Time Series Applications to Finance with R and S Plus 2nd Edition by Ngai Hang Chan ISBN 978-0470583623 0470583623
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ISBN 10: 0470583623
ISBN 13: 978-0470583623
Author: 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, 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.
Table of contents:
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 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 Маzіmum 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 u and I
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
12.2.1 Diagonal Form
12.2.2 Alternative Matriz Form
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
13.6.1 Canonical Correlations
13.6.2 Inference and Testing
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
14.3.1 Metropolis-Hastings Algorithm
14.3.2 Gibbs Sampling
14.3.3 Case Study: The Impact of Jumps on
Dow Jones
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
15.6.1 Formation of Cointegration Pairs
15.6.2 Trading with Cointegration Pairs
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
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Tags: Ngai Hang Chan, Time Series Applications, Finance with R