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基本説明
Two new chapters now appear on Bayesian methods and arbitrage statistics.
Full Description
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, and a related Web site features additional data sets.
Time 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.
Contents
List of Figures. List of Tables.
Preface.
Preface to the First Edition.
1 Introduction.
1.1 Basic Description.
1.2 Simple Descriptive Techniques.
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 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 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.
9.5 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.
10.4 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.
12.4 Example of Foreign Exchange Rates.
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.
13.7 Example of Spot Index and Futures.
13.8 Conclusions.
13.9 Exercises.
14 Markov Chain Monte Carlo Methods.
14.1 Introduction.
14.2 Bayesian Inference.
14.3 Markov Chain Monte Carlo.
14.4 Exercises.
15 Statistical Arbitrage.
15.1 Introduction.
15.2 Pairs Trading.
15.3 Cointegration.
15.4 Simple Pairs Trading.
15.5 Cointegrations and Pairs Trading.
15.6 Hang Seng Index Components Example.
15.7 Exercises.
16 Answers to Selected Exercises.
16.1 Chapter 1.
16.2 Chapter 2.
16.3 Chapter 3.
16.4 Chapter 4.
16.5 Chapter 5.
16.6 Chapter 6.
16.7 Chapter 7.
16.8 Chapter 8.
16.9 Chapter 9.
16.10 Chapter 10.
16.11 Chapter 11.
16.12 Chapter 12.
16.13 Chapter 13.
16.14 Chapter 14.
16.15 Chapter 15.
References.
Subject Index.
Author Index.
