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Time Series Ngai Hang Chan

Time Series By Ngai Hang Chan

Time Series by Ngai Hang Chan

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Summary

* The author is well known for his clear, concise, and authentic presentation. * Two new chapters now appear on Bayesian methods and arbitrage statistics. * Special emphasis is placed on applications from around the world, including, but not limited to, Asia; China; and Europe.

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Time Series Summary

Time Series: Applications to Finance with R and S-Plus by Ngai Hang Chan

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. An author website provides instructor notations and additional software subroutines, as well as complete solutions to the exercises in the text.

Time Series Reviews

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)

About Ngai Hang Chan

NGAI 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.

Table of 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 mu and GAMMA. 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.

Additional information

CIN0470583622VG
9780470583623
0470583622
Time Series: Applications to Finance with R and S-Plus by Ngai Hang Chan
Ngai Hang Chan
Used - Very Good
Hardback
John Wiley & Sons Inc
20101022
336
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in very good condition, but if you are not entirely satisfied please get in touch with us

Customer Reviews - Time Series