Time series : applications to finance with R and S-Plus

Ngai Hang Chan

A new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus® and R software Time Series: Applications to Finance with R and S-Plus®, Second Edition is designed to present an in-depth introduction to the conceptual underpinnings and modern ideas of time series analysis. Utilizing interesting, real-world applications and the latest software packages, this book successfully helps readers grasp the technical and conceptual manner of the topic in order to gain a deeper understanding of the ever-changing dynamics of the financial world. With balanced coverage of both theory and applications, this Second Edition includes new content to accurately reflect the current state-of-the-art nature of financial time series analysis. A new chapter on Markov Chain Monte Carlo presents Bayesian methods for time series with coverage of Metropolis-Hastings algorithm, Gibbs sampling, and a case study that explores the relevance of these techniques for understanding activity in the Dow Jones Industrial Average. The author also supplies a new presentation of statistical arbitrage that includes discussion of pairs trading and cointegration. In addition to standard topics such as forecasting and spectral analysis, real-world financial examples are used to illustrate recent developments in nonstandard techniques, including: Nonstationarity Heteroscedasticity Multivariate time series State space modeling and stochastic volatility Multivariate GARCH Cointegration and common trends The book's succinct and focused organization allows readers to grasp the important ideas of time series. All examples are systematically illustrated with S-Plus® and R software, highlighting the relevance of time series in financial applications. End-of-chapter exercises and selected solutions allow readers to test their comprehension of the presented material,

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Year of publication:	c2010 ; 2nd ed (Online-Ausg.)
Authors:	Chan, Ngai Hang
Publisher:	Hoboken, N.J : Wiley
Subject:	Zeitreihenanalyse \| Time series analysis \| Portfolio-Management \| Portfolio selection \| Theorie \| Theory
Description of contents:	Table of Contents [swbplus.bsz-bw.de] ; Table of Contents [external.dandelon.com] ; Description [zbmath.org]

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Extent:	Online-Ressource (1 online resource (xxiii, 296 p.)) ill.
Series:	Wiley Series in Probability and Statistics
Type of publication:	Book / Working Paper
Language:	English
Notes:	Includes bibliographical references and indexes. - Description based on print version record 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 Models 3.3 Autoregressive Models3.3.1 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 5.4 Exercises6 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 9.5 Example of Foreign Exchange Rates9.6 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 Form 12.3.1 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
ISBN:	978-1-280-75948-2 ; 978-0-470-58362-3 ; 978-0-470-58362-3 ; 0-470-58362-2 ; 978-1-118-03071-4
Source:	ECONIS - Online Catalogue of the ZBW

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