LEADER 06135oam 2200709 a 450 001 9910139201603321 005 20231220231048.0 010 $a1-280-75948-8 010 $a9786613678010 010 $a1-118-30294-X 010 $a1-118-03246-2 010 $a1-118-03071-0 035 $a(CKB)2560000000050713 035 $a(EBL)699152 035 $a(OCoLC)705354491 035 $a(SSID)ssj0000482407 035 $a(PQKBManifestationID)12231697 035 $a(PQKBTitleCode)TC0000482407 035 $a(PQKBWorkID)10526485 035 $a(PQKB)11357369 035 $a(MiAaPQ)EBC699152 035 $a(Au-PeEL)EBL699152 035 $a(CaPaEBR)ebr10444387 035 $a(CaONFJC)MIL367801 035 $a(EXLCZ)992560000000050713 100 $a20100423d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime series $eapplications to finance with R and S-Plus /$fNgai Hang Chan 205 $a2nd ed. 210 1$aHoboken, N.J. :$cWiley,$d2010. 210 4$d©2010 215 $a1 online resource (xxiii, 296 pages) $cillustrations 311 0 $a0-470-58362-2 320 $aIncludes bibliographical references and indexes. 327 $aTime 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 Models 327 $a3.3 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 2 327 $a5.4 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 ARCH 327 $a9.5 Example of Foreign Exchange Rates; 9.6 Exercises; 10 Multivariate Time Series; 10.1 Introduction; 10.2 Estimation of ? and ?; 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 Form 327 $a12.3.1 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 330 $a"This book is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds. The book offers succinct coverage of standard topics in statistical time series - such as forecasting and spectral analysis - in a manner that is both technical and conceptual. Recent developments in nonstandard time series techniques such as Bayesian methods and arbitrage statistics have been added to this edition, and they are illustrated in detail with real financial examples. Subroutines in R and S-Plus are lavishly displayed throughout in this new edition. An author website provides instructor notations and additional software subroutines, as well as complete solutions to the exercises in the text."--$cProvided by publisher. 330 $a"This book is designed to help readers grasp the conceptual underpinnings of time series modeling in order to gain a deeper understanding of the ever-changing dynamics of the financial world. It covers theory and application equally for readers from both financial and mathematical backgrounds"--$cProvided by publisher. 606 $aTime-series analysis 606 $aEconometrics 606 $aRisk management 606 $aR (Computer program language) 615 0$aTime-series analysis. 615 0$aEconometrics. 615 0$aRisk management. 615 0$aR (Computer program language). 676 $a332.01/51955 700 $aChan$b Ngai Hang$0282488 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139201603321 996 $aTime series$9672610 997 $aUNINA