{"product_id":"time-series-isbn-9780470583623","title":"Time Series","description":"\u003cp\u003eA new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus® and R software\u003c\/p\u003e \u003cp\u003eTime 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.\u003c\/p\u003e \u003cp\u003eWith 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNonstationarity\u003c\/li\u003e \u003cli\u003eHeteroscedasticity\u003c\/li\u003e \u003cli\u003eMultivariate time series\u003c\/li\u003e \u003cli\u003eState space modeling and stochastic volatility\u003c\/li\u003e \u003cli\u003eMultivariate GARCH\u003c\/li\u003e \u003cli\u003eCointegration and common trends\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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, and a related Web site features additional data sets.\u003c\/p\u003e \u003cp\u003eTime Series: Applications to Finance with R and S-Plus® is an excellent book for courses on financial time series at the upper-undergraduate and beginning graduate levels. It also serves as an indispensible resource for practitioners working with financial data in the fields of statistics, economics, business, and risk management.\u003c\/p\u003e  List of Figures.  \u003cp\u003eList of Tables.\u003c\/p\u003e \u003cp\u003ePreface.\u003c\/p\u003e \u003cp\u003ePreface to the First Edition.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Basic Description.\u003c\/p\u003e \u003cp\u003e1.2 Simple Descriptive Techniques.\u003c\/p\u003e \u003cp\u003e1.3 Transformations.\u003c\/p\u003e \u003cp\u003e1.4 Example.\u003c\/p\u003e \u003cp\u003e1.5 Conclusions.\u003c\/p\u003e \u003cp\u003e1.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Probability Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Stochastic Processes.\u003c\/p\u003e \u003cp\u003e2.3 Examples.\u003c\/p\u003e \u003cp\u003e2.4 Sample Correlation Function.\u003c\/p\u003e \u003cp\u003e2.5 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Autoregressive Moving Average Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Moving Average Models.\u003c\/p\u003e \u003cp\u003e3.3 Autoregressive Models.\u003c\/p\u003e \u003cp\u003e3.4 ARMA Models.\u003c\/p\u003e \u003cp\u003e3.5 ARIMA Models.\u003c\/p\u003e \u003cp\u003e3.6 Seasonal ARIMA.\u003c\/p\u003e \u003cp\u003e3.7 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Estimation in the Time Domain.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Moment Estimators.\u003c\/p\u003e \u003cp\u003e4.3 Autoregressive Models.\u003c\/p\u003e \u003cp\u003e4.4 Moving Average Models.\u003c\/p\u003e \u003cp\u003e4.5 ARMA Models.\u003c\/p\u003e \u003cp\u003e4.6 Maximum Likelihood Estimates.\u003c\/p\u003e \u003cp\u003e4.7 Partial ACF.\u003c\/p\u003e \u003cp\u003e4.8 Order Selections.\u003c\/p\u003e \u003cp\u003e4.9 Residual Analysis.\u003c\/p\u003e \u003cp\u003e4.10 Model Building.\u003c\/p\u003e \u003cp\u003e4.11 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Examples in \u003ci\u003eS\u003csmall\u003ePLUS\u003c\/small\u003e\u003c\/i\u003e and \u003ci\u003eR\u003c\/i\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Example 1.\u003c\/p\u003e \u003cp\u003e5.3 Example 2.\u003c\/p\u003e \u003cp\u003e5.4 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Forecasting.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Simple Forecasts.\u003c\/p\u003e \u003cp\u003e6.3 Box and Jenkins Approach.\u003c\/p\u003e \u003cp\u003e6.4 Treasury Bill Example.\u003c\/p\u003e \u003cp\u003e6.5 Recursions.\u003c\/p\u003e \u003cp\u003e6.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Spectral Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Spectral Representation Theorems.\u003c\/p\u003e \u003cp\u003e7.3 Periodogram.\u003c\/p\u003e \u003cp\u003e7.4 Smoothing of Periodogram.\u003c\/p\u003e \u003cp\u003e7.5 Conclusions.\u003c\/p\u003e \u003cp\u003e7.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Nonstationarity.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Nonstationarity in Variance.\u003c\/p\u003e \u003cp\u003e8.3 Nonstationarity in Mean: Random Walk with Drift.\u003c\/p\u003e \u003cp\u003e8.4 Unit Root Test.\u003c\/p\u003e \u003cp\u003e8.5 Simulations.\u003c\/p\u003e \u003cp\u003e8.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Heteroskedasticity.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 ARCH.\u003c\/p\u003e \u003cp\u003e9.3 GARCH.\u003c\/p\u003e \u003cp\u003e9.4 Estimation and Testing for ARCH.\u003c\/p\u003e \u003cp\u003e9.5 Example of Foreign Exchange Rates.\u003c\/p\u003e \u003cp\u003e9.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multivariate Time Series.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Estimation of μ and Γ.\u003c\/p\u003e \u003cp\u003e10.3 Multivariate ARMA Processes.\u003c\/p\u003e \u003cp\u003e10.4 Vector AR Models.\u003c\/p\u003e \u003cp\u003e10.5 Example of Inferences for VAR.\u003c\/p\u003e \u003cp\u003e10.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 State Space Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 State Space Representation.