58362-2 Includes bibliographical references and indexes.Time Series: Applications to Finance with R and S-Plus®, Second Edition; Contents; 12.2.1 Diagonal Form; 12.2.2 Alternative Matrix Form; List of Figures; List of Tables; Preface; Preface to the First Edition; 1 Introduction; 1.1 Basic Description; 1.2 Simple Descriptive Techniques; 1.2.1 Trends; 1.2.2 Seasonal Cycles; 1.3 Transformations; 1.4 Example; 1.5 Conclusions; 1.6 Exercises; 2 Probability Models; 2.1 Introduction; 2.2 Stochastic Processes; 2.3 Examples; 2.4 Sample Correlation Function; 2.5 Exercises; 3 Autoregressive Moving Average Models; 3.1 Introduction; 3.2 Moving Average Models3.3 Autoregressive Models; 3.3.1 Duality between Causality and Stationarity*; 3.3.2 Asymptotic Stationarity; 3.3.3 Causality Theorem; 3.3.4 Covariance Structure of AR Models; 3.4 ARMA Models; 3.5 ARIMA Models; 3.6 Seasonal ARIMA; 3.7 Exercises; 4 Estimation in the Time Domain; 4.1 Introduction; 4.2 Moment Estimators; 4.3 Autoregressive Models; 4.4 Moving Average Models; 4.5 ARMA Models; 4.6 Maximum Likelihood Estimates; 4.7 Partial ACF; 4.8 Order Selections*; 4.9 Residual Analysis; 4.10 Model Building; 4.11 Exercises; 5 Examples in SPLUS and R; 5.1 Introduction; 5.2 Example 1; 5.3 Example 25.4 Exercises; 6 Forecasting; 6.1 Introduction; 6.2 Simple Forecasts; 6.3 Box and Jenkins Approach; 6.4 Treasury Bill Example; 6.5 Recursions*; 6.6 Exercises; 7 Spectral Analysis; 7.1 Introduction; 7.2 Spectral Representation Theorems; 7.3 Periodogram; 7.4 Smoothing of Periodogram*; 7.5 Conclusions; 7.6 Exercises; 8 Nonstationarity; 8.1 Introduction; 8.2 Nonstationarity in Variance; 8.3 Nonstationarity in Mean: Random Walk with Drift; 8.4 Unit Root Test; 8.5 Simulations; 8.6 Exercises; 9 Heteroskedasticity; 9.1 Introduction; 9.2 ARCH; 9.3 GARCH; 9.4 Estimation and Testing for ARCH9.5 Example of Foreign Exchange Rates; 9.6 Exercises; 10 Multivariate Time Series; 10.1 Introduction; 10.2 Estimation of μ and Γ; 10.3 Multivariate ARMA Processes; 10.3.1 Causality and Invertibility; 10.3.2 Identifiability; 10.4 Vector AR Models; 10.5 Example of Inferences for VAR; 10.6 Exercises; 11 State Space Models; 11.1 Introduction; 11.2 State Space Representation; 11.3 Kalman Recursions; 11.4 Stochastic Volatility Models; 11.5 Example of Kalman Filtering of Term Structure; 11.6 Exercises; 12 Multivariate GARCH; 12.1 Introduction; 12.2 General Model; 12.3 Quadratic Form12.3.1 Single-Factor GARCH(1,1); 12.3.2 Constant-Correlation Model; 12.4 Example of Foreign Exchange Rates; 12.4.1 The Data; 12.4.2 Multivariate GARCH in SPLUS; 12.4.3 Prediction; 12.4.4 Predicting Portfolio Conditional Standard Deviations; 12.4.5 BEKK Model; 12.4.6 Vector-Diagonal Models; 12.4.7 ARMA in Conditional Mean; 12.5 Conclusions; 12.6 Exercises; 13 Cointegrations and Common Trends; 13.1 Introduction; 13.2 Definitions and Examples; 13.3 Error Correction Form; 13.4 Granger's Representation Theorem; 13.5 Structure of Cointegrated Systems; 13.6 Statistical Inference for Cointegrated Systems"This book is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds. The book offers succinct coverage of standard topics in statistical time series - such as forecasting and spectral analysis - in a manner that is both technical and conceptual. Recent developments in nonstandard time series techniques such as Bayesian methods and arbitrage statistics have been added to this edition, and they are illustrated in detail with real financial examples. Subroutines in R and S-Plus are lavishly displayed throughout in this new edition. An author website provides instructor notations and additional software subroutines, as well as complete solutions to the exercises in the text."--Provided by publisher."This book is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds"--Provided by publisher.Time-series analysisEconometricsRisk managementR (Computer program language)Time-series analysis.Econometrics.Risk management.R (Computer program language)332.01/51955Chan Ngai Hang282488MiAaPQMiAaPQMiAaPQBOOK9910816191003321Time series672610UNINA