\u003c\/p\u003e \u003cp\u003e11.3 Kalman Recursions.\u003c\/p\u003e \u003cp\u003e11.4 Stochastic Volatility Models.\u003c\/p\u003e \u003cp\u003e11.5 Example of Kalman Filtering of Term Structure.\u003c\/p\u003e \u003cp\u003e11.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Multivariate GARCH.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 General Model.\u003c\/p\u003e \u003cp\u003e12.3 Quadratic Form.\u003c\/p\u003e \u003cp\u003e12.4 Example of Foreign Exchange Rates.\u003c\/p\u003e \u003cp\u003e12.5 Conclusions.\u003c\/p\u003e \u003cp\u003e12.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Cointegrations and Common Trends.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Definitions and Examples.\u003c\/p\u003e \u003cp\u003e13.3 Error Correction Form.\u003c\/p\u003e \u003cp\u003e13.4 Granger’s Representation Theorem.\u003c\/p\u003e \u003cp\u003e13.5 Structure of Cointegrated Systems.\u003c\/p\u003e \u003cp\u003e13.6 Statistical Inference for Cointegrated Systems.\u003c\/p\u003e \u003cp\u003e13.7 Example of Spot Index and Futures.\u003c\/p\u003e \u003cp\u003e13.8 Conclusions.\u003c\/p\u003e \u003cp\u003e13.9 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Markov Chain Monte Carlo Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e14.2 Bayesian Inference.\u003c\/p\u003e \u003cp\u003e14.3 Markov Chain Monte Carlo.\u003c\/p\u003e \u003cp\u003e14.4 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Statistical Arbitrage.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction.\u003c\/p\u003e \u003cp\u003e15.2 Pairs Trading.\u003c\/p\u003e \u003cp\u003e15.3 Cointegration.\u003c\/p\u003e \u003cp\u003e15.4 Simple Pairs Trading.\u003c\/p\u003e \u003cp\u003e15.5 Cointegrations and Pairs Trading.\u003c\/p\u003e \u003cp\u003e15.6 Hang Seng Index Components Example.\u003c\/p\u003e \u003cp\u003e15.7 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Answers to Selected Exercises.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Chapter 1.\u003c\/p\u003e \u003cp\u003e16.2 Chapter 2.\u003c\/p\u003e \u003cp\u003e16.3 Chapter 3.\u003c\/p\u003e \u003cp\u003e16.4 Chapter 4.\u003c\/p\u003e \u003cp\u003e16.5 Chapter 5.\u003c\/p\u003e \u003cp\u003e16.6 Chapter 6.\u003c\/p\u003e \u003cp\u003e16.7 Chapter 7.\u003c\/p\u003e \u003cp\u003e16.8 Chapter 8.\u003c\/p\u003e \u003cp\u003e16.9 Chapter 9.\u003c\/p\u003e \u003cp\u003e16.10 Chapter 10.\u003c\/p\u003e \u003cp\u003e16.11 Chapter 11.\u003c\/p\u003e \u003cp\u003e16.12 Chapter 12.\u003c\/p\u003e \u003cp\u003e16.13 Chapter 13.\u003c\/p\u003e \u003cp\u003e16.14 Chapter 14.\u003c\/p\u003e \u003cp\u003e16.15 Chapter 15.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eSubject Index.\u003c\/p\u003e \u003cp\u003eAuthor Index.\u003c\/p\u003e  \"Both are on topics of intense interest among academicians and financial practitioners. Their inclusoin makes the book more up-to-date and hopefully entertains a broader spectrum of readers. Upon many requests from users of the first edition, a new chapter on solutions to selected exercises has also been prepared so as to make the book more accessible to instructors and students alike.\" (Mathematical Reviews, 2011)  \u003cp\u003e \u003c\/p\u003e \u003cp\u003eNGAI HANG CHAN, PhD, is Head and Chair Professor of Statistics at the Chinese University of Hong Kong. He has published extensively in the areas of time series, statistical finance, econometrics, risk management, and stochastic processes. A Fellow of the Institute of Mathematical Statistics and the American Statistical Association, Dr. Chan is the coauthor of Simulation Techniques in Financial Risk Management, also published by Wiley.\u003c\/p\u003e   \u003cp\u003eA new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus® and R software\u003c\/p\u003e \u003cp\u003eTime 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.\u003c\/p\u003e \u003cp\u003eWith 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNonstationarity\u003c\/li\u003e \u003cli\u003eHeteroscedasticity\u003c\/li\u003e \u003cli\u003eMultivariate time series\u003c\/li\u003e \u003cli\u003eState space modeling and stochastic volatility\u003c\/li\u003e \u003cli\u003eMultivariate GARCH\u003c\/li\u003e \u003cli\u003eCointegration and common trends\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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, and a related Web site features additional data sets.\u003c\/p\u003e \u003cp\u003eTime Series: Applications to Finance with R and S-Plus® is an excellent book for courses on financial time series at the upper-undergraduate and beginning graduate levels. It also serves as an indispensible resource for practitioners working with financial data in the fields of statistics, economics, business, and risk management.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990392193253,"sku":"NP9780470583623","price":154.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470583623.jpg?v=1761787642","url":"https:\/\/k12savings.com\/products\/time-series-isbn-9780470583623","provider":"K12savings","version":"1.0","type":"link"}